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 A Survey on the Integration of Machine Learning with Sampling based Motion Planning written by Troy McMahon and published by . This book was released on 2022-11-10 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 Motion Planning written by Roland Jan Geraerts and published by . This book was released on 2006 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Machine Learning for Robot Motion Planning written by Clark Zhang and published by . This book was released on 2021 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robot motion planning is a field that encompasses many different problems and algorithms. From the traditional piano mover’s problem to more complicated kinodynamic planning problems, motion planning requires a broad breadth of human expertise and time to design well functioning algorithms. A traditional motion planning pipeline consists of modeling a system and then designing a planner and planning heuristics. Each part of this pipeline can incorporate machine learning. Planners and planning heuristics can benefit from machine learned heuristics, while system modeling can benefit from model learning. Each aspect of the motion planning pipeline comes with trade offs between computational effort and human effort. This work explores algorithms that allow motion planning algorithms and frameworks to find a compromise between the two. First, a framework for learning heuristics for sampling-based planners is presented. The efficacy of the framework depends on human designed features and policy architecture. Next, a framework for learning system models is presented that incorporates human knowledge as constraints. The amount of human effort can be modulated by the quality of the constraints given. Lastly, semi-automatic constraint generation is explored to enable a larger range of trade-offs between human expert constraint generation and data driven constraint generation. We apply these techniques and show results in a variety of robotic systems.
Download or read book Sampling based Motion Planning Algorithms Analysis and Development written by Nathan Alexander Wedge and published by . This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robotic motion planning, which concerns the computation of paths and controls that drive an autonomous agent from one configuration to another, is quickly becoming a vitally important field of research as its applications diversify and become increasingly public. Many algorithms have been proposed to deal with this central problem; sampling-based approaches like the Rapidly-exploring Random Tree (RRT) and Probabilistic Roadmap Method (PRM) planners are among the most successful. Still, these algorithms are not fully understood and suffer from pathologically poorly-performing instances resulting from the contributions of random sampling and qualitative obstacle features like narrow passages. The large means and variances that result from these issues continue to motivate the development of new algorithms and adaptations to increase consistency and to allow more difficult problems to be solved. This research examines these performance issues with a focus on the Rapidly-exploring Random Tree (RRT) planner. Fundamental analysis establishes that the interaction of its Voronoi bias with particular obstacle features can compromise its efficacy and illustrates the types of distributions on its performance that result. It further provides guidance on the types of problems amenable to solutions by the algorithm and on the use of its alternative EXTEND and CONNECT heuristics and step size parameter. Observations from this analysis prompt an investigation of the use of restart strategies to manage issues of both scaling in computation and exploratory missteps. In turn, their impact provides a foundation for the introduction of a novel algorithm, the Path-length Annexed Random Tree (PART) planner, that directs its exploration on a local basis. This algorithm and its environment-adaptive successor, the Adaptive PART (APART) planner, demonstrate competitive performance on instructive examples and dramatic improvements on difficult benchmarks, while also supplementing their utility with the output of a connected roadmap.
Download or read book Sampling based Motion Planning with Differential Constraints written by Peng Cheng and published by . This book was released on 2005 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sampling based Motion Planning Algorithms for Dynamical Systems written by and published by . This book was released on 2015 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dynamical systems bring further challenges to the problem of motion planning, by additionally complicating the computation of collision-free paths with collision-free dynamic motions. This dissertation proposes efficient approaches for the optimal sampling-based motion planning algorithms, with a strong emphasis on the accommodation of realistic dynamical systems as the subject of motion planning. The main contribution of the dissertation is twofold: advances in general framework for asymptotically-optimal sampling-based algorithms, and the development of fast algorithmic components for certain classes of dynamical systems. The first part of the dissertation begins with key ideas from a number of recent sampling-based algorithms toward fast convergence rates. We reinterpret the ideas in the context of incremental algorithms, and integrate the key ingredients within the strict [omicron](log n) complexity per iteration, which we refer to as the enhanced RRT* algorithm. Subsequently, Goal-Rooted Feedback Motion Trees (GR-FMTs) are presented as an adaptation of sampling-based algorithms into the context of asymptotically-optimal feedback motion planning or replanning. Last but not least, we propose a loop of collective operations, or an efficient loop with cost-informed operations, which minimizes the exposure to the main challenges incurred by dynamical systems, i.e., steering problems or Two-Point Boundary Value Problems (TPBVPs). The second main part of the dissertation directly deals with the steering problems for three categories of dynamical systems. First, we propose a numerical TPBVP method for a general class of dynamical systems, including time-optimal off-road vehicle maneuvers as the main example. Second, we propose a semi-analytic TPBVP approach for differentially flat systems or partially flat systems, by which the computation of vehicle maneuvers is expedited and the capability to handle extreme scenarios is greatly enhanced. Third, we propose an efficient TPBVP algorithm for controllable linear systems, based on the computation of small-sized linear or quadratic programming problems in a progressive and incremental manner. Overall, the main contribution in this dissertation realizes the outcome of anytime algorithms for optimal motion planning problems. An initial solution is obtained within a small time, and the solution is further improved toward the optimal one. To our best knowledge from both simulation results and algorithm analyses, the proposed algorithms supposedly outperform or run at least as fast as other state-of-the-art sampling-based algorithms.
Download or read book Machine Learning for Vision Based Motion Analysis written by Liang Wang and published by Springer. This book was released on 2010-11-23 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Techniques of vision-based motion analysis aim to detect, track, identify, and generally understand the behavior of objects in image sequences. With the growth of video data in a wide range of applications from visual surveillance to human-machine interfaces, the ability to automatically analyze and understand object motions from video footage is of increasing importance. Among the latest developments in this field is the application of statistical machine learning algorithms for object tracking, activity modeling, and recognition. Developed from expert contributions to the first and second International Workshop on Machine Learning for Vision-Based Motion Analysis, this important text/reference highlights the latest algorithms and systems for robust and effective vision-based motion understanding from a machine learning perspective. Highlighting the benefits of collaboration between the communities of object motion understanding and machine learning, the book discusses the most active forefronts of research, including current challenges and potential future directions. Topics and features: provides a comprehensive review of the latest developments in vision-based motion analysis, presenting numerous case studies on state-of-the-art learning algorithms; examines algorithms for clustering and segmentation, and manifold learning for dynamical models; describes the theory behind mixed-state statistical models, with a focus on mixed-state Markov models that take into account spatial and temporal interaction; discusses object tracking in surveillance image streams, discriminative multiple target tracking, and guidewire tracking in fluoroscopy; explores issues of modeling for saliency detection, human gait modeling, modeling of extremely crowded scenes, and behavior modeling from video surveillance data; investigates methods for automatic recognition of gestures in Sign Language, and human action recognition from small training sets. Researchers, professional engineers, and graduate students in computer vision, pattern recognition and machine learning, will all find this text an accessible survey of machine learning techniques for vision-based motion analysis. The book will also be of interest to all who work with specific vision applications, such as surveillance, sport event analysis, healthcare, video conferencing, and motion video indexing and retrieval.
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.
Download or read book A Scalable Framework for Parallelizing Sampling Based Motion Planning Algorithms written by Samson Ade Jacobs and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Motion planning is defined as the problem of finding a valid path taking a robot (or any movable object) from a given start configuration to a goal configuration in an environment. While motion planning has its roots in robotics, it now finds application in many other areas of scientific computing such as protein folding, drug design, virtual prototyping, computer-aided design (CAD), and computer animation. These new areas test the limits of the best sequential planners available, motivating the need for methods that can exploit parallel processing. This dissertation focuses on the design and implementation of a generic and scalable framework for parallelizing motion planning algorithms. In particular, we focus on sampling-based motion planning algorithms which are considered to be the state-of-the-art. Our work covers the two broad classes of sampling-based motion planning algorithms--the graph-based and the tree-based methods. Central to our approach is the subdivision of the planning space into regions. These regions represent sub- problems that can be processed in parallel. Solutions to the sub-problems are later combined to form a solution to the entire problem. By subdividing the planning space and restricting the locality of connection attempts to adjacent regions, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We also describe how load balancing strategies can be applied in complex environments. We present experimental results that scale to thousands of processors on different massively parallel machines for a range of motion planning problems. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/152806
Download or read book Towards End To End Learning Based Algorithms in Motion Planning written by Yinglong Miao and published by . This book was released on 2020 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motion planning is one of the most critical tasks in robotics, as it is one of the few critical functions for robot autonomy. This component requires fast computations and generalization to different environments for problems such as collision avoidance. Deep learning, as a new fast-growing field, offers great advances in computational speed and generalization. It has shown success in computer vision and reinforcement learning, which is closely related to motion planning. In this thesis, we will investigate the combination of learning and motion planning methods. Specifically, we separately consider individual components of motion planning tasks. By combining the proposed learning-based methods for each component, we can obtain an integrated end-to-end learning-based motion planning algorithm. We show experimental results for each component. In general, our learning-based methods showed high computational speed with generalization in several motion planning tasks.
Download or read book Incremental Learning for Motion Prediction of Pedestrians and Vehicles written by Alejandro Dizan Vasquez Govea and published by Springer Science & Business Media. This book was released on 2010-06-23 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the problem of moving in a cluttered environment with pedestrians and vehicles. A framework based on Hidden Markov models is developed to learn typical motion patterns which can be used to predict motion on the basis of sensor data.
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 Sampling based Motion Planning with 3D Simultaneous Localization and Mapping for Social Aware Service Robotics Applications written by 黃昭霖 and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Integrating Motion Planning Into Reinforcement Learning to Solve Hard Exploration Problems written by Guillaume Matheron and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motion planning is able to solve robotics problems much quicker than any reinforcement learning algorithm by efficiently searching for a viable trajectory. Indeed, while the main object of interest in the field of Reinforcement Learning is the behavior of an agent, Motion Planning is concerned with the geometry and properties of the state-space, and uses a different set of primitives to achieve more efficient exploration. Some of these primitives require a model of the system and are not studied in this work, others such as reset-anywhere are only available in simulated environments. In contrast, Motion Planning approaches do not benefit from the same generalization properties as the policies produced by reinforcement learning. In this thesis, we study the ways in which techniques inspired from motion planning can speed up the solving of hard exploration problems for reinforcement learning without sacrificing the advantages of model-free learning and generalization. We identify a deadlock that can occur when applying reinforcement learning to seemingly-trivial sparse-reward problems, and contribute an exploration algorithm inspired by motion planning but specifically designed for reinforcement learning environments, as well as a framework to use the collected data to train a reinforcement learning algorithm in previously-intractable scenarios.
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.
