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Modeling, identification & control of robots
Written by two of Europe's leading robotics experts, this book provides the tools for a unified approach to the modelling of robotic manipulators, whatever their mechanical structure.No other publication covers the three fundamental issues of robotics: modelling, identification and control.
eBook
Introduction to mobile robot control
Introduction to Mobile Robot Control provides a complete and concise study of modeling, control, and navigation methods for wheeled non-holonomic and omnidirectional mobile robots and manipulators. The book begins with a study of mobile robot drives and corresponding kinematic and dynamic models, and discusses the sensors used in mobile robotics. It then examines a variety of model-based, model-free, and vision-based controllers with unified proof of their stabilization and tracking performance, also addressing the problems of path, motion, and task planning, along with localization and mapping topics. The book provides a host of experimental results, a conceptual overview of systemic and software mobile robot control architectures, and a tour of the use of wheeled mobile robots and manipulators in industry and society.Introduction to Mobile Robot Control is an essential reference, and is also a textbook suitable as a supplement for many university robotics courses. It is accessible to all and can be used as a reference for professionals and researchers in the mobile robotics field. Clearly and authoritatively presents mobile robot conceptsRichly illustrated throughout with figures and examplesKey concepts demonstrated with a host of experimental and simulation examplesNo prior knowledge of the subject is required; each chapter commences with an introduction and background
eBook
Robotic Urban Search and Rescue: A Survey from the Control Perspective
Robotic urban search and rescue (USAR) is a challenging yet promising research area which has significant application potentials as has been seen during the rescue and recovery operations of recent disaster events. To date, the majority of rescue robots used in the field are teleoperated. In order to minimize a robot operator’s workload in time-critical disaster scenes, recent efforts have been made to equip these robots with some level of autonomy. This paper provides a detailed overview of developments in the exciting and challenging area of robotic control for USAR environments. In particular, we discuss the efforts that have been made in the literature towards: 1) developing low-level controllers for rescue robot autonomy in traversing uneven terrain and stairs, and perception-based simultaneous localization and mapping (SLAM) algorithms for developing 3D maps of USAR scenes, 2) task sharing of multiple tasks between operator and robot via semi-autonomous control, and 3) high-level control schemes that have been designed for multi-robot rescue teams.
Journal Article
Achieving batch-size-of-one production model in robot flexible assembly cells
Manufacturing industry is facing new challenges in that fast-changing demands for products and services from customers push manufacturers to be more flexible and adaptive. The concept of batch-size-of-one production is presented in this paper, which defines a fully automated, highly customised, and short lead time production model. The desired batch-size-of-one production model is a promising solution for the above challenges in manufacturing industry, especially for highly customised or families of similar products like in the mobile phone industry. Along with the concept, we introduce a novel control method that enables the desired batch-size-of-one production model in operation of robots in manufacturing and assembly systems. The strategy was developed for robot control based on a distributed system to enable industrial robots to receive job commands on the fly and to conduct different jobs without the need for reconfiguration and reprogramming and without overheads. The aim of the research is to create the basis for a fully automated robot flexible assembly cell to perform batch-size-of-one assembly tasks with minimal human involvement by eliminating interruptions from the reconfiguration and reprogramming processes. The proposed strategy has been validated in practice in a multi-robot, multi-product flexible assembly cell.
Journal Article
A coarse-to-fine framework for accurate positioning under uncertainties—from autonomous robot to human–robot system
Recently, with the growing trend of high-mix, low-volume manufacturing, the demand for better flexibility and autonomy without sacrificing the accuracy of industrial robots and human–robot systems has increased. In this paper, a framework based on a coarse-to-fine strategy for industrial robots and human–robot systems is proposed to push the bounds of machine autonomy and machine flexibility while simultaneously maintaining good accuracy and efficiency. Under the proposed framework, industrial robots and human operators are designated to conduct coarse global motion with the aim of implementing low-bandwidth planning-level intelligence. Simultaneously, fine local motion for tackling accumulated on-line uncertainties is realized by an add-on robotic module with the role of implementing high-bandwidth action-level intelligence. Consequently, the overall system for both applications provides good adaptability to uncertain work conditions, while concurrently realizing fast and accurate positioning. A contour following task in two dimensions, simulating simplified tasks in industrial applications (e.g., sealant application, inspection, welding), was implemented and evaluated by autonomous robot control and human–robot collaboration.
Journal Article
Balancing of humanoid robot using contact force/moment control by task-oriented whole body control framework
Balancing control of humanoid robots is of great importance since it is a necessary functionality not only for maintaining a certain position without falling, but also for walking and running. For position controlled robots, the for-ce/torque sensors at each foot are utilized to measure the contact forces and moments, and these values are used to compute the joint angles to be commanded for balancing. The proposed approach in this paper is to maintain balance of torque-controlled robots by controlling contact force and moment using whole-body control framework with hierarchical structure. The control of contact force and moment is achieved by exploiting the full dynamics of the robot and the null-space motion in this control framework. This control approach enables compliant balancing behavior. In addition, in the case of double support phase, required contact force and moment are controlled using the redundancy in the contact force and moment space. These algorithms are implemented on a humanoid legged robot and the experimental results demonstrate the effectiveness of them.
Journal Article
Robotic Navigation and Mapping with Radar
Focusing on autonomous robotic applications, this cutting-edge resource offers you a practical treatment of short-range radar processing for reliable object detection at the ground level. This unique book demonstrates probabilistic radar models and detection algorithms specifically for robotic land vehicles. It examines grid based robotic mapping with radar based on measurement likelihood estimation. You find detailed coverage of simultaneous localization and Map Building (SLAM) - an area referred to as the Holy Grailù of autonomous robotic research. The book derives an extended Kalman Filter SLAM algorithm which exploits the penetrating ability of radar. This algorithm allows for the observation of visually occluded objects, as well as the usual directly observed objects, which contributes to a robot 's position and the map state update. Moreover, you discover how the Random Finite Set (RFS) provides a more appropriate approach for representing radar based maps than conventional frameworks.
eBook
Electroencephalography Reflects User Satisfaction in Controlling Robot Hand through Electromyographic Signals
This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were exposed to different experiences as the cutoff frequencies of a low-pass filter were changed. The participants attempted to grab a bottle by controlling a robot. They were asked to evaluate four indicators (stability, imitation, response time, and movement speed) and indicate their satisfaction at the end of each trial by completing a questionnaire. The electroencephalography signals of the participants were recorded while they controlled the robot and responded to the questionnaire. Two independent component clusters in the precuneus and postcentral gyrus were the most sensitive to subjective evaluations. For the moment that dominated satisfaction, we observed that brain activity exhibited significant differences in satisfaction not immediately after feeding an input but during the later stage. The other indicators exhibited independently significant patterns in event-related spectral perturbations. Comparing these indicators in a low-frequency band related to the satisfaction with imitation and movement speed, which had significant differences, revealed that imitation covered significant intervals in satisfaction. This implies that imitation was the most important contributing factor among the four indicators. Our results reveal that regardless of subjective satisfaction, objective performance evaluation might more fully reflect user satisfaction.
Journal Article
Adaptive Network Slicing and LSTM‐Based Resource Allocation for Real‐Time Industrial Robot Control in 6G Networks
The deployment of industrial robots in time‐critical applications demands ultra‐low latency and high reliability in communication systems. This study presents a novel delay optimisation framework for industrial robot control systems using 6G network slicing technologies. A Gale–Shapley (GS)‐based elastic switching model is proposed to dynamically match robot controllers to optimised network slices and base stations under latency‐sensitive conditions. To enhance resource adaptability, a long short‐term memory (LSTM)‐based encoder‐decoder structure is developed for predictive resource allocation across slices. The proposed integrated matching mechanism achieves a success rate of 91.16% for slice access and a base station access rate of 90.83%, outperforming conventional integrated and two‐stage schemes. The LSTM‐based resource allocation achieves a mean absolute error of 0.04 and a violation rate below 10%, with over 92% utilisation of both node and link resources. Experimental simulations demonstrate a consistent end‐to‐end latency below 7 ms and a throughput of 18.4 Mbit/s, validating the proposed models' effectiveness in ensuring robust, real‐time communication for industrial robot operations. This research contributes a scalable solution for dynamic 6G network resource management, providing a foundation for advanced industrial automation and intelligent manufacturing.
Journal Article