Question: Innovative technologies facilitating smart city services and applications.

Semantic Space: A Pervasive Computing Framework for Smart Environments
Semantic Space represents a cutting-edge pervasive computing framework that harnesses the power of semantic Web technologies to facilitate the explicit modeling, sophisticated querying, and adaptable reasoning about contexts within smart environments. This infrastructure is designed to enable a more nuanced understanding of the physical and digital contexts, thereby enhancing the functionality and intelligence of smart spaces. By integrating semantic Web technologies, Semantic Space offers a robust platform for the seamless interaction between various smart devices and systems, paving the way for more sophisticated and context-aware services in smart cities.

Vehicular applications and communications technologies are often referred to as vehicle-to-everything (V2X), which is classified into four different types: vehicle-to-vehicle (V2V), vehicle- to-infrastructure (V2I), vehicle-to-network (V2N), and vehicle-to-pedestrian (V2P) [1]. V2X related research projects, field tests, and regulatory work have been promoted in different countries and regions. In May 2015, the Ministry of Industry and Information Technology (MIIT) of China explained national strategies, "Made in China 2025," about intelligent connected vehicles. In 2020 and 2025, the overall technology and key technologies for intelligent driver assistance and automatic driving will be available in China, respectively [2]. V2X solutions are the critical technologies to support the realization of such visions. Although IEEE 802.11p has been selected as the technology for V2X communications in some countries such as the United States and in Europe, the intrinsic characteristics of IEEE 802.11p have confined the technology to support low latency with high reliability [3, 4]. Standardization of Long Term Evolution (LTE)-based V2X is being actively conducted by the Third Generation Partnership Project (3GPP) to provide the solution for V2X communications that benefit from the global deployment and fast commercialization of LTE systems. Because of the wide deployment of LTE networks, V2I and V2N services can be provided with high data rate, comprehensive quality of service (QoS) support, ubiquitous coverage, and high penetration rate [5]. Meanwhile, LTE can be extended to support V2V direct communications based on device-to-device (D2D) sidelink design to satisfy the QoS requirements, such as low latency, high reliability, and high speed in the case of high vehicle density [6].

Internet of Things (IoT) and its relevant technologies have been attracting the attention of researchers from academia, industry, and government in recent years. However, since the requirements of the IoT are quite different from what the Internet today can offer, several innovative techniques have been gradually developed and incorporated into IoT, which is referred to as the Future Internet of Things (FIoT). Among them, how to extract ‘‘data’’ and transfer them into ‘‘knowledge’’ from sensing layer to application layer has become a vital issue. This paper begins with an overview of IoT and FIoT, followed by discussions on how to apply data mining and computational intelligence to FIoT. An intelligent data management framework inspired by swarm optimization will then given. Finally, open issues and future trends of this field

Internet of Things (IoT) has recently received a great attention due to its potential and capacity to be integrated into any complex system. As a result of rapid development of sensing technologies such as radio-frequency identification, sensors and the convergence of information technologies such as wireless communication and Internet, IoT is emerging as an important technology for monitoring systems. This paper reviews and introduces a framework for structural health monitoring (SHM) using IoT technologies on intelligent and reliable monitoring. Specifically, technologies involved in IoT and SHM system implementation as well as data routing strategy in IoT environment are presented. As the amount of data generated by sensing devices are voluminous and faster than ever, big data solutions are introduced to deal with the complex and large amount of data collected from sensors installed on structures.

Smart Manufacturing is the dramatically intensified and pervasive application of networked information-based technologies throughout the manufacturing and supply chain enterprise. It responds and leads to a dramatic and fundamental business transformation to demand-dynamic economics keyed on customers, partners and the public; enterprise performance; demand-driven supply chain services; and broad-based workforce involvement. ITenabled Smart factories and supply networks can better respond to national interests and strategic imperatives and can revitalize the industrial sector by facilitating global competitiveness and exports, providing sustainable jobs, radically improving performance, and facilitating manufacturing innovation.

Most of the time, domestic energy usage is invisible to the users. Households have only a vague idea about the amount of energy they are using for different purposes during the day, and the electric bill is usually the only monthly/bimonthly feedback. Making the energy consumption promptly visible can make the difference and foster a really efficient usage of the energy in a city. The emerging smart metering infrastructure has the potential to address some of these goals, by providing both households and electric utilities useful information about energy consumption, with a detailed breakdown to single appliance contribution. This paper presents a novel ultra-low-cost smart metering system capable of providing domestic AC load identification, by implementing both basic/typical smart meter measurement and a simple yet effective load disaggregation algorithm, which can detect and monitor domestic loads starting from a single point of measurement.

Commercial buildings are attractive targets for introducing innovative cyber-physical control systems, because they are already highly instrumented distributed systems which consume large quantities of energy. However, they are not currently programmable in a meaningful sense because each building is constructed with vertically integrated, closed subsystems and without uniform abstractions to write applications against. We develop a set of operating system services called BOSS, which supports multiple portable, fault-tolerant applications on top of the distributed physical resources present in large commercial buildings. We evaluate our system based on lessons learned from deployments of many novel applications in our test building, a four-year-old, 140,000sf building with modern digital controls, as well as partial deployments at other sites.

The paper presents category classification of mobile travel applications accessible at the moment for tourists in application stores for most popular mobile operation systems (Android and iOS). The most interesting category is "Travel Guides" that combines "Information Resources" and "Location-Based Services" category. Authors propose application "Tourist assistant - TAIS" that is related to "Travel Guides" category and recommends the tourist attractions around. Information about attractions is extracted from different internet sources.

The Internet of Things is anticipated to connect billions of embedded devices equipped with sensors to perceive their surroundings. Thereby, the state of the real world will be available online and in real-time and can be combined with other data and services in the Internet to realize novel applications such as Smart Cities, Smart Grids, or Smart Healthcare. This requires an open representation of sensor data and scalable search over data from diverse sources including sensors. In this paper we show how the Semantic Web technologies RDF (an open semantic data format) and SPARQL (a query language for RDF-encoded data) can be used to address those challenges. In particular, we describe how prediction models can be employed for scalable sensor search, how these prediction models can be encoded as RDF, and how the models can be queried by means of SPARQL.

Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future. Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.

At the start of the second decade of 21th century, the time has come to make the Smart Houses a reallity for regular use. The different parts of a Smart House are researched but there are still distances from an applicable system, using the modern technology. In this paper we present an overview of the Smart House subsystems necessary for controlling the house using a mobile application efficiently and securely. The sequence diagram of the mobile application connectiing to the server application and also the usecases possible are presented. The challenges faced in designing the mobile application and illustrating the updated house top plane view in that application, are discussed and soloutions are adapted for it. Finally the designed mobile application was implemented and the important sections of it were described, such as the interactive house top view map which indicates the status of the devices using predefined icons. The facilities to manage the scheduled tasks and defined rules are also implemented in this mobile application that was developed for use in Windows Mobile platform. This application has the capability of connecting to the main server using GPRS mobile internet and SMS. This system is expected to be an important step towards a unified system structure that can be used efficiently in near future regular houses. Keywords—Smart House, Mobile Application, Remote Control, Automated Home, Windows Mobile.


Question: Utilizing a uniform evidential environment representation for grid-based mapping and tracking in dynamic settings.

This paper introduces FastSLAM, a groundbreaking algorithm designed to address the simultaneous localization and mapping (SLAM) challenge, which is crucial for the development of fully autonomous robots. Traditional approaches to SLAM, such as those based on Kalman filters, are limited by their computational complexity, which grows quadratically with the number of landmarks in the environment, making them unsuitable for real-world scenarios with vast numbers of features. FastSLAM, however, achieves a significant breakthrough by scaling logarithmically with the number of landmarks, making it capable of handling environments with an unprecedented number of landmarks. The algorithm's efficiency is attributed to an innovative factorization of the posterior distribution, which separates the robot's path distribution from the conditional landmark distributions. This allows FastSLAM to estimate the full posterior over robot pose and landmark locations in a computationally efficient manner. The algorithm has been tested successfully in environments with up to 50,000 landmarks, a scale far exceeding the capabilities of previous methods. Empirical evaluations on both simulated and real-world data have showcased the performance and limitations of FastSLAM, highlighting its potential to revolutionize the field of autonomous robotics.

Tracking, or camera pose determination, is the main technical challenge in creating augmented realities. Constraining the degree to which the environment may be altered to support tracking heightens the challenge. This paper describes several years of work at the USC Computer Graphics and Immersive Technologies (CGIT) laboratory to develop self-contained, minimally intrusive tracking systems for use in both indoor and outdoor settings. These hybrid-technology tracking systems combine vision and inertial sensing with research in fiducial design, feature detection, motion estimation, recursive filters, and pragmatic engineering to satisfy realistic application requirements.

Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features.

Autonomous driving poses unique challenges for vehicle environment perception in complex driving environments. Due to the uncertain nature of the vehicle environment and imperfection of any perception framework, multiple stages of estimation might be necessary to achieve the desired performance. However, it is highly possible that the estimation of one stage might result in output estimates with significant auto/cross-correlation, which would pass to another stage. In such situations, a decorrelation procedure is required. We present an object tracking approach taking into consideration the auto-correlation (of the velocity components) introduced by an upfront dynamic occupancy gridmap system. More specifically, we use a linear state-space system to approximately “reconstruct” the nonlinear estimation/mapping procedure for the purpose of auto-correlation quantification. We focus on demonstrating an estimation improvement of the proposed decorrelation tracker over a direct Kalman filter (i.e., KF that ignores the auto-correlation). For this we recorded several scenarios of different target motion behaviors. It is shown that the decorrelation tracker indeed introduces noticable estimation improvement in particularly velocity space if the target moves in a relatively smooth manner.

Autonomous micro aerial vehicles (MAVs) will soon play a major role in tasks such as search and rescue, environment monitoring, surveillance, and inspection. They allow us to easily access environments to which no humans or other vehicles can get access. This reduces the risk for both the people and the environment. For the above applications, it is, however, a requirement that the vehicle is able to navigate without using GPS, or without relying on a preexisting map, or without specific assumptions about the environment. This will allow operations in unstructured, unknown, and GPS-denied environments. We present a novel solution for the task of autonomous navigation of a micro helicopter through a completely unknown environment by using solely a single camera and inertial sensors onboard. Many existing solutions suffer from the problem of drift in the xy plane or from the dependency on a clean GPS signal. The novelty in the here-presented approach is to use a monocular simultaneous localization and mapping (SLAM) framework to stabilize the vehicle in six degrees of freedom. This way, we overcome the problem of both the drift and the GPS dependency. The pose estimated by the visual SLAM algorithm is used in a linear optimal controller that allows us to perform all basic maneuvers such as hovering, set point and trajectory following, vertical takeoff, and landing. All calculations including SLAM and controller are running in real time and online while the helicopter is flying. No offline processing or preprocessing is done. We show real experiments that demonstrate that the vehicle can fly autonomously in an unknown and unstructured environment. To the best of our knowledge, the here-presented work describes the first aerial vehicle that uses onboard monocular vision as a main sensor to navigate through an unknown GPS-denied environment and independently of any external artificial aids. C © 2011 Wiley Periodicals, Inc.

This paper deals with the computation of sensor motion from sets of displacement vectors obtained from consecutive pairs of images. The problem is investigated with emphasis on its application to autonomous robots and land vehicles. First, the effects of 3-D camera rotation and translation upon the observed image are discussed and in particular the concept of the focus of expansion (FOE). It is shown that locating the FOE precisely is difficult when displacement vectors are corrupted by noise and errors. A more robust performance can be achieved by computing a 2-D region of possible FOE-locations (termed the fuzzy FOE) instead of looking for a single-point FOE. The shape of this FOE-region is an explicit indicator for the accuracy of the result. It has been shown elsewhere that given the fuzzy FOE, a number of powerful inferences about the 3-D scene structure and motion become possible. This paper concentrates on the aspects of computing the fuzzy FOE and shows the performance of a particular algorithm on real motion sequences taken from a moving autonomous land vehicle. Zndex Tenns-Autonomous mobile robot, dynamic scene analysis, fuzzy focus of expansion, motion analysis, passive navigation, sensor motion estimation.

We describe how radio frequency identification (RFID) can be used in robot-assisted indoor navigation for the visually impaired. We present a robotic guide for the visually impaired that was deployed and tested both with and without visually unpaired participants in two indoor environments. We describe how we modified the standard potential fields algorithms to achieve navigation at moderate walking speeds and to avoid oscillation in narrow spaces. The experiments illustrate that passive RFID tags deployed in the environment can act as reliable stimuli that trigger local navigation behaviors to achieve global navigation objectives.

This paper present a particle filter also known as Monte Carlo Localization (MCL) to solve the localization problem presented before. A new resampling mechanism is proposed. This new resampling mechanism enables the particle filter to converge quicker and more robust to kidnaping problem. This particle filter is simulated in MATLAB and also experimented physically using a simple autonomous mobile robot built with Lego Mindstorms NXT with 3 ultrasonic sonar and RWTH Mindstorms NXT Toolbox for MATLAB to connect the robot to MATLAB. The particle filter with the new resampling algorithm can perform very well in the physical experiments.

This paper describes the UJI Librarian Robot, a mobile manipulator that is able to autonomously locate a book in an ordinary library, and grasp it from a bookshelf, by using eye-in-hand stereo vision and force sensing. The robot is only provided with the book code, a library map and some knowledge about its logical structure and takes advantage of the spatio-temporal constraints and regularities of the environment by applying disparate techniques such as stereo vision, visual tracking, probabilistic matching, motion estimation, multisensor-based grasping, visual servoing and hybrid control, in such a way that it exhibits a robust and dependable performance. The system has been tested, and experimental results show how it is able to robustly locate and grasp a book in a reasonable time without human intervention.

This paper presents the on-going design and implementation of a robust inertial sensor based Simultaneous Localisation And Mapping (SLAM) algorithm for an Unmanned Aerial Vehicle (UAV) using bearing-only observations. A single colour vision camera is used to observe the terrain from which image points corresponding to features in the environment are extracted. The SLAM algorithm estimates the complete 6-DoF motion of the UAV along with the three-dimensional position of the features in the environment. An Extended Kalman Filter (EKF) approach is used where a technique of delayed initialisation is performed to initialise the 3D position of features from bearing-only observations. Data association is achieved using a multi-hypothesis innovation gate based on the spatial uncertainty of each feature. Results are presented by running the algorithm off-line using inertial sensor and vision data collected during a flight test of a UAV.

With the development of biped robots, systems became able to navigate in a 3 dimensional world, walking up and down stairs, or climbing over small obstacles. We present a method for obtaining a labeled 2.5D grid map of the robot's surroundings. Each cell is marked either as floor or obstacle and contains a value telling the height of the floor or obstacle. Such height maps are useful for path planning and collision avoidance. The method uses a novel combination of a 3D occupancy grid for robust sensor data interpretation and a 2.5D height map for fine resolution floor values. We evaluate our approach using stereo vision on the humanoid robot QRIO and show the advantages over previous methods. Experimental results from navigation runs on an obstacle course demonstrate the ability of the method to generate detailed maps for autonomous navigation.


Question: Using a uniform evidential representation, grid-based mapping and tracking in dynamic environments.

This paper introduces an innovative approach to combining simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DTMO) for enhanced robotic navigation in both indoor and outdoor settings. Traditionally, SLAM and DTMO have been addressed as distinct challenges, each critical for robotics and automation, but often with limitations in outdoor environments or at high speeds. Here, we propose a unified solution that integrates SLAM and DTMO, recognizing their potential synergy. By merging these two functionalities, we aim to achieve comprehensive awareness and mapping of dynamic environments, critical for advanced robotic operations. The effectiveness of our integrated SLAM-DTMO method is demonstrated through practical experiments conducted using the CMU Navlab8 and Navlab11 vehicles. These tests, carried out in complex urban and suburban areas at speeds up to 45 mph, showcase the reliability and efficiency of our approach in managing the dual challenges of localization, mapping, and object tracking under dynamic conditions.

In this paper, we present an algorithm for autonomous stair climbing with a tracked vehicle. The proposed method achieves robust performance under real-world conditions, without assuming prior knowledge of the stair geometry, the dynamics of the vehicle’s interaction with the stair surface, or lighting conditions. Our approach relies on fast and accurate estimation of the robot’s heading and its position relative to the stair boundaries. An extended Kalman filter is used for quaternion-based attitude estimation, fusing rotational velocity measurements from a 3-axial gyroscope, and measurements of the stair edges acquired with an onboard camera. A twotiered controller, comprised of a centeringand a headingcontrol module, utilizes the estimates to guide the robot fast, safely, and accurately upstairs. Both the theoretical analysis and implementation of the algorithm are presented in detail, and extensive experimental results demonstrating the algorithm’s performance are described.

This paper presents the on-going design and implementation of a robust inertial sensor based Simultaneous Localisation And Mapping (SLAM) algorithm for an Unmanned Aerial Vehicle (UAV) using bearing-only observations. A single colour vision camera is used to observe the terrain from which image points corresponding to features in the environment are extracted. The SLAM algorithm estimates the complete 6-DoF motion of the UAV along with the three-dimensional position of the features in the environment. An Extended Kalman Filter (EKF) approach is used where a technique of delayed initialisation is performed to initialise the 3D position of features from bearing-only observations. Data association is achieved using a multi-hypothesis innovation gate based on the spatial uncertainty of each feature. Results are presented by running the algorithm off-line using inertial sensor and vision data collected during a flight test of a UAV.

This paper focuses on the integration of acoustic and visual information for people tracking. The system presented relies on a probabilistic framework within which information from multiple sources is integrated at an intermediate stage. An advantage of the method proposed is that of using a generative approach which supports easy and robust integration of multi source information by means of sampled projection instead of triangulation. The system described has been developed in the EU funded CHIL Project research activities. Experimental results from the CLEAR evaluation workshop are reported.

The ability to use a 2D map to navigate a complex 3D environment is quite remarkable, and even difficult for many humans. Localization and navigation is also an important problem in domains such as robotics, and has recently become a focus of the deep reinforcement learning community. In this paper we teach a reinforcement learning agent to read a map in order to find the shortest way out of a random maze it has never seen before. Our system combines several state-of-theart methods such as A3C and incorporates novel elements such as a recurrent localization cell. Our agent learns to localize itself based on 3D first person images and an approximate orientation angle. The agent generalizes well to bigger mazes, showing that it learned useful localization and naviga-

We describe how radio frequency identification (RFID) can be used in robot-assisted indoor navigation for the visually impaired. We present a robotic guide for the visually impaired that was deployed and tested both with and without visually unpaired participants in two indoor environments. We describe how we modified the standard potential fields algorithms to achieve navigation at moderate walking speeds and to avoid oscillation in narrow spaces. The experiments illustrate that passive RFID tags deployed in the environment can act as reliable stimuli that trigger local navigation behaviors to achieve global navigation objectives.

We consider an autonomous mapping and exploration problem in which a range-sensing mobile robot is guided by an information-based controller through an a priori unknown environment, choosing to collect its next measurement at the location estimated to yield the maximum information gain within its current field of view. We propose a novel and time-efficient approach to predict the most informative sensing action using a deep neural network. After training the deep neural network on a series of thousands of randomly-generated “dungeon maps”, the predicted optimal sensing action can be computed in constant time, with prospects for appealing scalability in the testing phase to higher dimensional systems. We evaluated the performance of deep neural networks on the autonomous exploration of two-dimensional workspaces, comparing several different neural networks that were selected due to their success in recent ImageNet challenges. Our computational results demonstrate that the proposed method provides high efficiency as well as accuracy in selecting informative sensing actions that support autonomous mobile robot exploration.

This technical report describes Team MIT's approach to the DARPA Urban Challenge. We have developed a novel strategy for using many inexpensive sensors, mounted on the vehicle periphery, and calibrated with a new crossmodal calibration technique. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of wellproven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course. Experimental results demonstrate all basic navigation and some basic traffic behaviors, including unoccupied autonomous driving, lane following using purepursuit control and our local frame perception strategy, obstacle avoidance using kinodynamic RRT path planning, Uturns, and precedence evaluation amongst other cars at intersections using our situational interpreter. We are working to extend these approaches to advanced navigation and traffic scenarios. † Executive Summary This technical report describes Team MIT's approach to the DARPA Urban Challenge. We have developed a novel strategy for using many inexpensive sensors, mounted on the vehicle periphery, and calibrated with a new cross-modal calibration technique. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real-time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of well-proven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course. Experimental results demonstrate all basic navigation and some basic traffic behaviors, including unoccupied autonomous driving, lane following using pure-pursuit control and our local frame perception strategy, obstacle avoidance using kino-dynamic RRT path planning, U-turns, and precedence evaluation amongst other cars at intersections using our situational interpreter. We are working to extend these approaches to advanced navigation and traffic scenarios. DISCLAIMER: The information contained in this paper does not represent the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA) or the Department of Defense. DARPA does not guarantee the accuracy or reliability of the information in this paper. Additional support …

In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the tracking of the multilevel patch features is closely coupled to the underlying extended Kalman filter (EKF) by directly using the intensity errors as innovation term during the update step. We follow a purely robocentric approach where the location of 3D landmarks are always estimated with respect to the current camera pose. Furthermore, we decompose landmark positions into a bearing vector and a distance parametrization whereby we employ a minimal representation of differences on a corresponding σ-Algebra in order to achieve better consistency and to improve the computational performance. Due to the robocentric, inverse-distance landmark parametrization, the framework does not require any initialization procedure, leading to a truly power-up-and-go state estimation system. The presented approach is successfully evaluated in a set of highly dynamic hand-held experiments as well as directly employed in the control loop of a multirotor unmanned aerial vehicle (UAV).

Recent advances of kernel methods have yielded a framework for representing probabilities using a reproducing kernel Hilbert space, called kernel embedding of distributions. In this paper, we propose a Monte Carlo filtering algorithm based on kernel embeddings. The proposed method is applied to state-space models where sampling from the transition model is possible, while the observation model is to be learned from training samples without assuming a parametric model. As a theoretical basis of the proposed method, we prove consistency of the Monte Carlo method combined with kernel embeddings. Experimental results on synthetic models and real vision-based robot localization confirm the effectiveness of the proposed approach.

We present a new SLAM system capable of producing high quality globally consistent surface reconstructions over hundreds of metres in real-time with only a low-cost commodity RGB-D sensor. By using a fused volumetric surface reconstruction we achieve a much higher quality map over what would be achieved using raw RGB-D point clouds. In this paper we highlight three key techniques associated with applying a volumetric fusion-based mapping system to the SLAM problem in real-time. First, the use of a GPU-based 3D cyclical buffer trick to efficiently extend dense every frame volumetric fusion of depth maps to function over an unbounded spatial region. Second, overcoming camera pose estimation limitations in a wide variety of environments by combining both dense geometric and photometric camera pose constraints. Third, efficiently updating the dense map according to place recognition and subsequent loop closure constraints by the use of an “as-rigid-as-possible” space deformation. We present results on a wide variety of aspects of the system and show through evaluation on de facto standard RGB-D benchmarks that our system performs strongly in terms of trajectory estimation, map quality and computational performance in comparison to other state-of-the-art systems.


Question: Using a uniform evidential representation for grid-based mapping and tracking in dynamic environments.

In urban settings, the detection of moving obstacles and the identification of free space are critical for advanced driving assistance systems and self-driving vehicles, particularly when utilizing front-facing lidar sensors. Uncertainty in these systems stems from two primary sources: ignorance and errors. Ignorance is encountered when new areas are being perceived, while errors arise from imprecise pose estimation and measurement noise, compounded by the added complexity of multi-echo and multi-layer data from the lidar. This work introduces an enhanced occupancy grid framework tailored to handle the various uncertainties inherent in dynamic environments. The proposed strategy employs grids projected onto the road surface in both global and local coordinate systems. The global grid facilitates mapping, whereas the local grid is optimized for tracking moving objects. To effectively model sensor data and integrate it with a world-fixed map, a credibilist approach is adopted for information fusion. Field tests conducted with a high-precision positioning system demonstrate that this perception strategy significantly outperforms conventional methods, enhancing the overall performance of autonomous vehicle systems in dynamic urban environments.

With the development of biped robots, systems became able to navigate in a 3 dimensional world, walking up and down stairs, or climbing over small obstacles. We present a method for obtaining a labeled 2.5D grid map of the robot's surroundings. Each cell is marked either as floor or obstacle and contains a value telling the height of the floor or obstacle. Such height maps are useful for path planning and collision avoidance. The method uses a novel combination of a 3D occupancy grid for robust sensor data interpretation and a 2.5D height map for fine resolution floor values. We evaluate our approach using stereo vision on the humanoid robot QRIO and show the advantages over previous methods. Experimental results from navigation runs on an obstacle course demonstrate the ability of the method to generate detailed maps for autonomous navigation.

Tracking, or camera pose determination, is the main technical challenge in creating augmented realities. Constraining the degree to which the environment may be altered to support tracking heightens the challenge. This paper describes several years of work at the USC Computer Graphics and Immersive Technologies (CGIT) laboratory to develop self-contained, minimally intrusive tracking systems for use in both indoor and outdoor settings. These hybrid-technology tracking systems combine vision and inertial sensing with research in fiducial design, feature detection, motion estimation, recursive filters, and pragmatic engineering to satisfy realistic application requirements.

Active SLAM poses the challenge for an autonomous robot to plan efficient paths simultaneous to the SLAM process. The uncertainties of the robot, map and sensor measurements, and the dynamic and motion constraints need to be considered in the planning process. In this paper, the active SLAM problem is formulated as an optimal trajectory planning problem. A novel technique is introduced that utilises an attractor combined with local planning strategies such as model predictive control (a.k.a. receding horizon) to solve this problem. An attractor provides high level task intentions and incorporates global information about the environment for the local planner, thereby eliminating the need for costly global planning with longer horizons. It is demonstrated that trajectory planning with an attractor results in improved performance over systems that have local planning alone

State of the art Bayesian classification approaches typically maintain a posterior distribution over possible classes given available sensor observations (images). Yet, while these approaches fuse all classifier outputs thus far, they do not provide any indication regarding how reliable the posterior classification is, thus limiting its functionality in terms of autonomous systems and robotics. On the other hand, current deep learning based classifiers provide an uncertainty measure, thereby quantifying model uncertainty. However, they do so on a single frame basis and do not consider a sequential framework. In this letter, we develop a novel approach that infers a distribution over posterior class probabilities, while accounting for model uncertainty. This distribution enables reasoning about uncertainty in the posterior classification and, therefore, is of prime importance for robust classification, object-level perception in uncertain and ambiguous scenarios, and for safe autonomy in general. The distribution of the posterior class probability has no known analytical solution; thus, we propose to approximate this distribution via sampling. We evaluate our approach in simulation and using real images fed into a convolutional neural network classifier.

Cooperative navigation (CN) enables a group of cooperative robots to reduce their individual navigation errors. For a general multi-robot (MR) measurement model that involves both inertial navigation data and other onboard sensor readings, taken at different time instances, the various sources of information become correlated. Thus, this correlation should be solved for in the process of information fusion to obtain consistent state estimation. The common approach for obtaining the correlation terms is to maintain an augmented covariance matrix. This method would work for relative pose measurements, but is impractical for a general MR measurement model, because the identities of the robots involved in generating the measurements, as well as the measurement time instances, are unknown a priori. In the current work, a new consistent information fusion method for a general MR measurement model is developed. The proposed approach relies on graph theory. It enables explicit on-demand calculation of the required correlation terms. The graph is locally maintained by every robot in the group, representing all the MR measurement updates. The developed method calculates the correlation terms in the most general scenarios of MR measurements while properly handling the involved process and measurement noise. A theoretical example and a statistical study are provided, demonstrating the performance of the method for vision-aided navigation based on a three-view measurement model. The method is compared, in a simulated environment, to a fixed-lag centralized smoothing approach. The method is also validated in an experiment that involved real imagery and navigation data. Computational complexity estimates show that the newly-developed method is computationally efficient.

The widespread use of vision-based video surveillance systems has inspired many research efforts on people localization. One of the current main trends in this field is based on probabilistic occupancy map (POM) obtained from multiple camera views. Although the POM-based approaches are robust against noisy foregrounds and can achieve great localization accuracy, they require high computation complexity. In this paper, two enhancement schemes are proposed to improve the efficiency of the POM-based people localization: (i) quick screening of potential people locations, and (ii) timely termination of iterations for occupancy probability estimation. Experimental results show that the proposed approach achieves up to 7.25 times speed-up compared to the standard POM-based approach, while delivering comparable people localization accuracy.

We present an open-source marker-based localization system intended as a low-cost easy-to-deploy solution for aerial and swarm robotics. The main advantage of the presented method is its high computational efficiency, which allows its deployment on small robots with limited computational resources. Even on low-end computers, the core component of the system can detect and estimate 3D positions of hundreds of black and white markers at the maximum frame-rate of standard cameras. The method is robust to changing lighting conditions and achieves accuracy in the order of millimeters to centimeters. Due to its reliability, simplicity of use and availability as an open-source ROS module (http://purl.org/robotics/whycon), the system is now used in a number of aerial robotics projects where fast and precise relative localization is required.

This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

This paper describes a mapping system that acquires 3D object models of man-made indoor environments such as kitchens. The system segments and geometrically reconstructs cabinets with doors, tables, drawers, and shelves, objects that are important for robots retrieving and manipulating objects in these environments. The system also acquires models of objects of daily use such glasses, plates, and ingredients. The models enable the recognition of the objects in cluttered scenes and the classification of newly encountered objects. Key technical contributions include (1) a robust, accurate, and efficient algorithm for constructing complete object models from 3D point clouds constituting partial object views, (2) feature-based recognition procedures for cabinets, tables, and other task-relevant furniture objects, and (3) automatic inference of object instance and class signatures for objects of daily use that enable robots to reliably recognize the objects in cluttered and real task contexts. We present results from the sensor-based mapping of a real kitchen.

This paper presents a new approach to the cooperative localization problem, namely distributed multi-robot localization. A group of M robots is viewed as a single system composed of robots that carry, in general, di erent sensors and have di erent positioning capabilities. A single Kalman lter is formulated to estimate the position and orientation of all the members of the group. This centralized schema is capable of fusing information provided by the sensors distributed on the individual robots while accommodating independencies and interdependencies among the collected data. In order to allow for distributed processing, the equations of the centralized Kalman lter are treated so that this lter can be decomposed into M modi ed Kalman lters each running on a separate robot. The distributed localization algorithm is applied to a group of 3 robots and the improvement in localization accuracy is presented.


Question: A uniform evidential representation for grid-based mapping and tracking in dynamic environments.

This paper presents an innovative technique for the rapid generation of occupancy grid maps using both lidar and radar sensors, critical for advanced driver-assistance systems. The method harnesses the power of the Graphics Processing Unit (GPU) to address the shortcomings of traditional occupancy grid computation methodologies in vehicular settings. With this approach, it's feasible to create highly detailed grid maps in mere milliseconds, preserving the accuracy of the sensors. Notably, when utilizing a lower-resolution radar sensor, the paper demonstrates the efficacy of applying super-resolution algorithms to attain the precision of a higher-resolution lidar scanner. Additionally, the work introduces a novel histogram-based strategy for detecting road boundaries using data from lidar and radar sensors, enhancing the overall functionality and reliability of the mapping system.

A mobile robot exploring an unknown environment has no absolute frame of reference for its position, other than features it detects through its sensors. Using distinguishable landmarks is one possible approach, but it requires solving the object recognition problem. In particular, when the robot uses two-dimensional laser range scans for localization, it is di cult to accurately detect and localize landmarks in the environment (such as corners and occlusions) from the range scans. In this paper, we develop two new iterative algorithms to register a range scan to a previous scan so as to compute relative robot positions in an unknown environment, that avoid the above problems. The rst algorithm is based on matching data points with tangent directions in two scans and minimizing a distance function in order to solve the displacement between the scans. The second algorithm establishes correspondences between points in the two scans and then solves the point-to-point least-squares problem to compute the relative pose of the two scans. Our methods work in curved environments and can handle partial occlusions by rejecting outliers.

We propose a novel approach to constrained path planning that is based on a special search space which efficiently encodes feasible paths. The paths are encoded implicitly as connections between states, but only feasible and local connections are included. Once this search space is developed, we systematically generate a near-minimal set of spatially distinct path primitives. This set expresses the local connectivity of constrained motions and also eliminates redundancies. The set of primitives is used to define heuristic search, and thereby create a very efficient path planner at the chosen resolution. We also discuss a wide variety of space and terrestrial robotics applications where this motion planner can be especially useful.

This paper describes an algorithm for deploying the members of a mobile robot team into an unknown environment. The algorithm deploys robots one-at-a-time, with each robot making use of information gathered by the previous robots to determine the next deployment location. The deployment pattern is designed to maximize the area covered by the robots’ sensors, while simultaneously ensuring that the robots maintain line-of-sight contact with one another. This paper describes the basic algorithm and presents results obtained from a series of experiments conducted using both real and simulated

In this paper, we present a monocular visual-inertial odometry algorithm which, by directly using pixel intensity errors of image patches, achieves accurate tracking performance while exhibiting a very high level of robustness. After detection, the tracking of the multilevel patch features is closely coupled to the underlying extended Kalman filter (EKF) by directly using the intensity errors as innovation term during the update step. We follow a purely robocentric approach where the location of 3D landmarks are always estimated with respect to the current camera pose. Furthermore, we decompose landmark positions into a bearing vector and a distance parametrization whereby we employ a minimal representation of differences on a corresponding σ-Algebra in order to achieve better consistency and to improve the computational performance. Due to the robocentric, inverse-distance landmark parametrization, the framework does not require any initialization procedure, leading to a truly power-up-and-go state estimation system. The presented approach is successfully evaluated in a set of highly dynamic hand-held experiments as well as directly employed in the control loop of a multirotor unmanned aerial vehicle (UAV).

Task planning for mobile robots usually relies solely on spatial information and on shallow domain knowledge, like labels attached to objects and places. Although spatial information is necessary for performing basic robot operations (navigation, localization, and obstacle avoidance), the use of deeper domain knowledge is pivotal to endow a robot with higher degrees of autonomy and intelligence. In this paper, we focus on semantic knowlege, and show how this type of knowledge can be profitably used for robot task planning. We start by defining a specific type of semantic maps, which integrate hierarchical spatial information and semantic knowledge. We then proceed to describe how these semantic maps can improve task planning in three ways: enriching the planning domain, relaxing unachievable goals, and improving the efficiency of the planner in large domains. Finally, we show several exepriments that demonstrate the effectiveness of our solutions in a doman involving robot navigation in a domestic environment.

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.

Elastic bands are proposed as the basis for a new framework to close the gap between global path planning and real-time sensor-based robot control. An elastic band is a deformable collision-free path. The initial shape of the elastic is the free path generated by a planner. Subjected to artificial forces, the elastic band deforms in real time to a short and smooth path that maintains clearance from the obstacles. The elastic continues to deform as changes in the environment are detected by sensors, enabling the robot to accommodate uncertainties and react to unexpected and moving obstacles. While providing a tight connection between the robot and its environment, the elastic band preserves the global nature of the planned path. This paper outlines the framework and discusses an efficient implementation based on bubbles.

This paper focuses on the integration of acoustic and visual information for people tracking. The system presented relies on a probabilistic framework within which information from multiple sources is integrated at an intermediate stage. An advantage of the method proposed is that of using a generative approach which supports easy and robust integration of multi source information by means of sampled projection instead of triangulation. The system described has been developed in the EU funded CHIL Project research activities. Experimental results from the CLEAR evaluation workshop are reported.

Robot navigation technology is required to accomplish difficult tasks in various environments. In navigation, it is necessary to know the information of the external environments and the state of the robot under the environment. On the other hand, various studies have been done on SLAM technology, which is also used for navigation, but also applied to devices for Mixed Reality and the like. In this paper, we propose a robot-device calibration method for navigation with a device using SLAM technology on a robot. The calibration is performed by using the position and orientation information given by the robot and the device. In the calibration, the most efficient way of movement is clarified according to the restriction of the robot movement. Furthermore, we also show a method to dynamically correct the position and orientation of the robot so that the information of the external environment and the shape information of the robot maintain consistency in order to reduce the dynamic error occurring during navigation. Our method can be easily used for various kinds of robots and localization with sufficient precision for navigation is possible with offline calibration and online position correction. In the experiments, we confirm the parameters obtained by two types of offline calibration according to the degree of freedom of robot movement and validate the effectiveness of online correction method by plotting localized position error during robot’s intense movement. Finally, we show the demonstration of navigation using SLAM device.

We describe linear-time algorithms for solving a class of problems that involve transforming a cost function on a grid using spatial information. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced by an arbitrary function on a grid. Alternatively they can be viewed in terms of the minimum convolution of two functions, which is an important operation in grayscale morphology. A consequence of our techniques is a simple and fast method for computing the Euclidean distance transform of a binary image. Our algorithms are also applicable to Viterbi decoding, belief propagation, and optimal control. ACM Classification: F.2.1, I.4 AMS Classification: 68T45, 68W40


Question: Utilizing a uniform evidential representation for grid-based mapping and tracking in dynamic environments.

This document explores an evidential methodology for constructing and updating a map for autonomous vehicles to facilitate navigation, employing a probabilistic framework. Given the inherent ambiguity in sensor measurements, a robust approach is required to amalgamate new sensory data and create a precise environmental map. The proposed system utilizes a 2-D occupancy grid as the foundational map structure. To integrate sensor readings into this grid, the Dempster–Shafer theory of evidence is applied, enabling a sophisticated fusion of data. This evidential technique, with its capacity for multivalued hypotheses, facilitates a thorough assessment of the data's reliability. The efficacy of the map-building algorithm is substantiated through empirical testing, using sonar data obtained from real-world settings. This evidential approach not only constructs an accurate map but also evaluates the quality of the sensory data, thereby enhancing the vehicle's navigational capabilities in dynamic environments.

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.

The potential impact of autonomous robotics is magnified when those robots are deployed in teams: a team of cooperating robots can greatly increase the effectiveness of a human working alone, making short work of search-and-rescue and reconnaissance tasks. To achieve this potential, however, a number of challenging problems ranging from multi-robot planning, state estimation, object detection, and human-robot interfaces must first be solved. The MAGIC 2010 competition, like the DARPA grand challenges that preceded it, presented a formidable robotics problem designed to foster fundamental advances in these difficult areas. MAGIC asked teams of robots to collaboratively explore and map a 500× 500m area, detect and track benign and dangerous objects, and collaborate with human commanders while respecting their cognitive limits. This paper describes our winning entry in the MAGIC contest, where we fielded a team of 14 autonomous robots supervised by two human operators. While the challenges in MAGIC were diverse, we believe that cooperative multi-robot state estimation is ultimately the critical factor in building a successful system. In this paper, we describe our system and some of the technological advances that we believe were responsible for our success. We also contrast our approach to those of other teams. a)

In this paper, we address the estimation, control, navigation and mapping problems to achieve autonomous inspection of penstocks and tunnels using aerial vehicles with on-board sensing and computation. Penstocks and tunnels have the shape of a generalized cylinder. They are generally dark and featureless. State estimation is challenging because range sensors do not yield adequate information and cameras do not work in the dark. We show that the six degrees of freedom (DOF) pose and velocity can be estimated by fusing information from an inertial measurement unit (IMU), a lidar and a set of cameras. This letter discusses in detail the range-based estimation part while leaving the details of vision component to our earlier work. The proposed algorithm relies only on a model of the generalized cylinder and is robust to changes in shape of the tunnel. The approach is validated through real experiments showing autonomous and shared control, state estimation and environment mapping in the penstock at Center Hill Dam, TN. To our knowledge, this is the first time autonomous navigation and mapping has been achieved in a penstock without any external infrastructure such GPS or external cameras.

We describe a method of mobile robot monocular visual navigation, which uses multiple visual cues to detect and segment the ground plane in the robot’s field of view. Corner points are tracked through an image sequence and grouped into coplanar regions using a method which we call an H-based tracker. The H-based tracker employs planar homographys and is initialised by 5-point planar projective invariants. This allows us to detect ground plane patches and the colour within such patches is subsequently modelled. These patches are grown by colour classification to give a ground plane segmentation, which is then used as an input to a new variant of the artificial potential field algorithm.

This paper describes an algorithm for deploying the members of a mobile robot team into an unknown environment. The algorithm deploys robots one-at-a-time, with each robot making use of information gathered by the previous robots to determine the next deployment location. The deployment pattern is designed to maximize the area covered by the robots’ sensors, while simultaneously ensuring that the robots maintain line-of-sight contact with one another. This paper describes the basic algorithm and presents results obtained from a series of experiments conducted using both real and simulated

This paper describes an architecture that combines the complementary strengths of probabilistic graphical models and declarative programming to enable robots to represent and reason with qualitative and quantitative descriptions of uncertainty and domain knowledge. An action language is used for the architecture’s low-level (LL) and high-level (HL) system descriptions, and the HL definition of recorded history is expanded to allow prioritized defaults. For any given objective, tentative plans created in the HL using commonsense reasoning are implemented in the LL using probabilistic algorithms, and the corresponding observations are added to the HL history. Tight coupling between the levels helps automate the selection of relevant variables and the generation of policies in the LL for each HL action, and supports reasoning with violation of defaults, noisy observations and unreliable actions in complex domains. The architecture is evaluated in simulation and on robots moving objects in indoor domains.

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.

Applications that provide location related services need to understand the environment in which humans live such that verbal references and human interaction are possible. We formulate this semantic labelling task as the problem of learning the semantic labels from the perceived 3D structure. In this contribution we propose a batch approach and a novel multi-view frame fusion technique to exploit multiple views for improving the semantic labelling results. The batch approach works offline and is the direct application of an existing single-view method to scene reconstructions with multiple views. The multi-view frame fusion works in an incremental fashion accumulating the single-view results, hence allowing the online multi-view semantic segmentation of single frames and the offline reconstruction of semantic maps. Our experiments show the superiority of the approaches based on our fusion scheme, which leads to a more accurate semantic labelling.

We introduce a method for following high-level navigation instructions by mapping directly from images, instructions and pose estimates to continuous low-level velocity commands for real-time control. The Grounded Semantic Mapping Network (GSMN) is a fully-differentiable neural network architecture that builds an explicit semantic map in the world reference frame by incorporating a pinhole camera projection model within the network. The information stored in the map is learned from experience, while the local-to-world transformation is computed explicitly. We train the model using DAGGERFM, a modified variant of DAGGER that trades tabular convergence guarantees for improved training speed and memory use. We test GSMN in virtual environments on a realistic quadcopter simulator and show that incorporating an explicit mapping and grounding modules allows GSMN to outperform strong neural baselines and almost reach an expert policy performance. Finally, we analyze the learned map representations and show that using an explicit map leads to an interpretable instruction-following model.

The combination of on-board sensors measurements with different statistical characteristics can be employed in robotics for localization and control, especially in GPS-denied environments. In particular, most aerial vehicles are packaged with low cost sensors, important for aerial robotics, such as camera, a gyroscope, and an accelerometer. In this work, we develop a visual inertial odometry system based on the Unscented Kalman Filter (UKF) acting on the Lie group SE(3), such to obtain an unique, singularity-free representation of a rigid body pose. We model this pose with the Lie group SE(3) and model the noise on the corresponding Lie algebra. Moreover, we extend the concepts used in the standard UKF formulation, such as state uncertainty and modeling, to correctly incorporate elements that do not belong to an Euclidean space such as the Lie group members. In this analysis, we use the parallel transport, which requires us to explicitly consider SE(3) as representing rigid bodies though the use of the affine connection. We present experimental results to show the effectiveness of the proposed approach for state estimation of a quadrotor platform.


Question: An HF outphasing transmitter employing class-E power amplifiers.

RF and Microwave Power Generation: Power Amplifiers and Transmitter Architectures
The demand for RF/microwave power spans across diverse fields, encompassing wireless communications, jamming, imaging, RF heating, and even miniature dc/dc converters. Each application presents specific criteria concerning frequency, bandwidth, load impedance, power level, efficiency, linearity, and cost-effectiveness. The generation of RF power employs a broad spectrum of methodologies, circuit designs, and active components. Power amplifiers find integration into transmitters across a multitude of configurations, ranging from linear to more complex topologies such as Kahn, envelope tracking, outphasing, and Doherty. To enhance linearity, advanced techniques like feedback, feedforward, and predistortion are employed, each tailored to optimize performance within the context of their respective applications.

This paper presents a practical implementation of an integrated MRI-compatible CMOS amplifier capable of directly driving a piezoelectric ultrasound transducer suitable for high-intensity focused ultrasound (HIFU) therapy. The amplifier operates in Class DE mode without the need for an output matching network. The integrated amplifier has been implemented with the AMS AG H35 CMOS process. A class DE amplifier design methodology for driving unmatched piezoelectric loads is presented along with simulation and experimental results. The proposed design achieves approximately 90% efficiency with over 800 mW of output power at 1010 kHz. The total die area including pads is 2 mm2. Compatibility with MRI was validated with B1 imaging of a phantom and the amplifier circuit.

We have developed a flip-chip interconnection technique to solve the problem of the degradation of the modulation bandwidth of an optical transmitter due to the parasitic inductance of bonding wire. And we have fabricated an LE-EA-DFB laser module that has a 3-dB bandwidth of 59 GHz thanks to our flip-chip interconnection technique. The fabricated optical receiver, which includes a wideband electrical amplifier, has a 3-dB bandwidth of 51 GHz. Using these optical modules with a 3-dB bandwidth of more than 50 GHz, we have obtained a bit-error rate of less than 2 × 10-4, which is an error-free condition using KP4 forward error correction, under 53.2-Gbaud 4-level pulse amplitude modulation (4-PAM) operation without an equalizer even after a 2-km transmission through single-mode fiber.

This paper describes the design and modeling of CMOS transistors, integrated passives, and circuit blocks at millimeter-wave (mm-wave) frequencies. The effects of parasitics on the high-frequency performance of 130-nm CMOS transistors are investigated, and a peak f/sub max/ of 135 GHz has been achieved with optimal device layout. The inductive quality factor (Q/sub L/) is proposed as a more representative metric for transmission lines, and for a standard CMOS back-end process, coplanar waveguide (CPW) lines are determined to possess a higher Q/sub L/ than microstrip lines. Techniques for accurate modeling of active and passive components at mm-wave frequencies are presented. The proposed methodology was used to design two wideband mm-wave CMOS amplifiers operating at 40 GHz and 60 GHz. The 40-GHz amplifier achieves a peak |S/sub 21/| = 19 dB, output P/sub 1dB/ = -0.9 dBm, IIP3 = -7.4 dBm, and consumes 24 mA from a 1.5-V supply. The 60-GHz amplifier achieves a peak |S/sub 21/| = 12 dB, output P/sub 1dB/ = +2.0 dBm, NF = 8.8 dB, and consumes 36 mA from a 1.5-V supply. The amplifiers were fabricated in a standard 130-nm 6-metal layer bulk-CMOS process, demonstrating that complex mm-wave circuits are possible in today's mainstream CMOS technologies.

A class-AB architecture for single-stage operational amplifiers is presented. The structure employs a switched-capacitor level shifter to apply the signal to both sink and source output transistors to create the class-AB behavior. Using this structure, the current mirror circuit of traditional class-AB structures can be eliminated. Thus some power can be saved and the operation frequency can be increased. Simulation results of a fast-settling telescopic-cascode op-amp employing this pseudo-class-AB technique confirm the effectiveness of the approach to reduce the power consumption. This approach can be applied to the first stage of a two-stage op-amp as well, to increase the slew rate. The simulated 12-bit 100-Ms/s sample-and-hold amplifier employing the proposed pseudo-class-AB telescopic-cascode op-amp consumes only 16 mW from 3.3 V power supply.

This paper deals with a relatively new area of radio-frequency (RF) technology based on microelectromechanical systems (MEMS). RF MEMS provides a class of new devices and components which display superior high-frequency performance relative to conventional (usually semiconductor) devices, and which enable new system capabilities. In addition, MEMS devices are designed and fabricated by techniques similar to those of very large-scale integration, and can be manufactured by traditional batch-processing methods. In this paper, the only device addressed is the electrostatic microswitch—perhaps the paradigm RF-MEMS device. Through its superior performance characteristics, the microswitch is being developed in a number of existing circuits and systems, including radio front-ends, capacitor banks, and time-delay networks. The superior performance combined with ultra-low-power dissipation and large-scale integration should enable new system functionality as well. Two possibilities addressed here are quasi-optical beam steering and electrically reconfigurable antennas.

A fully integrated technique for wideband cancellation of transmitter (TX) self-interference (SI) in the RF domain is proposed for multiband frequency-division duplexing (FDD) and full-duplex (FD) wireless applications. Integrated wideband SI cancellation (SIC) in the RF domain is accomplished through: 1) a bank of tunable, reconfigurable second-order high-Q RF bandpass filters in the canceller that emulate the antenna interface's isolation (essentially frequency-domain equalization in the RF domain) and 2) a linear N-path Gm-C filter implementation with embedded variable attenuation and phase shifting. A 0.8-1.4 GHz receiver (RX) with the proposed wideband SIC circuits is implemented in a 65 nm CMOS process. In measurement, >20 MHz 20 dB cancellation bandwidth (BW) is achieved across frequency-selective antenna interfaces: 1) a custom-designed LTElike 0.780/0.895 GHz duplexer with TX/RX isolation peak magnitude of 30 dB, peak group delay of 11 ns, and 7 dB magnitude variation across the TX band for FDD and 2) a 1.4 GHz antenna pair for FD wireless with TX/RX isolation peak magnitude of 32 dB, peak group delay of 9 ns, and 3 dB magnitude variation over 1.36-1.38 GHz. For FDD, SIC enhances the effective outof-band (OOB) IIP3 and IIP2 to +25-27 dBm and +90 dBm, respectively (enhancements of 8-10 and 29 dB, respectively). For FD, SIC eliminates RX gain compression for as high as -8 dBm of peak in-band (IB) SI, and enhances effective IB IIP3 and IIP2 by 22 and 58 dB.

In the last quarter of a century, high-frequency (HF) transformer design has been one of the major concerns to power electronics designers in order to increase converter power densities and efficiencies. Conventional design methodologies are based on iterative processes and rules of thumb founded more on expertise than on theoretical developments. This paper presents an analytical design methodology for litz-wired HF power transformers that provides a deep insight into the transformer design problem making it a powerful tool for converter designers. The most suitable models for the calculation of core and winding losses and the transformer thermal resistance are first selected and then validated with a 5-kW 50-kHz commercial transformer for a photovoltaic application. Based on these models, the design methodology is finally proposed, reducing the design issue to directly solve a five-variable nonlinear optimization problem. The methodology is illustrated with a detailed design in terms of magnetic material, core geometry, and primary and secondary litz-wire sizing. The optimal design achieves a 46.5% power density increase and a higher efficiency of 99.70% when compared with the commercial one.

A novel high performance envelope detector is offered in this work. Proposed circuit holds the signal peaks in two periods and combines them to obtain the envelope of the signal. This way, ripple is fixed by the peak holder and tracking can be improved without the traditional compensation between keeping and tracking required in these circuits. A comparison is offered between a conventional circuit, a previous work and the proposed envelope detector. It is shown the superior performance of the latter obtaining small ripple (<1%), fast settling (0.4mus) and high linearity.

CMOS transmit-receive (T/R) switches have been integrated in a 0.18-/spl mu/m standard CMOS technology for wireless applications at 2.4 and 5.2 GHz. This switch design achieves low loss and high linearity by increasing the substrate impedance of a MOSFET at the frequency of operation using a properly tuned LC tank. The switch design is asymmetric to accommodate the different linearity and isolation requirements in the transmit and receive modes. In the transmit mode, the switch exhibits 1.5-dB insertion loss, 28-dBm power, 1-dB compression point (P/sub 1dB/), and 30-dB isolation, at 2.4 and 5.2 GHz. In the receive mode, the switch achieves 1.6-dB insertion loss, 11.5-dBm P/sub 1dB/, and 15-dB isolation, at 2.4 and 5.2 GHz. The linearity obtained in the transmit mode is the highest reported to date in a standard CMOS process. The switch passes the 4-kV Human Body Model electrostatic discharge test. These results show that the switch design is suitable for narrow-band applications requiring a moderate-high transmitter power level (<1 W).

The idea of using higher order modes is demonstrated by an SIW slot array antenna with a TE20 mode to realize boresight radiation. In order to excite the TE20 mode, an SIW hybrid coupler and a compact SIW 90° phase shifter are designed to offer the two out-of-phase outputs with identical amplitude. Besides, a CPW-SIW transition is adopted for measurement. The SIW slot array antenna with the TE20 mode is designed at X-band in a single-layer PCB. The measured bandwidth is 5.5% (9.72–10.27 GHz). The measured total efficiency is 80% at 10 GHz. Compared to a conventional SIW slot array just with a power divider, using the TE20 mode provides the merits of not only reducing the number of vias but also relaxing fabrication limit and tolerance which could be the fabrication bottleneck at high frequencies.


Question: An HF outphasing transmitter utilizing class-E power amplifiers.

This paper explores the implementation of a high-efficiency outphasing transmitter utilizing class-E power amplifiers for enhanced performance. Unlike conventional transmitter designs, this approach focuses on optimizing the power amplification stage by adopting class-E amplifiers, which are renowned for their high efficiency at the expense of being more complex to design and control. The outphasing technique, when combined with class-E power amplifiers, offers a significant advantage in terms of energy conservation, making it particularly suitable for high-power applications. The class-E amplifier operates by employing a resonant circuit at the output, which facilitates the zero-voltage switching and zero-current turn-off conditions necessary for minimizing switching losses. This results in a more efficient conversion of electrical power into radio frequency (RF) energy. In the context of outphasing modulation, the input signal is split into two complementary amplitude-modulated signals, each driving a separate class-E power amplifier. These signals are then recombined at the output with a phase difference that ensures the sum of their amplitudes matches the original input signal. This method not only improves efficiency but also reduces the overall system complexity by eliminating the need for a high-power RF amplifier. The paper further discusses the design considerations and challenges associated with implementing such a system, including the selection of appropriate components, the optimization of the outphasing algorithm, and the control strategies required for maintaining stable operation under varying load conditions. Through a series of simulations and experimental results, the superiority of the proposed outphasing transmitter using class-E power amplifiers is demonstrated, particularly in terms of power efficiency and linearity.

This paper describes a transmitter driver based on a CMOS inverter with a resistive feedback. By employing the proposed driver topology, the pre-driver can be greatly simplified, resulting in a remarkable reduction of the overall driver power consumption. It also offers another advantage that the implementation of equalization is straightforward, compared with a conventional voltage-mode driver. Furthermore, the output impedance remains relatively constant while the data is being transmitted, resulting in good signal integrity. For evaluation of the driver performance, a fully functional 20 Gb/s transmitter is implemented, including a PRBS generator, a serializer, and a half-rate clock generator. In order to enhance the overall speed of the digital circuits for 20 Gb/s data transmission, the resistive feedback is applied to the time-critical inverters, which enables shorter rise/fall times. The prototype chip is fabricated in a 65 nm CMOS technology. The implemented driver circuit operates up to the data rate of 20 Gb/s, exhibiting an energy efficiency of 0.4 pJ/b for the output swing of 250 mVpp,diff.

A high-gain, broadband, and low-profile continuous transverse stub antenna array is presented in E-band. This array comprises 32 long slots excited in parallel by a uniform corporate parallel-plate-waveguide beamforming network combined to a pillbox coupler. The radiating slots and the corporate feed network are built in aluminum whereas the pillbox coupler and its focal source are fabricated in printed circuit board technology. Specific transitions have been designed to combine both fabrication technologies. The design, fabrication, and measurement results are detailed, and a simple design methodology is proposed. The antenna is well matched ( $S_{11} < -13.6$  dB) between 71 and 86 GHz, and an excellent agreement is found between simulations and measurements, thus validating the proposed design. The antenna gain is higher than 29.3 dBi over the entire bandwidth, with a peak gain of 30.8 dBi at 82.25 GHz, and a beam having roughly the same half-power beamwidth in E- and H-planes. This antenna architecture is considered as an innovative solution for long-distance millimeter-waves telecommunication applications such as fifth-generation backhauling in E-band.

This paper presents a compact and power-efficient 5 Ghz in-band full-duplex (FD) design in ANSYS HFSS using the 180-degree ring hybrid coupler. The proposed design achieves an excellent isolation of 57dB by taking advantage of destructive interference between two radiating antennas attached to the coupler, leading to a large reduction in self-interference. The design is passive and hence overcomes additional power requirement for adaptive channel estimation. In addition, it has a very workable physical size for the desired frequency of operation. The proposed FD design is therefore compact and power-efficient, which can be used in mobile devices, such as cell phones or tablet/phablet devices for a more flexible and greater contention of scarce RF resources.

We have demonstrated a decade bandwidth 100 W GaN HEMT power amplifier module with 15.5–18.6 dB gain, 104–121 W CW output power and 61.4–76.6 % drain efficiency over the 100–1000 MHz band. The 2 × 2 inch compact power amplifier module combines four 30 W lossy matched broadband GaN HEMT PAs packaged in a ceramic SO8 package. Each of the 4 devices is fully matched to 50 Ω and obtains 30.8–35.7 W with 68.6–79.6 % drain efficiency over the band. The packaged amplifiers contain a GaN on SiC device operating at 48V drain voltage, alongside GaAs integrated passive matching circuitry. The four devices are combined using a broadband low loss coaxial balun. We believe this combination of output power, bandwidth and efficiency is the best reported to date. These amplifiers are targeted for use in multi-band public mobile radios and for instrumentation applications.

We describe a straightforward method of separately characterizing up- and down-conversion in microwave mixers using a sampling oscilloscope. The method mismatch-corrects the results, determines both magnitude and phase, and uses a novel time-base correction scheme to improve the accuracy of the measurements. We estimate our measurement accuracy to be on the order of a tenth of a decibel in magnitude and a few degrees in phase. We use the method to characterize the magnitude and phase reciprocity of a microwave mixer.

A pair of novel 180deg broadband microstrip baluns are used to feed a dual-polarized patch antenna. The 180deg broadband balun delivers equal amplitude power division and consistent 180deg (plusmn5deg) phase shifting over a wide bandwidth (>50%). We demonstrate that for a dual-polarized quadruple L-probe square patch antenna, the use of the proposed 180deg broadband balun pair, in place of a conventional 180deg narrowband balun pair, provides improved input port isolation and reduced H-plane cross-polarization levels over a wider frequency range, while maintaining low E-plane cross-polarization levels and stable E- and H-plane co-polarization patterns throughout the impedance passband

This paper describes an integrated frequency multiplier, implemented as a Gilbert cell based frequency doubler in a 130 nm SiGe BiCMOS technology. The circuit demonstrates a 3 dB bandwidth of 97–134GHz with peak output power of 1 dBm for 1 dBm input power. The fundamental suppression, measured at the single-ended output, is better than 21 dBc while the frequency doubler consumes 69mW from a 3.3V supply. The doubler is preceded by a differential amplifier functioning as an active balun to generate a differential signal for the Gilbert cell.

In this paper, based on rigid-flex printed circuit board (PCB), a compact solution of Antenna-in-Package (AiP) including RF transmitter and microstrip passive phased antenna array is demonstrated. Apart from the landing of two connectors, the size shrinks down to 35 mm × 32 mm. RF transmitter works at the band of 26.2 GHz to 27.2 GHz, with conversion gain of 13 dB and rejection ratio up to 17.5 dBc. The 1 × 4 microstrip passive phased antenna array integrating 4 × 4 Butler Matrix achieves four scanning angles which are -10°, 30°, -30° and 10° with 20° half power beam width (HPBW), and simulated absolute gain is 13.51 dBi, 12.31 dBi, 12.23 dBi and 13.26 dBi respectively. Minor deviation on scanning angles from simulation data is observed in the testing due to measurement and manufacturing tolerance. However, among operating bandwidth, HPBW, main-to-side lobe ratio and the difference between two scanning angles when port 1, 4 and port 2, 3 are excited respectively are in good agreement with simulation results

A novel phase-shift control of a semibridgeless active rectifier (S-BAR) is investigated in order to utilize the S-BAR in wireless energy transfer applications. The standard receiver-side rectifier topology is developed by replacing rectifier lower diodes with synchronous switches controlled by a phase-shifted PWM signal. Theoretical and simulation results show that with the proposed control technique, the output quantities can be regulated without communication between the receiver and transmitter. To confirm the performance of the proposed converter and control, experimental results are provided using 8-, 15-, and 23-cm air gap coreless transformer which has dimension of 76 cm × 76 cm, with 120-V input and the output power range of 0 to 1kW with a maximum efficiency of 94.4%.

High voltage high frequency (HF) transformer provides the isolation between high and low dc link voltages in dual active bridge (DAB) converters. Such DAB converters are finding wide applications as an intermediate DC-DC converter in transformerless intelligent power substation (TIPS), which is proposed as an alternative for conventional distribution-transformer connecting 13.8 kV and 480 V grids. The design of HF transformer used in DAB stage of such application is very challenging considering the required isolation, size and cost. In this paper, the specification generation, design, characterization, test and measurement results on a 10kHz HF transformer are presented, highlighting the above challenges.