Question: Learning to rank non-factual responses: selecting comments in online forums.

This paper delves into the comparison and integration of two cutting-edge methods for automatic feature extraction: Convolution Tree Kernels (CTKs) and Convolutional Neural Networks (CNNs), specifically in the context of ranking answer sentences for Question Answering (QA) systems. Central to QA tasks is the effective representation of relational data between question and answer components in learning algorithms. To this end, we introduce innovative CNN architectures that incorporate relational information, and we combine these with CTKs that also leverage relational data. Our findings reveal that (i) both methodologies deliver state-of-the-art performance in a QA setting, with CTKs achieving higher accuracy levels, and (ii) the amalgamation of these approaches yields exceptional results, surpassing previous benchmarks.

Community-based question and answer (Q&A) systems facilitate information exchange and enable the creation of reusable knowledge repositories. While these systems are growing in usage and are changing how people find and share information, current designs are inefficient, wasting the time and attention of their users. Furthermore, existing systems do not support signaling and screening of joking and non-serious questions. Coupling Q&A services with instant and text messaging for faster questions and answers may exacerbate these issues, causing Q&A services to incur high interruption costs on their users.
 In this paper we present the design and evaluation of a market-based real-time Q&A system. We compared its use to a similar Q&A system without a market. We found that while markets can reduce wasted resources by reducing the number of less important questions and low quality answers, it may also reduce the socially conducive questions and usages that are vital to sustaining a Q&A community.

Posting reviews online has become an increasingly popular way for people to express opinions and sentiments toward the products bought or services received. Analyzing the large volume of online reviews available would produce useful actionable knowledge that could be of economic values to vendors and other interested parties. In this paper, we conduct a case study in the movie domain, and tackle the problem of mining reviews for predicting product sales performance. Our analysis shows that both the sentiments expressed in the reviews and the quality of the reviews have a significant impact on the future sales performance of products in question. For the sentiment factor, we propose Sentiment PLSA (S-PLSA), in which a review is considered as a document generated by a number of hidden sentiment factors, in order to capture the complex nature of sentiments. Training an S-PLSA model enables us to obtain a succinct summary of the sentiment information embedded in the reviews. Based on S-PLSFA, we propose ARSA, an Autoregressive Sentiment-Aware model for sales prediction. We then seek to further improve the accuracy of prediction by considering the quality factor, with a focus on predicting the quality of a review in the absence of user-supplied indicators, and present ARSQA, an Autoregressive Sentiment and Quality Aware model, to utilize sentiments and quality for predicting product sales performance. Extensive experiments conducted on a large movie data set confirm the effectiveness of the proposed approach.

With huge amount of information generated every day on the web, fact checking is an important and challenging task which can help people identify the authenticity of most claims as well as providing evidences selected from knowledge source like Wikipedia. Here we decompose this problem into two parts: an entity linking task (retrieving relative Wikipedia pages) and recognizing textual entailment between the claim and selected pages. In this paper, we present an end-to-end multi-task learning with bi-direction attention (EMBA) model to classify the claim as “supports”, “refutes” or “not enough info” with respect to the pages retrieved and detect sentences as evidence at the same time. We conduct experiments on the FEVER (Fact Extraction and VERification) paper test dataset and shared task test dataset, a new public dataset for verification against textual sources. Experimental results show that our method achieves comparable performance compared with the baseline system.

During large scale events, a large volume of content is posted on Twitter, but not all of this content is trustworthy. The presence of spam, advertisements, rumors and fake images reduces the value of information collected from Twitter, especially during sudden-onset crisis events where information from other sources is scarce. In this research work, we describe various facets of assessing the credibility of usergenerated content on Twitter during large scale events, and develop a novel real-time system to assess the credibility of tweets. Firstly, we develop a semi-supervised ranking model using SVM-rank for assessing credibility, based on training data obtained from six high-impact crisis events of 2013. An extensive set of forty-five features is used to determine the credibility score for each of the tweets. Secondly, we develop and deploy a system–TweetCred–in the form of a browser extension, a web application and an API at the link: http://twitdigest.iiitd.edu.in/TweetCred/. To the best of our knowledge, this is the first research work to develop a practical system for credibility on Twitter and evaluate it with real users. TweetCred was installed and used by 717 Twitter users within a span of three weeks. During this period, a credibility score was computed for more than 1.1 million unique tweets. Thirdly, we evaluated the real-time performance of TweetCred , observing that 84% of the credibility scores were displayed within 6 seconds. We report on the positive feedback that we received from the system’s users and the insights we gained into improving the system for future iterations.

Abstract—Nowadays social media information, such as news, links, images, or VDOs, is shared extensively. However, the effectiveness of disseminating information through social media lacks in quality: less fact checking, more biases, and several rumors. Many researchers have investigated about credibility on Twitter, but there is no the research report about credibility information on Facebook. This paper proposes features for measuring credibility on Facebook information. We developed the system for credibility on Facebook. First, we have developed FB credibility evaluator for measuring credibility of each post by manual human’s labelling. We then collected the training data for creating a model using Support Vector Machine (SVM). Secondly, we developed a chrome extension of FB credibility for Facebook users to evaluate the credibility of each post. Based on the usage analysis of our FB credibility chrome extension, about 81% of users’ responses agree with suggested credibility automatically computed by the proposed system.

The Internet is the largest source of information in the world. Search engines help people navigate the huge space of available data in order to acquire new skills and knowledge. In this paper, we present an in-depth analysis of sessions in which people explicitly search for new knowledge on the Web based on the log files of a popular search engine. We investigate within-session and cross-session developments of expertise, focusing on how the language and search behavior of a user on a topic evolves over time. In this way, we identify those sessions and page visits that appear to significantly boost the learning process. Our experiments demonstrate a strong connection between clicks and several metrics related to expertise. Based on models of the user and their specific context, we present a method capable of automatically predicting, with good accuracy, which clicks will lead to enhanced learning. Our findings provide insight into how search engines might better help users learn as they search.

Social media for news consumption is becoming popular nowadays. The low cost, easy access and rapid information dissemination of social media bring benefits for people to seek out news timely. However, it also causes the widespread of fake news, i.e., low-quality news pieces that are intentionally fabricated. The fake news brings about several negative effects on individual consumers, news ecosystem, and even society trust. Previous fake news detection methods mainly focus on news contents for deception classification or claim fact-checking. Recent Social and Psychology studies show potential importance to utilize social media data: 1) Confirmation bias effect reveals that consumers prefer to believe information that confirms their existing stances; 2) Echo chamber effect suggests that people tend to follow likeminded users and form segregated communities on social media. Even though users social engagements towards news on social media provide abundant auxiliary information for better detecting fake news, but existing work exploiting social engagements is rather limited. In this paper, we explore the correlations of publisher bias, news stance, and relevant user engagements simultaneously, and propose a Tri-Relationship Fake News detection framework (TriFN). We also provide two comprehensive real-world fake news datasets to facilitate fake news research. Experiments on these datasets demonstrate the effectiveness of the proposed approach. Introduction People nowadays tend to seek out and consume news from social media rather than traditional news organizations. For example, 62% of U.S. adults get news on social media in 2016, while in 2012, only 49 percent reported seeing news on social media. However, social media for news consumption is a double-edged sword. The quality of news on social media is much lower than traditional news organizations. Large volumes of “fake news”, i.e., those news articles with intentionally false information, are produced online for a variety of purposes, such as financial and political gain (Klein and Wueller 2017; Allcott and Gentzkow 2017). Fake news can have detrimental effects on individuals and the society. First, people may be misled by fake Copyright c © 2018, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. http://www.journalism.org/2016/05/26/news-use-acrosssocial-media-platforms-2016/ news and accept false beliefs (Nyhan and Reifler 2010; Paul and Matthews 2016). Second, fake news could change the way people respond to true news. Third, the widespread of fake news could break the trustworthiness of entire news ecosystem. Thus, it is important to detect fake news on social media. Fake news is intentionally written to mislead consumers, which makes it nontrivial to detect simply based on news content. Thus, it is necessary to explore auxiliary information to improve detection. For example, several style-based approaches try to capture the deceptive manipulators originated from the particular writing style of fake news (Rubin and Lukoianova 2015; Potthast et al. 2017). In addition, previous approaches try to aggregate users’ responses from relevant social engagements to infer the veracity of original news (Castillo, Mendoza, and Poblete 2011; Gupta, Zhao, and Han 2012). The news ecosystem on social media involves three basic entities, i.e., news publisher, news and social media users. Figure 1 gives an illustration of such ecosystem. In Figure 1, p1, p2 and p3 are news publishers who publish news a1, . . . , a4 and u1, . . . , u6 are users who have engaged in posting these news. In addition, users with similar interests can also form social links. The tri-relationship among publisher, news, and social engagements contains additional information to help detect fake news. First, sociallogical studies on journalism have theorized the correlation between the partisan bias of publisher and news contents veracity (Gentzkow, Shapiro, and Stone 2014; Entman 2007), where partisan means the perceived bias of the publisher in the selection of how news is reported and covered. For example, in Figure 1, for p1 with extreme left partisan bias and p2 with extreme right partisan bias, to support their own partisan, they have high degree to report fake news, such as a1 and a3; while for a mainstream publisher p3 that has least partisan bias, she has lower degree to manipulate original news events, and is more likely to write true news a4. Thus, exploiting publisher partisan information can bring additional benefits to predict fake news. Second, mining user engagements on social media towards the news also help fake news detection. Different users have different credibility levels on social media, and https://www.nytimes.com/2016/11/28/opinion/fake-newsand-the-internet-shell-game.html? Figure 1: Tri-relationship among publishers, news pieces, and social media users for news dissemination ecosystem. the credibility score which means “the quality of being trustworthy” (Abbasi and Liu 2013) has a strong indication of whether the user is more likely to engage fake news or not. Those less credible users, such as malicious accounts or normal users who are vulnerable to fake news, are more likely to spread fake news. For example, u2 and u4 are users with low credibility scores, and they tend to spread fake news more than other highly credible users. In addition, users tend to form relationships with like-minded people. For example, user u5 and u6 are friends on social media, so they tend to engage those news that confirm their own views, such as a4. Publisher partisan information can bridge the publishernews relationship, while social engagements can capture the news-user relationship. In other words, they provide complementary information that has potential to improve fake news prediction. Thus, it’s important to integrate these two components and model the tri-relationship simultaneously. In this paper, we study the novel problem of exploiting trirelationship for fake news detection. In essence, we need to address the following challenges (1) how to mathematically model the tri-relationship to extract news feature representations; and (2) how to take the advantage of tri-relationship learning for fake news detection. In an attempt to address these challenges, we propose a novel framework TriFN that captures the Tri-relationship for Fake News detection. The main contributions are: • We provide a principled way to model tri-relationship among publisher, news, and relevant user engagements simultaneously; • We propose a novel framework TriFN that exploits trirelationship for fake news prediction; and • We evaluate the effectiveness of the proposed framework for fake news detection through extensive experiments on newly collected real-world datasets. Problem Statement Even though fake news has been existed for long time, there is no agreed definition. In this paper, we follow the definition of fake news that is widely used in recent research (Shu et al. 2017; Zubiaga et al. 2017; Allcott and Gentzkow 2017), which has been shown to be able to 1) provide theoretical and practical values for fake news topic; and 2) eliminate the ambiguities between fake news and related concepts. DEFINITION 1 (FAKE NEWS) Fake news is a news article that is intentionally and verifiably false. Let A = {a1, a2, ..., an} be the set of n news articles and U = {u1, u2, ..., um} be the set of m users on social media engaging the news spreading process. We denote X ∈ R as the news feature matrix. Users can become friends with other users and we use A ∈ {0, 1} to denote the user-user adjacency matrix. On social media sites, users can easily share, comment and discuss about the news pieces. This kind of social engagements provide auxiliary information for fake news detection. We denote the social news engagement matrix as W ∈ {0, 1}, where Wij = 1 indicate that user ui has engaged in the spreading process of the news piece aj ; otherwise Wij = 0. It’s worth mentioning that we focus on those engagements that show that users agree with the news. For example, For example, we only utilize those users that directly post the news, or repost the news without adding comments. More details will introduced in Section . We also denote P = {p1, p2, ..., pl} as the set of l news publishers. In addition, we denote B ∈ R as the publisher-news relation matrix, and Bkj = 1 means news publisher pk publishes the news article aj ; otherwise Bkj = 0. We assume that the partisan labels of some publishers are given and available. We define o ∈ {−1, 0, 1} as the partisan label vectors, where -1, 0, 1 represents left-, neutral-, and right-partisan bias. Similar to previous research (Shu et al. 2017), we treat fake news detection problem as a binary classification problem. In other words, each news piece can be true or fake, and we use y = {y1;y2; ...;yn} ∈ R n×1 to represent the labels, and yj = 1 means news piece aj is fake news; yj = −1 means true news. With the notations given above, the problem is formally defined as, Given news article feature matrix X, user adjacency matrix A, user social engagement matrix W, publishernews publishing matrix B, publisher partisan label vector o, and partial labeled news vector yL, we aim to predict remaining unlabeled news label vector yU . A Tri-Relationship Embedding Framework In this section, we propose a semi-supervised detection framework by exploiting tri-relationship. The idea of modeling tri-relationship is demonstrated in Figure 2. Specifically, we first introduce the news latent feature embedding from news content, and then show how to model user social engagements and publisher partisan separately; At last, we integrate the components to model tri-relationship and provide a semi-supervised detection framework. A Basic Model for News Content Embedding The inherent manipulators of fake news can be reflected in the news content. Thus, it’s important to extract

Discourse in online media often takes the form of siloed discussions where unpopular views tend to get drowned out by the majority. This is especially true in platforms such as Reddit, where only the most popular opinions get visibility. In this work, we propose that this problem may be solved by building tools that surface opposing views and I advance a means for automatically identifying disagreeing views on Reddit.

Models for aggregating contributions by crowd workers have been shown to be challenged by the rise of taskspecific biases or errors. Task-dependent errors in assessment may shift the majority opinion of even large numbers of workers to an incorrect answer. We introduce and evaluate probabilistic models that can detect and correct task-dependent bias automatically. First, we show how to build and use probabilistic graphical models for jointly modeling task features, workers’ biases, worker contributions and ground truth answers of tasks so that task-dependent bias can be corrected. Second, we show how the approach can perform a type of transfer learning among workers to address the issue of annotation sparsity. We evaluate the models with varying complexity on a large data set collected from a citizen science project and show that the models are effective at correcting the task-dependent worker bias. Finally, we investigate the use of active learning to guide the acquisition of expert assessments to enable automatic detection and correction of worker bias.

Political views frequently conflict in the coverage of contentious political issues, potentially causing serious social problems. We present a novel social annotation analysis approach for identification of news articles' political orientation. The approach focuses on the behavior of individual commenters. It uncovers commenters' sentiment patterns towards political news articles, and predicts the political orientation from the sentiments expressed in the comments. It takes advantage of commenters' participation as well as their knowledge and intelligence condensed in the sentiment of comments, thereby greatly reduces the high complexity of political view identification. We conduct extensive study on commenters' behaviors, and discover predictive commenters showing a high degree of regularity in their sentiment patterns. We develop and evaluate sentiment pattern-based methods for political view identification.


Question: Trajectory planning for an exoskeleton robot utilizing cubic and quintic polynomial equations.

This paper delves into the fundamental aspects of robotics, focusing specifically on the mechanics and control systems that govern robotic operations, as detailed in "Introduction to Robotics: Mechanics and Control" by John J. Craig. To broaden your understanding and adaptability in the field of robotics, it's essential to embrace knowledge from various sources, including the experiences of others and the narratives found in literature. Books serve as a reliable medium for this purpose, offering a wealth of information and insights. Among the many titles we recommend for enthusiasts and professionals alike, "Introduction to Robotics: Mechanics and Control" stands out as a comprehensive guide. This resource not only covers the basics but also provides detailed explanations and practical applications, making it a valuable tool for anyone looking to enhance their skills in robotics. The book's solution manual, in particular, offers a deeper understanding of the concepts discussed, allowing readers to apply theoretical knowledge to real-world problems, including the intricate process of trajectory planning for exoskeleton robots using cubic and quintic polynomial equations. By engaging with such resources, you can significantly expand your robotics expertise and foster a more innovative mindset.

This paper presents algorithms for computing constraints on the position of an object due to the presence of ther objects. This problem arises in applications that require choosing how to arrange or how to move objects without collisions. The approach presented here is based on characterizing the position and orientation of an object as a single point in a configuration space, in which each coordinate represents a degree of freedom in the position or orientation of the object. The configurations forbidden to this object, due to the presence of other objects, can then be characterized as regions in the configuration space, called configuration space obstacles. The paper presents algorithms for computing these configuration space obstacles when the objects are polygons or polyhedra.

The first energetically autonomous lower extremity exoskeleton capable of carrying a payload has been demonstrated at U.C. Berkeley. This paper summarizes the mechanical design of the Berkeley Lower Extremity Exoskeleton (BLEEX). The anthropomorphically-based BLEEX has seven degrees of freedom per leg, four of which are powered by linear hydraulic actuators. The selection of the degrees of freedom and their ranges of motion are described. Additionally, the significant design aspects of the major BLEEX components are covered.

An optimal control problem to find the fastest collision-free trajectory of a robot surrounded by obstacles is presented. The collision avoidance is based on linear programming arguments and expressed as state constraints. The optimal control problem is solved with a sequential programming method. In order to decrease the number of unknowns and constraints a backface culling active set strategy is added to the resolution technique.

An acrobot is a planar robot with a passive actuator in its first joint. The control problem of the acrobot tries to make it rise from the rest position to the inverted pendulum position. This control problem can be divided in the swing-up problem, when the robot has to rise itself through swinging up as a human acrobat does, and the balancing problem, when the robot has to maintain itself on the inverted pendulum position. We have developed three controllers for the swing-up problem applied to two types of motors: small and big. For small motors, we used the SARSA controller and the PD with a trajectory generator. For big motors, we propose a new controller to control the acrobot, a PWM controller. All controllers except SARSA are tuned using a Genetic Algorithm.

In this paper, we use Kinect Xbox 360 sensor to implement the motion control of Baxter robot, a semi humanoid robot with limbs of 7 DOF joints with collision avoidance capabilities. Two different methods using vector approach and inverse kinematics approach have been designed to achieve a given task. Primitive experiments have been carried out to verify the effectiveness of the developed methods. Human motions is captured by Kinect sensor and calculated with Processing Software using SimpleOpenNI wrapper for OpenNI and NITE. UDP protocol is adopted to send reference motion to Baxter robot. Python and RosPy script programming kit is used to calculate joint angles of Baxter robot based on vector approach and inverse kinematics approach. The experimental results demonstrate that both of our proposed approaches have achieved satisfactory performance.

Trajectory planning is a critical aspect in driving an autonomous tractor-trailer vehicle. This study considers trajectory planning as a minimal-time optimal control problem that incorporates the kinematics, mechanical/physical constraints, environmental requirements and an optimization criterion. This formulation benefits in the ability to directly handle time-dependent requirements. A gradient-based numerical solver is adopted to tackle the formulated optimal control problem. An incremental strategy is proposed to enhance the global optimization ability of that solver and to help find satisfactory solutions in challenging cases. Specifically, when an optimal solution is found, we doubt whether that is merely a local optimum and then still expect to make evolution; when optimal solutions are not easy to obtain, we expect at least one feasible solution first, which is taken as a preliminary guess to assist the subsequent optimization process. Both procedures proceed alternatively until no progress can be made any further. Some intricate simulation results are well beyond manual operation ability. Moreover, our overall proposal, as a unified and open framework, can deal with a wide variety of user-specified requirements.

We present GRAVITY-ASSIST, a series elastic active body weight support and inertia compensation system for use in robot assisted gait rehabilitation. The device consists of a single degree of freedom series elastic actuator that connects to the trunk of a patient. The series elastic system is novel in that, it can provide the desired level of dynamic unloading such that the patient experiences only a percentage of his/her weight and inertia. Inertia compensation is important, since the inertial forces can cause significant deviations from the desired unloading force, specially at low support forces and fast walking speeds. Furthermore, this feature enables the inertia of the harness and force sensing unit attached to the patient to be compensated for, making sure that the device does not interfere with the natural gait cycle. We present a functional prototype of the device, its characterization and experimental verification of the approach.

The authors have been using the ZMP (@ro Mfoment&oint) as a criterion to distinguish the stability of walking for a biped walking robot which has a trunk. T6 the authors introduce a control method of dynamic biped walking for a biped walking robot to compensate for the three-axis (pitch, roll and yaw-axis) moment on an arbitraryplanned ZMP by trunk motion. The authors developed a biped walking robot and performed a walking experiment with the robot using the control method. The result was a fast dynamic biped walking a t the walking speed of 0.54 slstep with a 0.3 m step on a flat floor. This walking speed is about 50 percent faster than that with the robot which compensates for only the twoaxis (pitch and roll-axis) moment by t runk motion. -z In 1986, the authors developed a biped walking robot WL12 (Waseda Leg No. 12) which has a trunk for the stabilization of walking. At the same time the authors proposed a control method of dynamic biped walking for a biped walking robot to compensate for the two-axis (pitch and roll-axis) moment on an arbitrary planned ZMP by the trunk[l]-[3). In 1990, by using this walking control method, the authors achieved dynamic biped walking at the fastest walking speed of 0.8 s/step with 0.3 m step length on a flat floor[5]. The method was based on the assumption that the contact point between robot foot and the floor would not slide. That is to say, only the pitch-axis and roll-axis moments were compensated for by trunk motion, and the yaw-axis moment was not taken into account in the stabilization of walking. In the walking experiment, however, as the robot walked faster, the yaw-axis moment began to give the robot a spin on the yaw-axis, which became a considerable problem that affected the stability of walking. In contrast, i r was considered that human beings compensate not only for the pitch and roll-axis moment but also for the yaw-axis moment by swinging both arms and rotating the waist to maintain the total stability of walking. 0-7803-0823-9p33/$3.00 (C) 1993 IEEE __ _I------" I -__-.*-Fig. 1 Biped walking robot WL-12RV Therefore, the objective of this study was to develop a biped walking robot which has an ability to compensate for the three-axis moment by trunk motion, to work out a control method of dynamic biped walking for the robot and to realize faster walking than before.

This paper presents a design procedure for six-bar linkages that use eight accuracy points to approximate a specified input-output function. In the kinematic synthesis of linkages, this is known as the synthesis of a function generator to perform mechanical computation. Our formulation uses isotropic coordinates to define the loop equations of the Watt II, Stephenson II, and Stephenson III six-bar linkages. The result is 22 polynomial equations in 22 unknowns that are solved using the polynomial homotopy software Bertini. The bilinear structure of the system yields a polynomial degree of 705,432. Our first run of Bertini generated 92,736 nonsingular solutions, which were used as the basis of a parameter homotopy solution. The algorithm was tested on the design of the Watt II logarithmic function generator patented by Svoboda in 1944. Our algorithm yielded his linkage and 64 others, in 129 minutes of parallel computation on a Mac Pro with 12⇥2.93 GHz processors. Three additional examples are provided as well.

Robotic lower limb exoskeletons that can alter joint mechanical power output are novel tools for studying the relationship between the mechanics and energetics of human locomotion. We built pneumatically powered ankle exoskeletons controlled by the user's own soleus electromyography (i.e. proportional myoelectric control) to determine whether mechanical assistance at the ankle joint could reduce the metabolic cost of level, steady-speed human walking. We hypothesized that subjects would reduce their net metabolic power in proportion to the average positive mechanical power delivered by the bilateral ankle exoskeletons. Nine healthy individuals completed three 30 min sessions walking at 1.25 m s(-1) while wearing the exoskeletons. Over the three sessions, subjects' net metabolic energy expenditure during powered walking progressed from +7% to -10% of that during unpowered walking. With practice, subjects significantly reduced soleus muscle activity (by approximately 28% root mean square EMG, P<0.0001) and negative exoskeleton mechanical power (-0.09 W kg(-1) at the beginning of session 1 and -0.03 W kg(-1) at the end of session 3; P=0.005). Ankle joint kinematics returned to similar patterns to those observed during unpowered walking. At the end of the third session, the powered exoskeletons delivered approximately 63% of the average ankle joint positive mechanical power and approximately 22% of the total positive mechanical power generated by all of the joints summed (ankle, knee and hip) during unpowered walking. Decreases in total joint positive mechanical power due to powered ankle assistance ( approximately 22%) were not proportional to reductions in net metabolic power ( approximately 10%). The ;apparent efficiency' of the ankle joint muscle-tendon system during human walking ( approximately 0.61) was much greater than reported values of the ;muscular efficiency' of positive mechanical work for human muscle ( approximately 0.10-0.34). High ankle joint ;apparent efficiency' suggests that recoiling Achilles' tendon contributes a significant amount of ankle joint positive power during the push-off phase of walking in humans.


Question: Trajectory planning for an exoskeleton robot utilizing cubic and quintic polynomial equations.

This comprehensive review examines the control algorithms designed for robotic ankle systems integrated into lower-limb orthoses, prostheses, and exoskeletons, with an emphasis on strategies for identifying gait phases, adapting to various walking conditions, and recognizing motion intentions. The review also delves into the hardware configurations and the controllers at the hardware level, providing a holistic perspective on the development of these robotic ankle systems. Additionally, the review encompasses control algorithms developed for other actuated lower-limb joints, such as the knee and hip, which have potential implications for the advancement of ankle device technologies. This approach allows for a cross-disciplinary understanding, highlighting the synergies between different joint control strategies that can be leveraged to enhance the functionality and performance of robotic ankle systems in assistive and rehabilitative applications.

Humanoid biped  robots are  typically complex in design, having numerous Degrees‐of‐Freedom (DOF) due to the ambitious goal of mimicking the human gait. The paper proposes a new architecture for a biped robot with  seven  DOF  per  each  leg  and  one  DOF corresponding  to  the  toe  joint.  Furthermore,  this work presents  close  equations  for  the  forward  and  inverse kinematics by dividing the walking gait into the Sagittal and  Frontal  planes.  This  paper  explains  the mathematical model of  the dynamics  equations  for  the legs  into  the  Sagittal  and  Frontal  planes  by  further applying the principle of Lagrangian dynamics. Finally, a control approach using a PD control law with gravity compensation  was  recurred  in  order  to  control  the desired  trajectories and  finding  the  required  torque by the  joints.  The  paper  contains  several  simulations  and numerical  examples  to  prove  the  analytical  results, using  SimMechanics  of  MATLAB  toolbox  and SolidWorks to verify the analytical results.

An optimal control problem to find the fastest collision-free trajectory of a robot surrounded by obstacles is presented. The collision avoidance is based on linear programming arguments and expressed as state constraints. The optimal control problem is solved with a sequential programming method. In order to decrease the number of unknowns and constraints a backface culling active set strategy is added to the resolution technique.

In this article, a HUmanoid Robotic Leg (HURL) via the utilization of pneumatic muscle actuators (PMAs) is presented. PMAs are a pneumatic form of actuation possessing crucial attributes for the implementation of a design that mimics the motion characteristics of a human ankle. HURL acts as a feasibility study in the conceptual goal of developing a 10 degree-of-freedom (DoF) lower-limb humanoid for compliance and postural control, while serving as a knowledge basis for its future alternative use in prosthetic robotics. HURL’s design properties are described in detail, while its 2-DoF motion capabilities (dorsiflexion–plantar flexion, eversion–inversion) are experimentally evaluated via an advanced nonlinear PID-based control algorithm.

In the human robot coexisting environment, to reach the goal efficiently and safely is very meaningful for the mobile service robot. In this paper, a Risk based Rapidly-exploring Random Tree for optimal motion planning (Risk-RRT∗) algorithm is proposed by combining the comfort and collision risk (CCR) map with the RRT∗ algorithm, which provides a variant of the RRT∗ algorithm in the dynamic human robot coexisting environment. In the experiments, the time cost in the navigation process and the length of the trajectory are utilized for the evaluation of the proposed algorithm. A comparison with the Risk-RRT algorithm is carried out and experimental results reveal that our proposed algorithm can achieve a better performance than that of the Risk-RRT in both static and dynamic environments.

We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance-sensitive robot trajectory planning provided by our approach.

F or D walkingcontr ol of a biped robot we analyze the dynamics of a three dimensional inverted pendu lum in which motion is constr aine d to move along an arbitr arily de ned plane This analysis leads us a sim ple line ar dynamics the Thr ee Dimensional Linear In verte d Pendulum Mode D LIPM Geometric nature of trajectories under the D LIPM and a method for walking pattern gener ationare discusse d A simula tion r esult of a walking contr ol using a d o f biped robot model is also shown

In recent years impressive results have been presented illustrating the potential of quadrotors to solve challenging tasks. Generally, the derivation of the controllers involve complex analytical manipulation of the dynamics and are very specific to the task at hand. In addition, most approaches construct a trajectory and then design a stabilizing controller in a separate step, whereas a fully optimal solution requires finding both simultaneously. In this paper, a generalized approach is presented using an iterative optimal control algorithm. A series of complex tasks are thus solved using the same algorithm without the need for manual manipulation of the system dynamics, heuristic simplifications, or manual trajectory generation. First, aggressive maneuvers are performed by requiring the quadrotor to pass with a slung load through a window not high enough for the load to pass while hanging straight down. Second, go-to-goal tasks with single and double rotor failure are demonstrated. The adaptability and applicability of this unified approach to such diverse tasks with a nonlinear, underactuated, constrained, and in the case of the slung load, hybrid quadrotor systems is thus shown.

For high-rise work in fields such as agriculture, forestry and architecture, and inspired by the climbing motion of inchworms, chimpanzees, and sloths, we have developed a biped climbing robot - Climbot. Consisting of five 1-DOF joint modules in series and two special grippers at the ends, Climbot is capable of grasping objects and climbing poles, trees and trusses. In this paper, we first introduce this novel robot, and then present three climbing gaits. We perform simulation to illustrate, verify and compare the proposed climbing gaits.

This paper describes a novel actuation system for very compact and light-weight robotic devices, like artificial hands. The actuation concept presented here allows the implementation of powerful tendon-based driving systems, using as actuators small-size DC motors characterized by high speed and low torque. After the presentation of the basic concept of this novel actuation system, the constitutive equations of the system are given, validated by means of laboratory tests. Moreover, the problem of tracking a desired actuation force profile is taken into account, considering as load a mass-spring-damper system. A control algorithm based on a second-order sliding manifold has been firstly evaluated by means of simulations, and then validated by experiments. This output-feedback controller has been chosen to guarantee a high level of robustness against disturbances, parameter variations and uncertainties while maintaining a low computational burden.

To control exoskeletons for walking gait assistance, it is of primary importance to control them to act synchronously with the gaits of users. To effectively estimate the gait cycle (or the phase within a stride) of users, we propose a new adaptive frequency oscillator (AFO). While previous AFOs successfully estimated the walking frequency from joint angles as inputs, the new AFO, called particularly-shaped adaptive oscillator (PSAO) can estimate gait cycle from the same inputs, which would have required foot contact sensors in previous approaches. To predict the effects of PSAO-based gait assistance on human walking, it has been tested with neuromuscular walking simulation. In the simulation, the gait assistance system reduced the metabolic cost of walking for some assistance patterns. The walk ratio (step length per step rate) also changed as assistance patterns shifted in phase, which is meaningful because metabolic cost of walking in general is minimal at specific walk ratio. For a prototype exoskeleton we developed, the effect of gait assistance was experimented on a human subject walking on level ground and inclining slopes to verify the predictions from the simulation: (1) physiological cost index computed from heart rate significantly decreased indicating reduction in metabolic energy expenditure; (2) walk ratio was in fact controllable to an extent.


Question: Trajectory planning for an exoskeleton robot utilizing cubic and quintic polynomial equations.

This paper introduces an innovative exoskeleton robot (ExoRob) designed for the rehabilitation and facilitation of upper limb motion, particularly focusing on the elbow and forearm. The significance of shoulder, elbow, and wrist movements in daily activities underscores the importance of restoring upper-limb functionality. In pursuit of a comprehensive (7DOF) upper-arm motion assistance system, this research highlights the development of a 2DOF exoskeleton robot specifically targeting elbow (flexion/extension) and forearm (pronation/supination) motions. The proposed ExoRob is designed to be worn on the lateral side of the forearm, enabling naturalistic range movements. The paper also delves into the modeling and control aspects of the ExoRob. Utilizing modified Denavit-Hartenberg notations, a kinematic model has been established. For dynamic simulation, a nonlinear sliding mode control technique has been employed to ensure trajectory tracking in line with standard rehabilitation (passive) exercises. This approach has been validated through simulations, demonstrating the controller's efficiency in maneuvering the ExoRob to accurately track desired trajectories. The controlled movements, akin to those used in rehab therapy, can be executed effectively with the ExoRob, underscoring the potential of this technology in enhancing rehabilitation outcomes.

This paper describes a brain-machine interface for the online control of a powered lower-limb exoskeleton based on electroencephalogram (EEG) signals recorded over the user’s sensorimotor cortical areas. We train a binary decoder that can distinguish two different mental states, which is applied in a cascaded manner to efficiently control the exoskeleton in three different directions: walk front, turn left and turn right. This is realized by first classifying the user’s intention to walk front or change the direction. If the user decides to change the direction, a subsequent classification is performed to decide turn left or right. The user’s mental command is conditionally executed considering the possibility of obstacle collision. All five subjects were able to successfully complete the 3-way navigation task using brain signals while mounted in the exoskeleton. We observed on average 10.2% decrease in overall task completion time compared to the baseline protocol. August 25, 2016 DRAFT

In this paper a control design of a nonholonomic mobile robot with a differential drive is presented. On the basis of robot kinematics equations a robot control is designed where the robot is controlled to follow the arbitrary path reference with a predefined velocity profile. The designed control algorithm proved stable and robust to the errors in robot initial positions, to input and output noises and to other disturbances. The obtained control law is demonstrated on a simple trajectory example, however, for a more general applicability a time-optimal motion planning algorithm considering acceleration constraints is presented as well

Robotically assisted rehabilitation therapy, which is therapy performed using a robotic device that guides or assists motion and in some cases triggered by a biological signal (such as a myoelectric signal or brain signal) is effective in recovering motor function following impairment. In the field of rehabilitation robots and human support robot, exoskeleton robots have been widely studied both for upper and lower limbs. Applying the exoskeleton to a wrist joint remains challenging because the wrist has two degrees of freedom in a structure smaller than the structures of other upper and lower limb joints and the center of rotation moves according to motion owing to the complex anatomical structure. To tackle this problem, we developed a wrist exoskeleton mechanism that consists of two elastic elements that largely deform during motion in transmitting forces from two linear actuators implemented on the user's forearm and in transforming the forces to wrist flexion/extension and adduction/abduction. The advantages of the presented device are the device's compactness, low weight, and inherent flexibility due to the elastic structure. The present letter presents an overview of the newly developed wrist exoskeleton mechanism, prototype implementation, and preliminary mechanical evaluations. The technical verification of the exoskeleton revealed the feasibility of generating forces that can generate wrist flexion/extension and adduction/abduction movements.

F or D walkingcontr ol of a biped robot we analyze the dynamics of a three dimensional inverted pendu lum in which motion is constr aine d to move along an arbitr arily de ned plane This analysis leads us a sim ple line ar dynamics the Thr ee Dimensional Linear In verte d Pendulum Mode D LIPM Geometric nature of trajectories under the D LIPM and a method for walking pattern gener ationare discusse d A simula tion r esult of a walking contr ol using a d o f biped robot model is also shown

This paper presents design and integration of the ROBHAZ-DT3, which is a newly developed mobile robot system with chained double-track mechanisms. It is designed to carry out military and civilian missions in various hazardous environments. A passive adaptation mechanism equipped between the front and rear body enables the ROBHAZ-DT3 to have good adaptability to uneven terrains including stairs. The passive adaptation mechanism reduces energy consumption when moving on uneven terrain as well as its simplicity in design and remote control, since no actuator is necessary for adaptation. Based on this novel mobile platform, a rescue version of the ROBHAZ-DT3 with appropriate sensors and a semi-autonomous mapping and localization algorithm is developed to participate in the RoboCup2004 US-Open: Urban Search and Rescue Competition. From the various experiments in the realistic rescue arena, we can verify that the ROBHAZ-DT3 is reliable in travelling rugged terrain and the proposed mapping and localization algorithm are effective in the unstructured environment with uneven ground.

This paper challenges the well-established assumption in robotics that in order to control a robot it is necessary to know its kinematic information, that is, the arrangement of links and joints, the link dimensions and the joint positions. We propose a kinematic-free robot control concept that does not require any prior kinematic knowledge. The concept is based on our hypothesis that it is possible to control a robot without explicitly measuring its joint angles, by measuring instead the effects of the actuation on its end-effector. We implement a proof-of-concept encoderless robot controller and apply it for the position control of a physical 2-DOF planar robot arm. The prototype controller is able to successfully control the robot to reach a reference position, as well as to track a continuous reference trajectory. Notably, we demonstrate how this novel controller can cope with something that traditional control approaches fail to do: adapt to drastic kinematic changes such as 100% elongation of a link, 35-degree angular offset of a joint, and even a complete overhaul of the kinematics involving the addition of new joints and links.

This paper presents the design and analysis of a passive body weight (BW)-support lower extremity exoskeleton (LEE) with compliant joints to relieve compressive load in the knee. The biojoint-like mechanical knee decouples human gait into two phases, stance and swing, by a dual snap fit. The knee joint transfers the BW to the ground in the stance phases and is compliant to free the leg in the swing phases. Along with a leg dynamic model and a knee biomechanical model, the unmeasurable knee internal forces are simulated. The concept feasibility and dynamic models of the passive LEE design have been experimentally validated with measured plantar forces. The reduced knee forces confirm the effectiveness of the LEE in supporting human BW during walking and also provide a basis for computing the internal knee forces as a percentage of BW. Energy harvested from the hip spring reveals that the LEE can save human walking energy.

Skid-steered mobile robots have been widely used for terrain exploration and navigation. In this paper, we present an adaptive trajectory control design for a skid-steered wheeled mobile robot. Kinematic and dynamic modeling of the robot is first presented. A pseudo-static friction model is used to capture the interaction between the wheels and the ground. An adaptive control algorithm is designed to simultaneously estimate the wheel/ground contact friction information and control the mobile robot to follow a desired trajectory. A Lyapunov-based convergence analysis of the controller and the estimation of the friction model parameter are presented. Simulation and preliminary experimental results based on a four-wheel robot prototype are demonstrated for the effectiveness and efficiency of the proposed modeling and control scheme

Real-time robot motion planning using rasterizing computer graphics hardware. In Proc. OY82] C. O'D unlaing and C.K. Yap. A retraction method for planning the motion of a disc. A local approach for path planning of manipulators with a high number of degrees of freedom. a path generation algorithm into oo-line programming of airbus panels. Motion planning for many degrees of freedom-random reeections at c-space obstacles. REFERENCES 31 iterative collision checker. We observed no dramatic slowdown of the planner. A challenging research goal would now be to extend the method to dynamic scenes. One rst question is: How should a roadmap computed for a given workspace be updated if a few obstacles are removed or added? Answering this question would be useful to apply our method to scenes subject to small incremental changes. Such changes occur in many manufacturing (e.g., assembly) cells; while most of the geometry of such a cell is permanent and stationary, a few objects (e.g., xtures) are added or removed between any two consecutive manufacturing operations. Similar incremental changes also occur in automatic graphic animation. A second question is: How should the learning and query phase be modiied if some obstacles are moving along known trajectories? An answer to this question might consist of applying our roadmap method in the conngurationtime space of the robot Lat91]. The roadmap would then have to be built as a directed graph, since local paths between any two nodes must monotonically progress along the time axis, with possibly additional constraints on their slope and curvature to reeect bounds on the robot's velocity and acceleration. A motion planner with performance proportional to task diiculty. 30 In KL94a, KL94b, O S94] prior versions of the method have been applied to a great variety of holonomic robots including planar and spatial articulated robots with revolute, prismatic, and/or spherical joints, xed or free base, and single or multiple kinematic chains. In Sve93, SO94] a variation of the method (essentially one with a diierent general local planner) was also run successfully on examples involving nonholonomic car-like robots. Experimental results show that our method can eeciently solve problems which are beyond the capabilities of other existing methods. For example, for planar articulated robots with many dofs, the customized implementation of Section 5 is much more consistent than the Randomized Path Planner (RPP) of BL91]. Indeed, the latter can be very fast on some diicult problems, but it …

ELECT A novel design of a biped robot inspired by domestic cats for fast running is introduced. The skeletomuscular system of a cat is analysed and applied to determine the link parameters and the linkage structure of the proposed mechanism. The linkage design of the leg mechanism is explained and a kinematic analysis based on vector loop equations is performed. The effectiveness of the proposed mechanism is verified experimentally. The biped robot runs at an average speed of 2 m/s at a step frequency of 4 Hz. This leg mechanism can facilitate the development of fast running robot systems.


Question: Trajectory planning for an exoskeleton robot utilizing cubic and quintic polynomial equations.

This document explores the application of robotics and automation in neurorehabilitation, specifically focusing on a clinical study involving twenty stroke patients. The study utilized a prototype robotic rehabilitation system developed at MIT and evaluated at Burke Rehabilitation Hospital. The paper not only discusses the outcomes of the clinical trial but also introduces a method for analyzing kinematic data obtained during robot-assisted assessments. Key findings include the absence of negative effects from robotic therapy, the patients' acceptance of the procedure, and evidence suggesting that external manipulation of the affected limb may facilitate brain recovery. These conclusions are supported by conventional clinical evaluation methods. Additionally, the document outlines an analytical technique that employs kinematic data in the context of robot-assisted assessment. The results indicate that robot-aided neurorehabilitation can be a safe and effective tool for stroke recovery, potentially enhancing brain function through targeted limb manipulation.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

**Similar Documents**: This paper presents a novel hybrid algorithm that integrates genetic algorithms (GA) and particle swarm optimization (PSO) for the design of recurrent neural networks. The proposed method, termed Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO), offers a fresh perspective on the traditional GA and PSO paradigms by incorporating elements from both. In HGAPSO, the creation of new generations is not solely reliant on GA's crossover and mutation operations; it also draws from PSO principles. The elite strategy is a central concept in HGAPSO, where the best-performing individuals (elites) in the population undergo enhancement through a PSO-like process before contributing to the next generation. Half of the new population is composed of these enhanced elites, while the other half is produced through GA operations on these elites. The paper applies HGAPSO to the design of recurrent neural networks, specifically a fully connected recurrent network for temporal sequence production and a Takagi-Sugeno-Kang-type recurrent fuzzy network for dynamic plant control. Comparative studies with standard GA and PSO demonstrate the superiority of HGAPSO in recurrent network design tasks.

**Question**: Trajectory Planning For Exoskeleton Robot By Using Cubic And Quintic Polynomial Equation

**Documents**: Robot-aided neurorehabilitation

**Similar Documents**: This document discusses the application of robotic technology in the field of neurorehabilitation. The focus is on a clinical study conducted at Burke Rehabilitation Hospital, where a robotic rehabilitation system developed at MIT was tested

The KUKA Control Toolbox (KCT) is a collection of MATLAB functions developed at the University of Siena, for motion control of KUKA robot manipulators. The toolbox, which is compatible with all 6 DOF small and low-payload KUKA robots that use the Eth.RSIXML, runs on a remote computer connected with the KUKA controller via TCP/IP. KCT includes more than 40 functions, spanning operations such as forward and inverse kinematics computation, point-to-point joint and Cartesian control, trajectory generation, graphical display, 3-D animation and diagnostics. Applicative examples show the flexibility of KCT and its easy interfacing with other toolboxes and external devices.

In this paper, we have developed a method to tele-control a robot arm to imitate human writing skills using electromyography (EMG) signals. The control method is implemented on the Baxter® robot with a brush attached on the endpoint of its arm to imitate human writing skills, and a MYO sensor is used to measure the surface electromyographic (sEMG) signals generated by contractions of human muscles involved in writing tasks. A haptic device Sensable® Omni is used to control the motion of the Baxter® robot arm, and V-Rep® is employed to simulate the movement of the arm and to calculate the inverse kinematic of Baxter® robot arm. All the communications for Baxter® robot, V-Rep simulator, Omni device and MYO are integrated in MATLAB®/Simulink®. The main test is to make the Baxter® arm following the movement of a human subject when writing with Omni stylus, and the EMG signals are processed using low pass filter and moving average technique to extract the smoothed envelope which is utilised to control the variation of position instead of variation of force along the vertical Z axis, so when the operator writes with force variations, the Baxter® can draw lines with variable thickness. This imitation system is successfully tested to make the Baxter® arm to follow the writing movement of the operator's hand and would have potential applications in more complicated human robot teleoperation tasks such as tele-rehabilitation and tele-surgery.

Despite significant advancements in the field of wearable robots (WRs), commercial WRs still use traditional direct-drive actuation units to power their joints. On the other hand, in research prototypes compliant actuators are increasingly being used to more adequately address the issues of safety, robustness, control and overall system efficiency. The advantages of mechanical compliance are exploited in a novel modular actuator prototype designed for the knee joint. Due to its modularity, the actuator can be implemented in a knee joint of a standalone or a multi-joint lower-limbs orthosis, for use in gait rehabilitation and/or walking assistance. Differently from any other actuator used in orthotic research prototypes, it combines a Variable Stiffness Actuator (VSA) and a Parallel Elasticity Actuation (PEA) unit in a single modular system. Although independent, the units are designed to work together in order to fully mimic dynamic behavior of the human knee joint. In this paper, design aspects and functional evaluation of the new actuator are presented and a rationale for such a design in biomechanics of the human knee joint is given. The VSA subsystem is characterized in a quasi-static benchmarking environment and the results showing main performance indicators are presented.

In physical Human-Robot Interaction, the basic problem of fast detection and safe robot reaction to unexpected collisions has been addressed successfully on advanced research robots that are torque controlled, possibly equipped with joint torque sensors, and for which an accurate dynamic model is available. In this paper, an end-user approach to collision detection and reaction is presented for an industrial manipulator having a closed control architecture and no additional sensors. The proposed detection and reaction schemes have minimal requirements: only the outer joint velocity reference to the robot manufacturer's controller is used, together with the available measurements of motor currents and joint positions. No a priori information on the robot dynamic model and existing low-level joint controllers is strictly needed. A suitable on-line processing of the motor currents allows to distinguish between accidental collisions and intended human-robot contacts, so as to switch the robot to a collaboration mode when needed. Two examples of reaction schemes for collaboration are presented, with the user pushing/pulling the robot at any point of its structure (e.g., for manual guidance) or with a compliant-like robot behavior in response to forces applied by the human. The actual performance of the methods is illustrated through experiments on a KUKA KR5 manipulator.

Abstract. This research focuses on developing and testing the highlevel control of a novel eight DOF hybrid robot using a DaVinci master manipulator. The teleoperation control is formulated as weighted, multi objective constrained least square (LS) optimization problems one for the master controller and the other for the slave controller. This allows us to incorporate various virtual fixtures in our control algorithm as constraints of the LS problem based on the robot environment. Experimental validation to ensure position tracking and sufficient dexterity to perform suturing in confined spaces such as throat are presented.

A co-simulation on virtual prototype of a type of spot welding robot has been accomplished by combining MATLAB/Simulink with RecurDyn. The control algorithm is executed under MATLAB to generate command forces, which are then exported to RecurDyn environment and applied to actuators of the virtual prototype at each cycle of time. Dynamic control on joint angles was accomplished through the co-simulation. Torques of each joint and the velocity of the clamp end were also acquired by co-simulation, which can give useful information for optimizing robot design.

We present a learning system which is able to quickly and reliably acquire a robust feedback control policy for 3D dynamic walking from a blank-slate using only trials implemented on our physical robot. The robot begins walking within a minute and learning converges in approximately 20 minutes. This success can be attributed to the mechanics of our robot, which are modeled after a passive dynamic walker, and to a dramatic reduction in the dimensionality of the learning problem. We reduce the dimensionality by designing a robot with only 6 internal degrees of freedom and 4 actuators, by decomposing the control system in the frontal and sagittal planes, and by formulating the learning problem on the discrete return map dynamics. We apply a stochastic policy gradient algorithm to this reduced problem and decrease the variance of the update using a state-based estimate of the expected cost. This optimized learning system works quickly enough that the robot is able to continually adapt to the terrain as it walks.

There is a strong demand in many fields for practical robots, such as a porter robot and a personal mobility robot, that can move over rough terrain while carrying a load horizontally. We have developed a robot, called RT-Mover, which shows adequate mobility performance on targeted types of rough terrain. It has four drivable wheels and two leg-like axles but only five active shafts. A strength of this robot is that it realizes both a leg mode and a wheel mode in a simple mechanism. In this paper, the mechanical design concept is discussed. With an emphasis on minimizing the number of drive shafts, a mechanism is designed for a four-wheeled mobile body that is widely used in practical locomotive machinery. Also, strategies for moving on rough terrain are proposed. The kinematics, stability, and control of RT-Mover are also described in detail. Some typical cases of rough terrain for wheel mode and leg mode are selected, and the robot’s ability of locomotion is assessed through simulations and experiments. In each case, the robot is able to move over rough terrain while maintaining the horizontal orientation of its platform.

The snakeboard is a well-studied example for mechanical systems analysis, largely because of its simultaneous richness in behavior and simplicity in design. However, few snakeboard models incorporate dissipative friction in the traveling direction and skidding as a violation of the rigid nonholonomic constraints. In this paper we investigate these effects on trajectory planning by evaluating a previously proposed friction model as well as a novel skidding model based on the addition of Rayleigh dissipation functions. We show how these additions change the usual behavior of gaits in the forward planning problem, and incorporate the changes into the solutions of the inverse planning problem by utilizing body coordinates along with a curvature parameterization for trajectories.

This paper deals with the mechanical design of a novel haptic Hand Exoskeleton (HE) that allows exerting controlled forces on the fingertip of the index and thumb of the operator. The proposed device includes several design solutions for optimizing the accuracy and mechanical performances. Remote Centers of Motion mechanisms have been adopted for delocalizing the encumbrance of linkages of the structure away from the operator's fingers. An improved stiffness of the transmission and reduced requirements for the actuators have been achieved thanks to a novel Patent Pending principle for integrating speed reduction ratio with the transmission system.


Question: Path planning for an exoskeleton robot utilizing cubic and quintic polynomial equations.

This document details the pioneering work in developing robots for rehabilitation therapy at the VA Palo Alto and Stanford University over a period spanning more than two decades. Initially, the focus was on personal assistant robots for individuals with severe disabilities. More recently, the emphasis shifted to utilizing robots for delivering rehabilitation therapy. The paper highlights three mechatronic systems designed for post-stroke therapy, outlining the guiding philosophy and experiences that have shaped their development. A distinctive feature of the Palo Alto approach is the provision for bimanual, mirror-image, patient-controlled therapeutic exercise. The feasibility of this approach was initially demonstrated using a 2-DOF elbow/forearm manipulator. Subsequent testing of a second-generation therapy robot, which facilitated planar forearm movements, in a group of 19 hemiplegic and control subjects, confirmed the reliability of interaction forces during mechanically assisted upper-limb movements. Comparative clinical trials involving 21 chronic stroke patients revealed that 3-D robot-assisted therapy resulted in significant improvements in motor recovery as measured by the Fugl-Meyer (FM) scale, outperforming traditional therapy methods.

**Question**: A Review on the Application of Artificial Neural Networks for Short-Term Load Forecasting in Power Systems

**Documents**: Artificial Neural Network for short-term load forecasting in power systems: a review
The accurate prediction of electric power load is an essential requirement for effective operation and planning of electric power systems. This paper presents a review of the application of Artificial Neural Networks (ANNs) for short-term load forecasting (STLF) in power systems. The paper begins with an introduction to ANNs and their characteristics that make them suitable for STLF. The review covers various types of ANNs, such as feed-forward, recurrent, and radial basis function networks, and their applications in STLF. The paper also discusses the impact of different input variables, including weather conditions, day type, and time of day, on the accuracy of STLF using ANNs. The review highlights the advantages of ANNs over conventional forecasting methods, such as their ability to handle non-linear relationships and their robustness to noisy data. The paper concludes with a discussion of the challenges and future research directions in the application of ANNs for STLF.

**Similar Documents**: This review paper explores the utilization of Artificial Neural Networks (ANNs) for short-term load forecasting (STLF) in power systems, emphasizing their significance for efficient operation and planning. It starts with a comprehensive overview of ANNs, highlighting their features that make them ideal for STLF tasks.

This paper proposes an effective framework of human-humanoid robot physical interaction. Its key component is a new control technique for full-body balancing in the presence of external forces, which is presented and then validated empirically. We have adopted an integrated system approach to develop humanoid robots. Herein, we describe the importance of replicating human-like capabilities and responses during human-robot interaction in this context. Our balancing controller provides gravity compensation, making the robot passive and thereby facilitating safe physical interactions. The method operates by setting an appropriate ground reaction force and transforming these forces into full-body joint torques. It handles an arbitrary number of force interaction points on the robot. It does not require force measurement at interested contact points. It requires neither inverse kinematics nor inverse dynamics. It can adapt to uneven ground surfaces. It operates as a force control process, and can therefore, accommodate simultaneous control processes using force-, velocity-, or position-based control. Forces are distributed over supporting contact points in an optimal manner. Joint redundancy is resolved by damping injection in the context of passivity. We present various force interaction experiments using our full-sized bipedal humanoid platform, including compliant balance, even when affected by unknown external forces, which demonstrates the effectiveness of the method.

This paper aims to model the forward and inverse kinematics of a KUKA KR-16KS robotic arm in the application of a simple welding process. A simple welding task to weld a block onto a metal sheet is carried out in order to investigate the forward and inverse kinematics models of KR-16KS. A movement flow planning is designed and further developed into the KR-16KS programming. Eleven points of movement are studied for the forward kinematic modeling. A summary of calculation is obtained. A general D-H representation of forward and inverse matrix is obtained. This can be used in each of the welding operation movement based on KUKA KR-16KS robotic arm. A forward kinematic and an inverse kinematic aspect of KUKA KR-16KS is successfully modeled based on a simple welding task.

A new robot toolbox for dynamics simulation based on MATLAB Graphical User Interface (GUI) is developed for educational purposes. It is built on the previous version named as ROBOLAB performing only the kinematics analysis of robot manipulators. The toolbox presented in this paper provides interactive real-time simulation and visualization of the industrial robot manipulator’s dynamics based on Langrange Euler and Newton Euler formulations. Since most of the industrial robot manipulators are produced with six degrees of freedom (DOF) such as the PUMA 560, the Fanuc ArcMate 120iB and Stanford Arm, the library of the toolbox includes sixteen fundamental 6-DOF robot manipulators with Euler wrist. The software can be used to interactively perform robotics analysis and off-line programming of robot dynamics such as forward and inverse dynamics as well as to interactively teach and simulate the basic principles of robot dynamics in a very realistic way. In order to demonstrate the user-friendly features of the toolbox much better, simulation of the NS robot manipulator (Stanford Arm) is provided as an example. 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 18: 319 330, 2010; Published online in Wiley InterScience (www.interscience.wiley.com); DOI 10.1002/cae.20262

Biped walking is easily adaptable to rough terrain such as stairs and stony paths, but the walk speed and energy efficiency on the flat ground is not so effective compared with wheeled locomotion. In this paper, we propose a biped robot to be able to walk or wheel according to the ground conditions. For wheeled locomotion, WS-2 (Waseda Shoes -No. 2) is developed which is composed of a DC motor, a spherical caster and two rubber pads on each foot. WS-2 is attached to the feet of WL-16 (Waseda Leg -No. 16) that is the world's first biped-walking robot capable of carrying a human. Also, a path planning for wheeled locomotion is presented. Through hardware experiments, the effectiveness of this foot module is confirmed.

A survey is presented of an approach to motion planning that emphasizes object-oriented, exact, and discrete (or combinatorial) algorithmic techniques in which worst-case asymptotically efficient solutions are being sought. Following a statement of the problem, motion planning in static and known environments is treated. The discussion covers general solutions, lower bounds, the projection method, the retraction method, the expanded obstacles, the single-component approach, and a mobile convex object moving in a 2D polygonal space. Variants of the motion-planning problem are then considered, namely, optimal motion planning, adaptive and exploratory motion planning, motion planning in the presence of moving obstacles, constrained motion planning, motion planning with uncertainty, and general task planning.<<ETX>>

In order to control a reaching movement of the arm and body, several different computational problems must be solved. Some parallel methods that could be implemented in networks of neuron-like processors are described. Each method solves a different part of the overall task. First, a method is described for finding the torques necessary to follow a desired trajectory. The methods is more economical and more versatile than table look-up and requires very few sequential steps. Then a way of generating an internal representation of a desired trajectory is described. This method shows the trajectory one piece at a time by applying a large set of heuristic rules to a "motion blackboard" that represents the static and dynamic parameters of the state of the body at the current point in the trajectory. The computations are simplified by expressing the positions, orientations, and motions of parts of the body in terms of a single, non-accelerating, world-based frame of reference, rather than in terms of the joint-angles or an egocentric frame based on the body itself.

This paper introduces a four-degree-of-freedom parallel manipulator dedicated to pick-and-place. It has been developed with the goal of reaching very high speed. This paper shows that its architecture is particularly well adapted to high dynamics. Indeed, it is an evolution of Delta, H4 and 14 robots architectures: it keeps the advantages of these existing robots, while overcoming their drawbacks. In addition, an optimization method based on velocity using adept motion has been developed and applied to this high speed parallel robot. All these considerations led to experimentations that proved we can reach high accelerations (13 G) and obtain a cycle time of 0.28 s.

This paper presents an extension of previous model predictive control (MPC) schemes to the stabilization of the time-varying divergent component of motion (DCM). To address the control authority limitations caused by fixed footholds, the step positions and rotations are treated as control inputs, allowing the generation and execution of stable walking motions, both at high speeds and in the face of disturbances. Rotation approximations are handled by applying a mixed-integer program, which, when combined with the use of the time-varying DCM to account for the effects of height changes, improve the versatility of MPC. Simulation results of fast walking and step recovery with the ESCHER humanoid demonstrate the effectiveness of this approach.

Traditional control and planning algorithms for wheeled mobile robots (WMR) either totally ignore or make simplifying assumptions about the effects of wheel slip on the motion. While this approach works reasonably well in practice on benign terrain, it fails very quickly when the WMR is deployed in terrain that induces significant wheel slip. We contribute a novel control framework that predictively corrects for the wheel slip to effectively minimize path following errors. Our framework, the Receding Horizon Model Predictive Path Follower (RHMPPF), specifically addresses the problem of path following in challenging environments where the wheel slip substantially affects the vehicle mobility. We formulate the solution to the problem as an optimal controller that utilizes a slip-aware model predictive component to effectively correct the controls generated by a strictly geometric pure-pursuit path follower. We present extensive experimental validation of our approach using a simulated 6-wheel skid-steered robot in a high-fidelity data-driven simulator, and on a real 4-wheel skid-steered robot. Our results show substantial improvement in the path following performance in both simulation and real world experiments.

This is the first installment of a two-part tutorial. The goal of the first part is to give the reader a basic understanding of the technical issues and types of approaches to solving the basic path planning or obstacle avoidance problem. The second installment will cover more advanced issues, including feedback, differential constraints, and uncert ainty. Note that is a brieftutorial, rather than a comprehensive surveyof methods. For the latter, consult recent textbooks [4], [9].


Question: Efficient ARX-based block cipher implementations for IoT processors.

This paper delves into the design considerations of the Simon and Speck families of lightweight block ciphers, which were meticulously crafted to provide robust security on devices with limited resources. The primary emphasis on design simplicity is essential for achieving efficient implementation on such constrained platforms. However, the versatility required for diverse applications presents a significant challenge, as it necessitates a delicate balance between simplicity, security, and flexibility. The paper elucidates the design philosophy employed to harmonize these inherently competing objectives in the creation of the Simon and Speck ciphers, offering insights into the trade-offs and strategies employed to ensure their effectiveness across a range of Internet of Things (IoT) processors.

As cloud computing is widely used in various fields, more and more individuals and organizations are considering outsourcing data to the public cloud. However, the security of the cloud data has become the most prominent concern of many customers, especially those who possess a large volume of valuable and sensitive data. Although some technologies like Homomorphic Encryption were proposed to solve the problem of secure data, the result is still not satisfying. With the advent of Intel SGX processor, which aims to thoroughly eliminate the security concern of cloud environment in a hardware-assisted approach, it brings us a number of questions on its features and its practicability for the current cloud platform. To evaluate the potential impact of Intel SGX, we analyzed the current SGX programming mode and inferred some possible factors that may arise the overhead. To verify our performance hypothesis, we conducted a systematic study on SGX performance by a series of benchmark experiments. After analyzing the experiment result, we performed a workload characterization to help programmer better exploit the current availability of Intel SGX and identify feasible research directions.

Two-factor authentication has been widely used due to the vulnerabilities associated with traditional text-based password. One-time password (OTP) plays an indispensable role on authenticating mobile users to critical web services that demand a high level of security. As the smartphones are increasingly gaining popularity nowadays, software-based OTP generators have been developed and installed into smartphones as software apps, which bring great convenience to the users without introducing extra burden. However, software-based OTP solutions cannot guarantee the confidentiality of the generated passwords or even the seeds when the mobile OS is compromised. Moreover, they also suffer from denial-of-service attacks when the mobile OS crashes. Hardware-based OTP tokens can solve these security problems in the software-based OTP solutions; however, it is inconvenient for the users to carry physical tokens with them, particularly, when there are more than one token to be carried. In this paper, we present TrustOTP, a secure one-time password solution that can achieve both the flexibility of software tokens and the security of hardware tokens by using ARM TrustZone technology. TrustOTP can not only protect the confidentiality of the OTPs against a malicious mobile OS, but also guarantee reliable OTP generation and trusted OTP display when the mobile OS is compromised or even crashes. It is flexible to integrate multiple OTP algorithms and instances for different application scenarios on the same smartphone platform without modifying the mobile OS. We develop a prototype of TrustOTP on Freescale i.MX53 QSB. The experimental results show that TrustOTP has small impacts on the mobile OS and its power consumption is low.

This paper describes the Elliptic Curve Cryptography algorithm and its suitability for smart cards.

Most hardware ‘‘True’’ Random Number Generators (TRNG) take advantage of the thermal agitation around a flip-flop metastable state. In Field Programmable Gate Arrays (FPGA), the classical TRNG structure uses at least two oscillators, build either from PLL or ring oscillators. This creates good TRNG albeit limited in frequency by the interference rate which cannot exceed a few Mbit/s. This article presents an architecture allowing higher bit rates while maintaining provable unconditional security. This speed requirement becomes stringent for secure communication applications such as the cryptographic quantum key distribution protocols. The proposed architecture is very simple and generic as it is based on an open loop structure with no specific component such as PLL. & 2009 Elsevier Ltd. All rights reserved.

Information security can be compromised by leakage via low-level hardware features. One recently prominent example is cache probing attacks, which rely on timing channels created by caches. We introduce a hardware design language, SecVerilog, which makes it possible to statically analyze information flow at the hardware level. With SecVerilog, systems can be built with verifiable control of timing channels and other information channels. SecVerilog is Verilog, extended with expressive type annotations that enable precise reasoning about information flow. It also comes with rigorous formal assurance: we prove that SecVerilog enforces timing-sensitive noninterference and thus ensures secure information flow. By building a secure MIPS processor and its caches, we demonstrate that SecVerilog makes it possible to build complex hardware designs with verified security, yet with low overhead in time, space, and HW designer effort.

The Internet of Things (IoT) is revolutionizing the Internet of the future and the way smart devices, people, and processes interact with each other. Challenges in endpoint devices (EDs) communication are due not only to the security and privacy-related requirements but also due to the ever-growing amount of data transferred over the network that needs to be handled, which can considerably reduce the system availability to perform other tasks. This paper presents an IPv6 over low power wireless personal area networks (6LoWPAN) accelerator for the IP-based IoT networks, which is able to process and filter IPv6 packets received by an IEEE 802.15.4 radio transceiver without interrupting the microcontroller. It offers nearly 13.24% of performance overhead reduction for the packet processing and the address filtering tasks, while guaranteeing full system availability when unwanted packets are received and discarded. The contribution is meant to be deployed on heterogeneous EDs at the network edge, which include on the same architecture, field-programmable gate array (FPGA) technology beside a microcontroller and an IEEE 802.15.4-compliant radio transceiver. Performed evaluations include the low FPGA hardware resources utilization and the energy consumption analysis when the processing and accepting/rejection of a received packet is performed by the 6LoWPAN accelerator.

The stream cipher ChaCha20 and the MAC function Poly1305 have been published as IETF RFC 7539. Since then, the industry is starting to use it more often. For example, it has been implemented by Google in their Chrome browser for TLS and also support has been added to OpenSSL, as well as OpenSSH. It is often claimed, that the algorithms are designed to be resistant to side-channel attacks. However, this is only true, if the only observable side-channel is the timing behavior. In this paper, we show that ChaCha20 is susceptible to power and EM side-channel analysis, which also translates to an attack on Poly1305, if used together with ChaCha20 for key generation. As a first countermeasure, we analyze the effectiveness of randomly shuffling the operations of the ChaCha round function.

Recent trends of hardware intellectual property (IP) piracy and reverse engineering pose major business and security concerns to an IP-based system-on-chip (SoC) design flow. In this paper, we propose a Register Transfer Level (RTL) hardware IP protection technique based on low-overhead key-based obfuscation of control and data flow. The basic idea is to transform the RTL core into control and data flow graph (CDFG) and then integrate a well-obfuscated finite state machine (FSM) of special structure, referred as “Mode-Control FSM”, into the CDFG in a manner that normal functional behavior is enabled only after application of a specific input sequence. We provide formal analysis of the effectiveness of the proposed approach and present a simple metric to quantify the level of obfuscation. We also present an integrated design flow that implements the proposed obfuscation at low computational overhead. Simulation results for two open-source IP cores show that high levels of security is achievable at nominal area and power overheads under delay constraint.

We develop a new, custom-built hardware for emulating contactless smartcards compliant to ISO 14443. The device is based on a modern low-cost microcontroller and can support basically all relevant (cryptographic) protocols used by contactless smartcards today, e.g., those based on AES or Triple-DES. As a proof of concept, we present a full emulation of Mifare Classic cards on the basis of our highly optimized implementation of the stream cipher Crypto1. The implementation enables the creation of exact clones of such cards, including the UID. We furthermore reverse-engineered the protocol of DESFire EV1 and realize the first emulation of DESFire and DESFire EV1 cards in the literature. We practically demonstrate the capabilities of our emulator by spoofing several real-world systems, e.g., creating a contactless payment card which allows an attacker to set the stored credit balance as desired and hence make an infinite amount of payments.

Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications. In the era of IoT and mobile systems, the efficient deployment of DNNs on embedded platforms is vital to enable the development of intelligent applications. This paper summarises our recent work on the optimised mapping of DNNs on embedded settings. By covering such diverse topics as DNN-to-accelerator toolflows, high-throughput cascaded classifiers and domain-specific model design, the presented set of works aim to enable the deployment of sophisticated deep learning models on cutting-edge mobile and embedded systems.