Download or read book Real time Sampling based Motion Planning with Dynamic Obstacles written by Kevin Rose and published by . This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Predictive Sampling based Robot Motion Planning in Unmodeled Dynamic Environments written by Javier Matias Ruiz and published by . This book was released on 2019 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis describes a predictive sampling-based algorithm for real-time robot motion planning to reach dynamic goals. The planner utilizes all available information about future obstacle and goal positions over a time window to select a path that approximately minimizes the time to reach this goal. Then, we integrate the proposed method with an online learning algorithm that predicts future goal and obstacle positions. Because future states are predicted by propagation, an incorrect model would result into a greater prediction error for large horizons. Thus, using a pool of candidate models, we utilize a Multiple Model Adaptive Estimation~(MMAE) method with online parameter estimation to learn an appropriate model that keeps this error bounded. Several simulations show the efficacy of the proposed algorithms.
Download or read book Massive Parallelism and Sampling Strategies for Robust and Real time Robotic Motion Planning written by Brian Ichter and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Motion planning is a fundamental problem in robotics, whereby one seeks to compute a low-cost trajectory from an initial state to a goal region that avoids any obstacles. Sampling-based motion planning algorithms have emerged as an effective paradigm for planning with complex, high-dimensional robotic systems. These algorithms maintain only an implicit representation of the state space, constructed by sampling the free state space and locally connecting samples (under the supervision of a collision checking module). This thesis presents approaches towards enabling real-time and robust sampling-based motion planning with improved sampling strategies and massive parallelism. In the first part of this thesis, we discuss algorithms to leverage massively parallel hardware (GPUs) to accelerate planning and to consider robustness during the planning process. We present an algorithm capable of planning at rates amenable to application within control loops, ∼10 ms. This algorithm uses approximate dynamic programming to explore the state space in a massively-parallel, near-optimal manner. We further present two algorithms capable of real-time, uncertainty-aware and perception-aware motion planning that exhaustively explore the state space via a multiobjective search. This search identifies a Pareto set of promising paths (in terms of cost and robustness) and certifies their robustness via Monte Carlo methods. We demonstrate the effectiveness of these algorithm in numerical simulations and a physical experiment on a quadrotor. In the second part of this thesis, we examine sampling-strategies for probing the state space; traditionally this has been uniform, independent, and identically distributed (i.i.d.) random points. We present a methodology for biasing the sample distribution towards regions of the state space in which the solution trajectory is likely to lie. This distribution is learned via a conditional variational autoencoder, allowing a general methodology, which can be used in combination with any sampling- based planner and can effectively exploit the underlying structure of a planning problem while maintaining the theoretical guarantees of sampling-based approaches. We also analyze the use of deterministic, low-dispersion samples instead of i.i.d. random points. We show that this allows deterministic asymptotic optimality (as opposed to probabilistic), a convergence rate bound in terms of the sample dispersion, reduced computational complexity, and improved practical performance. The technical approaches in this work are applicable to general robotic systems and lay the foundations of robustness and algorithmic speed required for robotic systems operating in the world.
Download or read book Robust Sampling based Motion Planning for Autonomous Vehicles in Uncertain Environments written by Brandon Douglas Luders and published by . This book was released on 2014 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: While navigating, autonomous vehicles often must overcome significant uncertainty in their understanding of the world around them. Real-world environments may be cluttered and highly dynamic, with uncertainty in both the current state and future evolution of environmental constraints. The vehicle may also face uncertainty in its own motion. To provide safe navigation under such conditions, motion planning algorithms must be able to rapidly generate smooth, certifiably robust trajectories in real-time. The primary contribution of this thesis is the development of a real-time motion planning framework capable of generating feasible paths for autonomous vehicles in complex environments, with robustness guarantees under both internal and external uncertainty. By leveraging the trajectory-wise constraint checking of sampling-based algorithms, and in particular rapidly-exploring random trees (RRT), the proposed algorithms can efficiently evaluate and enforce complex robustness conditions. For linear systems under bounded uncertainty, a sampling-based motion planner is presented which iteratively tightens constraints in order to guarantee safety for all feasible uncertainty realizations. The proposed bounded-uncertainty RRT* (BURRT*) algorithm scales favorably with environment complexity. Additionally, by building upon RRT*, BU-RRT* is shown to be asymptotically optimal, enabling it to efficiently generate and optimize robust, dynamically feasible trajectories. For large and/or unbounded uncertainties, probabilistically feasible planning is provided through the proposed chance-constrained RRT (CC-RRT) algorithm. Paths generated by CC-RRT are guaranteed probabilistically feasible for linear systems under Gaussian uncertainty, with extensions considered for nonlinear dynamics, output models, and/or non-Gaussian uncertainty. Probabilistic constraint satisfaction is represented in terms of chance constraints, extending existing approaches by considering both internal and external uncertainty, subject to time-step-wise and path-wise feasibility constraints. An explicit bound on the total risk of constraint violation is developed which can be efficiently evaluated online for each trajectory. The proposed CC-RRT* algorithm extends this approach to provide asymptotic optimality guarantees; an admissible risk-based objective uses the risk bounds to incentivize risk-averse trajectories. Applications of this framework are shown for several motion planning domains, including parafoil terminal guidance and urban navigation, where the system is subject to challenging environmental and uncertainty characterizations. Hardware results demonstrate a mobile robot utilizing this framework to safely avoid dynamic obstacles.
Download or read book Generalized Sampling based Feedback Motion Planners written by Sandip Kumar and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The motion planning problem can be formulated as a Markov decision process (MDP), if the uncertainties in the robot motion and environments can be modeled probabilistically. The complexity of solving these MDPs grow exponentially as the dimension of the problem increases and hence, it is nearly impossible to solve the problem even without constraints. Using hierarchical methods, these MDPs can be transformed into a semi-Markov decision process (SMDP) which only needs to be solved at certain landmark states. In the deterministic robotics motion planning community, sampling based algorithms like probabilistic roadmaps (PRM) and rapidly exploring random trees (RRTs) have been successful in solving very high dimensional deterministic problem. However they are not robust to system with uncertainties in the system dynamics and hence, one of the primary objective of this work is to generalize PRM/RRT to solve motion planning with uncertainty. We first present generalizations of randomized sampling based algorithms PRM and RRT, to incorporate the process uncertainty, and obstacle location uncertainty, termed as "generalized PRM" (GPRM) and "generalized RRT" (GRRT). The controllers used at the lower level of these planners are feedback controllers which ensure convergence of trajectories while mitigating the effects of process uncertainty. The results indicate that the algorithms solve the motion planning problem for a single agent in continuous state/control spaces in the presence of process uncertainty, and constraints such as obstacles and other state/input constraints. Secondly, a novel adaptive sampling technique, termed as "adaptive GPRM" (AGPRM), is proposed for these generalized planners to increase the efficiency and overall success probability of these planners. It was implemented on high-dimensional robot n-link manipulators, with up to 8 links, i.e. in a 16-dimensional state-space. The results demonstrate the ability of the proposed algorithm to handle the motion planning problem for highly non-linear systems in very high-dimensional state space. Finally, a solution methodology, termed the "multi-agent AGPRM" (MAGPRM), is proposed to solve the multi-agent motion planning problem under uncertainty. The technique uses a existing solution technique to the multiple traveling salesman problem (MTSP) in conjunction with GPRM. For real-time implementation, an "inter-agent collision detection and avoidance" module was designed which ensures that no two agents collide at any time-step. Algorithm was tested on teams of homogeneous and heterogeneous agents in cluttered obstacle space and the algorithm demonstrate the ability to handle such problems in continuous state/control spaces in presence of process uncertainty.
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 Motion Planning in Dynamic Environments written by Kikuo Fujimura and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer Science Workbench is a monograph series which will provide you with an in-depth working knowledge of current developments in computer technology. Every volume in this series will deal with a topic of importance in computer science and elaborate on how you yourself can build systems related to the main theme. You will be able to develop a variety of systems, including computer software tools, computer graphics, computer animation, database management systems, and computer-aided design and manufacturing systems. Computer Science Workbench represents an important new contribution in the field of practical computer technology. TOSIYASU L. KUNII To my parents Kenjiro and Nori Fujimura Preface Motion planning is an area in robotics that has received much attention recently. Much of the past research focuses on static environments - various methods have been developed and their characteristics have been well investigated. Although it is essential for autonomous intelligent robots to be able to navigate within dynamic worlds, the problem of motion planning in dynamic domains is relatively little understood compared with static problems.
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 Real Time Collision Detection written by Christer Ericson and published by CRC Press. This book was released on 2004-12-22 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by an expert in the game industry, Christer Ericson's new book is a comprehensive guide to the components of efficient real-time collision detection systems. The book provides the tools and know-how needed to implement industrial-strength collision detection for the highly detailed dynamic environments of applications such as 3D games, virt
Download or read book The Complexity of Robot Motion Planning written by John Canny and published by MIT Press. This book was released on 1988 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Complexity of Robot Motion Planning makes original contributions both to roboticsand to the analysis of algorithms. In this groundbreaking monograph John Canny resolveslong-standing problems concerning the complexity of motion planning and, for the central problem offinding a collision free path for a jointed robot in the presence of obstacles, obtains exponentialspeedups over existing algorithms by applying high-powered new mathematical techniques.Canny's newalgorithm for this "generalized movers' problem," the most-studied and basic robot motion planningproblem, has a single exponential running time, and is polynomial for any given robot. The algorithmhas an optimal running time exponent and is based on the notion of roadmaps - one-dimensionalsubsets of the robot's configuration space. In deriving the single exponential bound, Cannyintroduces and reveals the power of two tools that have not been previously used in geometricalgorithms: the generalized (multivariable) resultant for a system of polynomials and Whitney'snotion of stratified sets. He has also developed a novel representation of object orientation basedon unnormalized quaternions which reduces the complexity of the algorithms and enhances theirpractical applicability.After dealing with the movers' problem, the book next attacks and derivesseveral lower bounds on extensions of the problem: finding the shortest path among polyhedralobstacles, planning with velocity limits, and compliant motion planning with uncertainty. Itintroduces a clever technique, "path encoding," that allows a proof of NP-hardness for the first twoproblems and then shows that the general form of compliant motion planning, a problem that is thefocus of a great deal of recent work in robotics, is non-deterministic exponential time hard. Cannyproves this result using a highly original construction.John Canny received his doctorate from MITAnd is an assistant professor in the Computer Science Division at the University of California,Berkeley. The Complexity of Robot Motion Planning is the winner of the 1987 ACM DoctoralDissertation Award.
Download or read book Advances in Robot Kinematics 2020 written by Jadran Lenarčič and published by Springer Nature. This book was released on 2020-07-17 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is of interest to researchers wanting to know more about the latest topics and methods in the fields of the kinematics, control and design of robotic systems. The papers cover the full range of robotic systems, including serial, parallel and cable-driven manipulators. The systems range from being less than fully mobile, to kinematically redundant, to over-constrained. The book brings together 43 peer-reviewed papers. They report on the latest scientific and applied achievements. The main theme that connects them is the movement of robots in the most diverse areas of application.
Download or read book Robotics in Smart Manufacturing written by Pedro Neto and published by Springer. This book was released on 2013-06-12 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the International Workshop on Robotics in Smart Manufacturing, WRSM 2013, held in Porto, Portugal, in June 2013. The 20 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers address issues such as robotic machining, off-line robot programming, robot calibration, new robotic hardware and software architectures, advanced robot teaching methods, intelligent warehouses, robot co-workers and application of robots in the textile industry.
Download or read book A Sampling based Model Predictive Control Approach to Motion Planning for Autonomous Underwater Vehicles written by Charmane Venda Caldwell and published by . This book was released on 2011 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: ABSTRACT: In recent years there has been a demand from the commercial, research and military industries to complete tedious and hazardous underwater tasks. This has lead to the use of unmanned vehicles, in particular autonomous underwater vehicles (AUVs). To operate inthis environment the vehicle must display kinematically and dynamically feasible trajectories. Kinematic feasibility is important to allow for the limited turn radius of an AUV, while dynamic feasibility can take into consideration limited acceleration and braking capabilities due to actuator limitations and vehicle inertia. Model Predictive Control (MPC) is a method that has the ability to systematically handle multi-input multi-output (MIMO) control problems subject to constraints. It finds the control input by optimizing a cost function that incorporates a model of the system to predict future outputs subject to the constraints. This makes MPC a candidate method for AUV trajectory generation. However, traditional MPC has difficulties in computing control inputs in real time for processes with fast dynamics. This research applies a novel MPC approach, called Sampling-Based Model Predictive Control (SBMPC), to generate kinematically or dynamically feasible system trajectories for AUVs. The algorithm combines the benefits of sampling-based motion planning with MPC while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC, namely large computation times and local minimum problems. SBMPC is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization method (e.g., A?) in place of standard nonlinear programming. SBMPC can avoid local minimum, has only two parameters to tune, and has small computational times that allows it to be used online fast systems. A kinematic model, decoupled dynamic model and full dynamic model are incorporated in SBMPC to generate a kinematic and dynamic feasible 3D path. Simulation results demonstrate the efficacy of SBMPC in guiding an autonomous underwater vehicle from a start position to a goal position in regions populated with various types of obstacles.
Download or read book Motion Planning written by Edgar A. Martínez García and published by BoD – Books on Demand. This book was released on 2022-01-26 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms.
Download or read book Semantic 3D Object Maps for Everyday Robot Manipulation written by Radu Bogdan Rusu and published by Springer. This book was released on 2013-01-13 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book written by Dr. Radu B. Rusu presents a detailed description of 3D Semantic Mapping in the context of mobile robot manipulation. As autonomous robotic platforms get more sophisticated manipulation capabilities, they also need more expressive and comprehensive environment models that include the objects present in the world, together with their position, form, and other semantic aspects, as well as interpretations of these objects with respect to the robot tasks. The book proposes novel 3D feature representations called Point Feature Histograms (PFH), as well as a frameworks for the acquisition and processing of Semantic 3D Object Maps with contributions to robust registration, fast segmentation into regions, and reliable object detection, categorization, and reconstruction. These contributions have been fully implemented and empirically evaluated on different robotic systems, and have been the original kernel to the widely successful open-source project the Point Cloud Library (PCL) -- see http://pointclouds.org.
Download or read book Motion and Operation Planning of Robotic Systems written by Giuseppe Carbone and published by Springer. This book was released on 2016-10-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the broad multi-disciplinary topic of robotics, and presents the basic techniques for motion and operation planning in robotics systems. Gathering contributions from experts in diverse and wide ranging fields, it offers an overview of the most recent and cutting-edge practical applications of these methodologies. It covers both theoretical and practical approaches, and elucidates the transition from theory to implementation. An extensive analysis is provided, including humanoids, manipulators, aerial robots and ground mobile robots. ‘Motion and Operation Planning of Robotic Systems’ addresses the following topics: *The theoretical background of robotics. *Application of motion planning techniques to manipulators, such as serial and parallel manipulators. *Mobile robots planning, including robotic applications related to aerial robots, large scale robots and traditional wheeled robots. *Motion planning for humanoid robots. An invaluable reference text for graduate students and researchers in robotics, this book is also intended for researchers studying robotics control design, user interfaces, modelling, simulation, sensors, humanoid robotics.
Download or read book Sampling Based Motion Planning Algorithms for Replanning and Spatial Load Balancing written by Beth Leigh Boardman and published by . This book was released on 2017 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: The common theme of this dissertation is sampling-based motion planning with the two key contributions being in the area of replanning and spatial load balancing for robotic systems. Here, we begin by recalling two sampling-based motion planners: the asymptotically optimal rapidly-exploring random tree (RRT*), and the asymptotically optimal probabilistic roadmap (PRM*). We also provide a brief background on collision cones and the Distributed Reactive Collision Avoidance (DRCA) algorithm. The next four chapters detail novel contributions for motion replanning in environments with unexpected static obstacles, for multi-agent collision avoidance, and spatial load balancing. First, we show improved performance of the RRT* when using the proposed Grandparent-Connection (GP) or Focused-Refinement (FR) algorithms. Next, the Goal Tree algorithm for replanning with unexpected static obstacles is detailed and proven to be asymptotically optimal. A multi-agent collision avoidance problem in obstacle environments is approached via the RRT*, leading to the novel Sampling-Based Collision Avoidance (SBCA) algorithm. The SBCA algorithm is proven to guarantee collision free trajectories for all of the agents, even when subject to uncertainties in the knowledge of the other agents' positions and velocities. Given that a solution exists, we prove that livelocks and deadlock will lead to the cost to the goal being decreased. We introduce a new deconfliction maneuver that decreases the cost-to-come at each step. This new maneuver removes the possibility of livelocks and allows a result to be formed that proves convergence to the goal configurations. Finally, we present a limited range Graph-based Spatial Load Balancing (GSLB) algorithm which fairly divides a non-convex space among multiple agents that are subject to differential constraints and have a limited travel distance. The GSLB is proven to converge to a solution when maximizing the area covered by the agents. The analysis for each of the above mentioned algorithms is confirmed in simulations.
