Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Control Strategies Of Human Interactive Robot Under Uncertain Environments PDF full book. Access full book title Control Strategies Of Human Interactive Robot Under Uncertain Environments.
| Author | : Haiwei Dong |
| Publisher | : |
| Total Pages | : |
| Release | : 2011 |
| Genre | : |
| ISBN | : 9789533078427 |
| Author | : Binghan He |
| Publisher | : |
| Total Pages | : 362 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
Human-centered robots are a specific kind of service robot, which interact with humans physically or cognitively and help humans with tasks in uncertain environments. They can be humanoid robots, exoskeletons, or manipulators and mobile platforms that provide us good services. However, human-centered robots are still not perfect enough for us to use nowadays. On the one hand, human-centered robots are still slow and inefficient for their tasks because the human inputs and dynamics that they react to are uncertain, immeasurable, or even completely unknown. On the other hand, human-centered robots face much more complicated safety requirements than other kinds of robots because humans are dynamic and vulnerable during physical human-robot interaction. To resolve these issues of human-centered robots, the work in this dissertation explores new models for reducing human uncertainty and new control algorithms for improving safety warranty. The first half of this dissertation introduces a complex stiffness model for describing the uncertain human impedance. The discovery of this new model is motivated to explain the observation of a consistent damping ratio of a human versus different environmental dynamics. It replaces the linear damping term in a conventional mass-spring-damping model with a hysteretic damping term, an imaginary value in the frequency domain. Because of the correlation between the stiffness term and the newly discovered hysteretic damping term in the complex stiffness model, we can significantly reduce the human impedance uncertainty. Based on the complex stiffness model, we can adopt nonlinear control strategies for improving the responsiveness and the human-friendliness of human-centered robots. The second half of this dissertation introduces the concept of a barrier pair, which consists of a barrier function and a controller for the safety verification and warranty of a human-centered robot. We obtain a barrier pair by solving an optimization problem subject to a series of linear matrix inequalities representing the state-space, input, and stability constraints of a human-centered robot. By incorporating sampling-based methods into the synthesis of barrier pairs, human-centered robots can guarantee safe operation with non-convex state-space constraints. The sampling-based barrier pair method helps us construct a control framework of human-robot shared autonomy. A human-centered robot in this control framework uses an inference of a human's objective to figure out how to assist the human and prevent the human from potential accidents
| Author | : Yue Wang |
| Publisher | : Springer |
| Total Pages | : 424 |
| Release | : 2017-01-24 |
| Genre | : Technology & Engineering |
| ISBN | : 3319405330 |
This book provides an overview of recent research developments in the automation and control of robotic systems that collaborate with humans. A measure of human collaboration being necessary for the optimal operation of any robotic system, the contributors exploit a broad selection of such systems to demonstrate the importance of the subject, particularly where the environment is prone to uncertainty or complexity. They show how such human strengths as high-level decision-making, flexibility, and dexterity can be combined with robotic precision, and ability to perform task repetitively or in a dangerous environment. The book focuses on quantitative methods and control design for guaranteed robot performance and balanced human experience from both physical human-robot interaction and social human-robot interaction. Its contributions develop and expand upon material presented at various international conferences. They are organized into three parts covering: one-human–one-robot collaboration; one-human–multiple-robot collaboration; and human–swarm collaboration. Individual topic areas include resource optimization (human and robotic), safety in collaboration, human trust in robot and decision-making when collaborating with robots, abstraction of swarm systems to make them suitable for human control, modeling and control of internal force interactions for collaborative manipulation, and the sharing of control between human and automated systems, etc. Control and decision-making algorithms feature prominently in the text, importantly within the context of human factors and the constraints they impose. Applications such as assistive technology, driverless vehicles, cooperative mobile robots, manufacturing robots and swarm robots are considered. Illustrative figures and tables are provided throughout the book. Researchers and students working in controls, and the interaction of humans and robots will learn new methods for human–robot collaboration from this book and will find the cutting edge of the subject described in depth.
| Author | : Janusz Bȩdkowski |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 406 |
| Release | : 2011-12-02 |
| Genre | : Technology & Engineering |
| ISBN | : 9533078421 |
The objective of this book is to cover advances of mobile robotics and related technologies applied for multi robot systems' design and development. Design of control system is a complex issue, requiring the application of information technologies to link the robots into a single network. Human robot interface becomes a demanding task, especially when we try to use sophisticated methods for brain signal processing. Generated electrophysiological signals can be used to command different devices, such as cars, wheelchair or even video games. A number of developments in navigation and path planning, including parallel programming, can be observed. Cooperative path planning, formation control of multi robotic agents, communication and distance measurement between agents are shown. Training of the mobile robot operators is very difficult task also because of several factors related to different task execution. The presented improvement is related to environment model generation based on autonomous mobile robot observations.
| Author | : Wen Yu |
| Publisher | : John Wiley & Sons |
| Total Pages | : 288 |
| Release | : 2021-10-06 |
| Genre | : Technology & Engineering |
| ISBN | : 1119782767 |
A comprehensive exploration of the control schemes of human-robot interactions In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation. Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control. The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics. Readers will also enjoy: A thorough introduction to model-based human-robot interaction control Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.
| Author | : Miomir Vukobratovi? |
| Publisher | : World Scientific |
| Total Pages | : 657 |
| Release | : 2009 |
| Genre | : Technology & Engineering |
| ISBN | : 9812834753 |
This book covers the most attractive problem in robot control, dealing with the direct interaction between a robot and a dynamic environment, including the human-robot physical interaction. It provides comprehensive theoretical and experimental coverage of interaction control problems, starting from the mathematical modeling of robots interacting with complex dynamic environments, and proceeding to various concepts for interaction control design and implementation algorithms at different control layers. Focusing on the learning principle, it also shows the application of new and advanced learning algorithms for robotic contact tasks.The ultimate aim is to strike a good balance between the necessary theoretical framework and theoretical aspects of interactive robots.
| Author | : Lucian Bușoniu |
| Publisher | : Springer |
| Total Pages | : 407 |
| Release | : 2016-02-06 |
| Genre | : Technology & Engineering |
| ISBN | : 3319263277 |
This book focuses on two challenges posed in robot control by the increasing adoption of robots in the everyday human environment: uncertainty and networked communication. Part I of the book describes learning control to address environmental uncertainty. Part II discusses state estimation, active sensing, and complex scenario perception to tackle sensing uncertainty. Part III completes the book with control of networked robots and multi-robot teams. Each chapter features in-depth technical coverage and case studies highlighting the applicability of the techniques, with real robots or in simulation. Platforms include mobile ground, aerial, and underwater robots, as well as humanoid robots and robot arms. Source code and experimental data are available at http://extras.springer.com. The text gathers contributions from academic and industry experts, and offers a valuable resource for researchers or graduate students in robot control and perception. It also benefits researchers in related areas, such as computer vision, nonlinear and learning control, and multi-agent systems.
| Author | : Wilko Schwarting |
| Publisher | : |
| Total Pages | : 235 |
| Release | : 2021 |
| Genre | : |
| ISBN | : |
Autonomous robots will soon be a commonplace presence in our daily lives in environments such as homes, factories, and roads. In order to reap the tremendous benefits that these robots offer to society, we must ensure that they can interact with humans seamlessly and safely. In this dissertation, we study intelligent agents that learn how to reason about human behavior and people's intentions. These agents predict others' intentions and implicitly communicate their own intentions through human-like actions that can be understood by people. They also anticipate and leverage the effect of their actions on the actions of others in the environment. When their own interests and the interests of others are not aligned, the agents quantify people's willingness to cooperate or defect and negotiate through social behavior. The agents form beliefs by perceiving the world and the actions of others. They create plans to actively gather information about themselves, others, and the environment, while simultaneously avoiding actions that lead to high uncertainty. They also reason about the beliefs of others, and can leverage how their actions influence others' beliefs. In part (I) of this thesis, we formulate social human-robot interactions between agents as a best-response game wherein each agent negotiates to maximize their utility, and learn human rewards from data. We measure Social Value Orientation (SVO) to quantify an agent's degree of selfishness or altruism to better predict human behavior. In part (II) we additionally enable agents to leverage information gain and reasoning about the beliefs of others in stochastic environments with partial observations by combining game-theoretic and belief-space planning. In part (III) we present a multi-agent reinforcement learning algorithm that learns competitive visual control policies through self-play in imagination. The agent learns from competition by imagining multi-agent interaction sequences in the compact latent space of a learned world model that combines a joint transition function with opponent viewpoint prediction. Lastly, in part (IV) we introduce Parallel Autonomy, a Guardian system that uses uncertain predictions to provide safety in challenging driving scenarios while following people's desired actions as close as safely possible.
| Author | : Chang Liu |
| Publisher | : |
| Total Pages | : 154 |
| Release | : 2017 |
| Genre | : |
| ISBN | : |
Recent progress in robotic systems has significantly advanced robot functional capabilities, including perception, planning, and control. As robots are gaining wider applications in our society, they have started entering our workplace and interacting with us. This leads to new challenges for robots: they are expected to not only be more functionally capable automatic machines, but also become human-compatible, which requires robots to make themselves competent agents to work for people and collaborative partners to work with people on diverse tasks. The capability to planning under uncertainty lies at the core to achieving this goal. The aim of this dissertation is to develop new approaches that improve the autonomy and intelligence of robots to enable them to reliably work for and with people. Especially, this dissertation investigates uncertainty reduction and the planning under various types of uncertainty with the focus on three related topics, including distributed filtering, informative path planning, and planning for human-robot interaction. In the first topic, the dissertation studies uncertainty reduction via distributed filtering using networked robots. We consider the distributed version of the generic Bayesian filter. Two new methods of measurement exchange among networked robots are proposed, which enable the dissemination of robots' sensor measurements in time-invariant and time-variant communication networks. By using such methods, the communication burden of the robot network can be significantly reduced compared to traditionally used methods. Based on these measurement exchange methods, we develop two distributed Bayesian filters for time-invariant and time-variant networks. It has been proved that the proposed distributed Bayesian filter can achieve consistent estimation. The application in target localization and tracking is presented. In the second part, the dissertation focuses on planning under the uncertainty of target position and motion model. This part investigates the path planning of a mobile robot to autonomously search and localize/track a static/moving target. We first study the case of linear Gaussian sensing and mobility models. A path planning approach based on model predictive control (MPC) is proposed, which uses a modified Kalman filter for uncertainty prediction and a sequential planning strategy for path generation. We then investigate the path planning in a dynamic environment, with the sensor using a binary model. A closed-form objective function for the MPC-based path planner is proposed, which significantly reduces the computational complexity. The safety of robot is enforced by using a barrier function in the objective function of MPC. The first two topics concentrate on making robots autonomously work for people. In the third topic, the dissertation addresses the demands to make robots work with people and achieve coordination. We first consider the planning of robots under the uncertainty of humans' trajectory in a human-following application, where the robot needs to generate a path to follow a person in a safe and comfortable way. We propose a model-based human motion prediction approach using the principle of interacting multiple model estimation. A path planner based on nonlinear MPC is then developed for the robot to generate human-following paths, which takes into account the safety and comfort of the accompanied person. We then investigate the planning of robots under the uncertainty of humans' internal states, including their intention and belief. Especially, the task planning in the human-robot collaboration is considered. We develop an adaptive task planning scheme that allows a robot to use motion-level inference to understand a human partner's plan and then adjust its task-level plan to coordinate with the person. In addition, we model a person's inference process and develop a task planning approach for a robot to generate human-predictable plans, which aims to reduce the misalignment between people's belief and robots' plan.
| Author | : Xuefeng Zhou |
| Publisher | : |
| Total Pages | : 138 |
| Release | : 2020-10-09 |
| Genre | : Computers |
| ISBN | : 9781013277504 |
This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors' papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
