Download or read book Sampling based Robot Task and Motion Planning in the Real World written by Caelan Reed Garrett and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We seek to program a robot to autonomously complete complex tasks in a variety of real-world settings involving different environments, objects, manipulation skills, degrees of observability, initial states, and goal objectives. In order to successfully generalize across these settings, we take a model-based approach to building the robot's policy, which enables it to reason about the effects of it executing different sequences of parameterized manipulation skills. Specifically, we introduce a general-purpose hybrid planning framework that uses streams, modules that encode sampling procedures, to generate continuous parameter-value candidates. We present several domain-independent algorithms that efficiently combine streams in order to solve for parameter values that jointly satisfy the constraints necessary for a sequence of skills to achieve the goal. Each stream can be either engineered to perform a standard robotics subroutine, like inverse kinematics and collision checking, or learned from data to capture difficult-to-model behaviors, such as pouring, scooping, and grasping. Streams are also able to represent probabilistic inference operations, which enables our framework to plan in belief space and intentionally select actions that reduce the robot's uncertainty about the unknown world. Throughout this thesis, we demonstrate the generality of our approach by applying it to several real-world tabletop, kitchen, and construction tasks and show that it can even be effective in settings involving objects that the robot has never seen before.
Download or read book Robot Motion Planning written by Jean-Claude Latombe and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the ultimate goals in Robotics is to create autonomous robots. Such robots will accept high-level descriptions of tasks and will execute them without further human intervention. The input descriptions will specify what the user wants done rather than how to do it. The robots will be any kind of versatile mechanical device equipped with actuators and sensors under the control of a computing system. Making progress toward autonomous robots is of major practical inter est in a wide variety of application domains including manufacturing, construction, waste management, space exploration, undersea work, as sistance for the disabled, and medical surgery. It is also of great technical interest, especially for Computer Science, because it raises challenging and rich computational issues from which new concepts of broad useful ness are likely to emerge. Developing the technologies necessary for autonomous robots is a formidable undertaking with deep interweaved ramifications in auto mated reasoning, perception and control. It raises many important prob lems. One of them - motion planning - is the central theme of this book. It can be loosely stated as follows: How can a robot decide what motions to perform in order to achieve goal arrangements of physical objects? This capability is eminently necessary since, by definition, a robot accomplishes tasks by moving in the real world. The minimum one would expect from an autonomous robot is the ability to plan its x Preface own motions.
Download or read book Practical Motion Planning in Robotics written by Kamal Gupta and published by Chichester, England ; Toronto : J. Wiley. This book was released on 1998-10-15 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Motion Planning in Robotics Current Approaches and Future Directions Edited by Kamal Gupta Simon Fraser University, Burnaby, Canada Angel P. del Pobil Jaume-l University, Castellon, Spain Designed to bridge the gap between research and industry, Practical Motion Planning in Robotics brings theoretical advances to bear on real-world applications. Capitalizing on recent progress, this comprehensive study emphasizes the practical aspects of techniques for collision detection, obstacle avoidance, path planning and manipulation planning. The broad approach spans both model- and sensor-based motion planning, collision detection and geometric complexity, and future directions. Features include: - Review of state-of-the-art techniques and coverage of the main issues to be considered in the development of motion planners for use in real applications - Focus on gross motion planning for articulated arms enabling robots to perform non-contact tasks with relatively high tolerances plus brief consideration of mobile robots - The use of efficient algorithms to tackle incremental changes in the environment - Illlustration of robot motion planning applications in virtual prototyping and the shipbuilding industry - Demonstration of efficient path planners combining both local and global planning approaches in conjunction with efficient techniques for collision detection and distance computations - International contributions from academia and industry Combining theory and practice, this timely book will appeal to academic researchers and practising engineers in the fields of robotic systems, mechatronics and computer science.
Download or read book Single Query Robot Motion Planning Using Rapidly Exploring Random Trees RRTs written by Jonathan Bagot and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robots moving about in complex environments must be capable of determining and performing difficult motion sequences to accomplish tasks. As the tasks become more complicated, robots with greater dexterity are required. An increase in the number of degrees of freedom and a desire for autonomy in uncertain environments with real-time requirements leaves much room for improvement in the current popular robot motion planning algorithms. In this thesis, state of the art robot motion planning techniques are surveyed. A solution to the general movers problem in the context of motion planning for robots is presented. The proposed robot motion planner solves the general movers problem using a sample-based tree planner combined with an incremental simulator. The robot motion planner is demonstrated both in simulation and the real world. Experiments are conducted and the results analyzed. Based on the results, methods for tuning the robot motion planner to improve the performance are proposed.
Download or read book Planning Algorithms written by Steven M. LaValle and published by Cambridge University Press. This book was released on 2006-05-29 with total page 844 pages. Available in PDF, EPUB and Kindle. Book excerpt: Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations.
Download or read book Experimental Robotics written by Oussama Khatib and published by Springer. This book was released on 2013-08-20 with total page 919 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporating papers from the 12th International Symposium on Experimental Robotics (ISER), December 2010, this book examines the latest advances across the various fields of robotics. Offers insights on both theoretical concepts and experimental results.
Download or read book Motion Planning for Humanoid Robots written by Kensuke Harada and published by Springer Science & Business Media. This book was released on 2010-08-12 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on humanoid robots has been mostly with the aim of developing robots that can replace humans in the performance of certain tasks. Motion planning for these robots can be quite difficult, due to their complex kinematics, dynamics and environment. It is consequently one of the key research topics in humanoid robotics research and the last few years have witnessed considerable progress in the field. Motion Planning for Humanoid Robots surveys the remarkable recent advancement in both the theoretical and the practical aspects of humanoid motion planning. Various motion planning frameworks are presented in Motion Planning for Humanoid Robots, including one for skill coordination and learning, and one for manipulating and grasping tasks. The problem of planning sequences of contacts that support acyclic motion in a highly constrained environment is addressed and a motion planner that enables a humanoid robot to push an object to a desired location on a cluttered table is described. The main areas of interest include: • whole body motion planning, • task planning, • biped gait planning, and • sensor feedback for motion planning. Torque-level control of multi-contact behavior, autonomous manipulation of moving obstacles, and movement control and planning architecture are also covered. Motion Planning for Humanoid Robots will help readers to understand the current research on humanoid motion planning. It is written for industrial engineers, advanced undergraduate and postgraduate students.
Download or read book A Survey on the Integration of Machine Learning with Sampling based Motion Planning Introduction 2 Sampling based Motion Planning 3 Learning Primitives of Sampling based Motion Planning 4 Learning based Pipelines 5 SBMP with Learned Models 6 Discussion References written by Troy McMahon and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motion planning is the problem of finding valid paths, expressed as sequences of configurations, or trajectories, expressed as sequences of controls, which move a robot from a given start state to a desired goal state while avoiding obstacles. Sampling-based methods are widely adopted solutions for robot motion planning. The methods are straightforward to implement, and effective in practice for many robotic systems. Furthermore, they have numerous desirable properties, such as probabilistic completeness and asymptotic optimality. Nevertheless, sampling-based methods still face challenges as the complexity of the underlying planning problem increases, especially under tight computation time constraints, which impact the quality of returned solutions or given inaccurate models. This has motivated machine learning to improve the computational efficiency and applicability of Sampling-Based Motion Planners (SBMPs).There are numerous publications on the use of machine learning algorithms to improve the efficiency of robotic systems in general. Recently, attention has focussed on the progress of deep learning methods, which has resulted in many efforts to utilize the corresponding tools in robotics. This monograph focuses specifically on integrating machine learning tools to improve the efficiency, convergence, and applicability of SBMPs. The publication covers a wide range of robotic applications, including, but not limited to, manipulation planning, and planning for systems with dynamic constraints. In particular, this manuscript first reviews the attempts to use machine learning to improve the performance of individual primitives used by SBMPs. It also studies a series of planners that use machine learning to adaptively select from a set of motion planning primitives. The monograph then proceeds to study a series of integrated architectures that learn an end-to-end mapping of sensor inputs to robot trajectories or controls. Finally, the monograph shows how SBMPs can operate over learned models of robotic system due to the presence of noise and uncertainty, and it concludes with a comparative discussion of the different approaches covered in terms of their impact on computational efficiency of the planner, quality of the computed paths as well as the usability of SBMPs. Also outlined are the broad difficulties and limitations of these methods, as well as potential directions of future work.
Download or read book Sampling Based Methods for Motion Planning with Constraints written by Zachary Kingston and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robots with many degrees of freedom (e.g., humanoid robots and mobile manipulators) have increasingly been employed to accomplish realistic tasks in domains such as disaster relief, spacecraft logistics, and home caretaking. Finding feasible motions for these robots autonomously is essential for their operation. Sampling-based motion planning algorithms are effective for these high-dimensional systems; however, incorporating task constraints (e.g., keeping a cup level or writing on a board) into the planning process introduces significant challenges. This survey describes the families of methods for sampling-based planning with constraints and places them on a spectrum delineated by their complexity. Constrained sampling-based methods are based on two core primitive operations: ( a) sampling constraint-satisfying configurations and ( b) generating constraint-satisfying continuous motion. Although this article presents the basics of sampling-based planning for contextual background, it focuses on the representation of constraints and sampling-based planners that incorporate constraints.
Download or read book Sensing Intelligence Motion written by Vladimir J. Lumelsky and published by John Wiley & Sons. This book was released on 2005-11-28 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: A leap forward in the field of robotics Until now, most of the advances in robotics have taken place instructured environments. Scientists and engineers have designedhighly sophisticated robots, but most are still only able tooperate and move in predetermined, planned environments designedspecifically for the robots and typically at very high cost. Thisnew book takes robotics to the next level by setting forth thetheory and techniques needed to achieve robotic motion inunstructured environments. The ability to move and operate in anarbitrary, unplanned environment will lead to automating a widerange of new robotic tasks, such as patient care, toxic sitecleanup, and planetary exploration. The approach that opens the door for robots to handle unstructuredtasks is known as Sensing-Intelligence-Motion (SIM), which drawsfrom research in topology, computational complexity, controltheory, and sensing hardware. Using SIM as an underlyingfoundation, the author's carefully structured presentation isdesigned to: * Formulate the challenges of sensor-based motion planning and thenbuild a theoretical foundation for sensor-based motion planningstrategies * Investigate promising algorithmic strategies for mobile robotsand robot arm manipulators, in both cases addressing motionplanning for the whole robot body * Compare robot performance to human performance in sensor-basedmotion planning to gain better insight into the challenges of SIMand help build synergistic human-robot teams for tele-operationtasks. It is both exciting and encouraging to discover that robotperformance decisively exceeds human performance in certain tasksrequiring spatial reasoning, even when compared to trainedoperators * Review sensing hardware that is necessary to realize the SIMparadigm Some 200 illustrations, graphic sketches, and photos are includedto clarify key issues, develop and validate motion planningapproaches, and demonstrate full systems in operation. As the first book fully devoted to robot motion planning inunstructured environments, Sensing, Intelligence, Motion is amust-read for engineers, scientists, and researchers involved inrobotics. It will help them migrate robots from highly specializedapplications in factories to widespread use in society whereautonomous robot motion is needed.
Download or read book Probabilistic Motion Planning and Optimization Incorporating Chance Constraints written by Siyu Dai (S.M.) and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: For high-dimensional robots, motion planning is still a challenging problem, especially for manipulators mounted to underwater vehicles or human support robots where uncertainties and risks of plan failure can have severe impact. However, existing risk-aware planners mostly focus on low-dimensional planning tasks, meanwhile planners that can account for uncertainties and react fast in high degree-of-freedom (DOF) robot planning tasks are lacking. In this thesis, a risk-aware motion planning and execution system called Probabilistic Chekov (p-Chekov) is introduced, which includes a deterministic stage and a risk-aware stage. A systematic set of experiments on existing motion planners as well as p-Chekov is also presented. The deterministic stage of p-Chekov leverages the recent advances in obstacle-aware trajectory optimization to improve the original tube-based-roadmap Chekov planner. Through experiments in 4 common application scenarios with 5000 test cases each, we show that using sampling-based planners alone on high DOF robots can not achieve a high enough reaction speed, whereas the popular trajectory optimizer TrajOpt with naive straight-line seed trajectories has very high collision rate despite its high planning speed. To the best of our knowledge, this is the first work that presents such a systematic and comprehensive evaluation of state-of-the-art motion planners, which are based on a significant amount of experiments. We then combine different stand-alone planners with trajectory optimization. The results show that the deterministic planning part of p-Chekov, which combines a roadmap approach that caches the all pair shortest paths solutions and an online obstacle-aware trajectory optimizer, provides superior performance over other standard sampling-based planners' combinations. Simulation results show that, in typical real-life applications, this "roadmap + TrajOpt" approach takes about 1 s to plan and the failure rate of its solutions is under 1%. The risk-aware stage of p-Chekov accounts for chance constraints through state probability distribution and collision probability estimation. Based on the deterministic Chekov planner, p-Chekov incorporates a linear-quadratic Gaussian motion planning (LQG-MP) approach into robot state probability distribution estimation, applies quadrature-sampling theories to collision risk estimation, and adapts risk allocation approaches for chance constraint satisfaction. It overcomes existing risk-aware planners' limitation in real-time motion planning tasks with high-DOF robots in 3- dimensional non-convex environments. The experimental results in this thesis show that this new risk-aware motion planning and execution system can effectively reduce collision risk and satisfy chance constraints in typical real-world planning scenarios for high-DOF robots. This thesis makes the following three main contributions: (1) a systematic evaluation of several state-of-the-art motion planners in realistic planning scenarios, including popular sampling-based motion planners and trajectory optimization type motion planners, (2) the establishment of a "roadmap + TrajOpt" deterministic motion planning system that shows superior performance in many practical planning tasks in terms of solution feasibility, optimality and reaction time, and (3) the development of a risk-aware motion planning and execution system that can handle high-DOF robotic planning tasks in 3-dimensional non-convex environments.
Download or read book Algorithmic Foundations of Robotics XIV written by Steven M. LaValle and published by Springer Nature. This book was released on 2021-02-08 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: This proceedings book helps bring insights from this array of technical sub-topics together, as advanced robot algorithms draw on the combined expertise of many fields—including control theory, computational geometry and topology, geometrical and physical modeling, reasoning under uncertainty, probabilistic algorithms, game theory, and theoretical computer science. Intelligent robots and autonomous systems depend on algorithms that efficiently realize functionalities ranging from perception to decision making, from motion planning to control. The works collected in this SPAR book represent the state of the art in algorithmic robotics. They originate from papers accepted to the 14th International Workshop on the Algorithmic Foundations of Robotics (WAFR), traditionally a biannual, single-track meeting of leading researchers in the field of robotics. WAFR has always served as a premiere venue for the publication of some of robotics’ most important, fundamental, and lasting algorithmic contributions, ensuring the rapid circulation of new ideas. Though an in-person meeting was planned for June 15–17, 2020, in Oulu, Finland, the event ended up being canceled owing to the infeasibility of international travel during the global COVID-19 crisis.
Download or read book Determinantal Point Processes for Machine Learning written by Alex Kulesza and published by Now Pub. This book was released on 2012-11-29 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides a comprehensible introduction to DPPs, focusing on the intuitions, algorithms, and extensions that are most relevant to the machine learning community.
Download or read book Adaptive State Time Lattices A Contribution to Mobile Robot Motion Planning in Unstructured Dynamic Environments written by Petereit, Janko and published by KIT Scientific Publishing. This book was released on 2017-01-20 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile robot motion planning in unstructured dynamic environments is a challenging task. Thus, often suboptimal methods are employed which perform global path planning and local obstacle avoidance separately. This work introduces a holistic planning algorithm which is based on the concept of state.
Download or read book Generalizable Robot Manipulation Through Task and Motion Planning and Interactive Perception written by Xiaolin Fang (Researcher in electrical engineering and computer science) and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For a robot operating in a daily household environment, generality is of great importance. It should be able to generalize to different tasks that involve different objects in varying backgrounds and configurations. In this thesis, we will move towards this goal from two perspectives. We will first present a strategy for designing a robot manipulation system that can generalize to a wide range of goals, environments, and objects. Such generality is achieved through task and motion planning with affordances estimated by both learned and engineered modules. We demonstrate that this strategy can enable a single policy to perform a wide variety of real-world manipulation tasks. Next, we will present an interactive perception solution to deal with the uncertainty in the estimated affordances, with a focus on the segmentation of objects. We adopt an object-based belief representation to estimate the uncertainty coming from predicted segmentation, and select actions to reduce that efficiently. Our experiments show that our system can generalize better to different environments and reduce uncertainty more efficiently compared to our baselines.
Download or read book Motion Planning of Mobile Robot in Dynamic Environment Using Potential Field and Roadmap Based Planner written by Waqar Ahmad Malik and published by . This book was released on 2003 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Mobile robots are increasingly being used to perform tasks in unknown environments. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently locate and interact with objects in their environment. My research focuses on developing algorithms to plan paths for mobile robots in a partially known environment observed by an overhead camera. The environment consists of dynamic obstacles and targets. A new methodology, Extrapolated Artificial Potential Field, is proposed for real time robot path planning. An algorithm for probabilistic collision detection and avoidance is used to enhance the planner. The aim of the robot is to select avoidance maneuvers to avoid the dynamic obstacles. The navigation of a mobile robot in a real-world dynamic environment is a complex and daunting task. Consider the case of a mobile robot working in an office environment. It has to avoid the static obstacles such as desks, chairs and cupboards and it also has to consider dynamic obstacles such as humans. In the presence of dynamic obstacles, the robot has to predict the motion of the obstacles. Humans inherently have an intuitive motion prediction scheme when planning a path in a crowded environment. A technique has been developed which predicts the possible future positions of obstacles. This technique coupled with the generalized Voronoi diagram enables the robot to safely navigate in a given environment.
Download or read book Automated Planning and Acting written by Malik Ghallab and published by Cambridge University Press. This book was released on 2016-08-09 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the most recent and advanced techniques for creating autonomous AI systems capable of planning and acting effectively.
