Download or read book Incremental High Quality Probabilistic Roadmap Construction for Robot Path Planning written by Yueqiao Li and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Probabilistic Roadmaps for Path Planning in High dimensional Configuration Spaces written by Lydia Kavraki and published by . This book was released on 1994 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new motion planning method for robots in static workspaces is presented. This method proceeds according to two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and edges to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the considered scenes, that is the robots and their workspaces. But these values turn out to be relatively easy to choose. Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (approximately 150 MIPS), after learning for relatively short periods of time (a few dozen seconds).
Download or read book Probabilistic roadmap based path planning for mobile robots written by and published by . This book was released on 2014 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robot Motion Planning and Control written by Jean-Paul Laumond and published by Springer. This book was released on 1998 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Content Description #Includes bibliographical references.
Download or read book A Probabilistic Roadmap Based Path Planning for Visual Servo of Robotic Manipulators written by Farid Arvani and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Algorithmic Foundation of Robotics VII written by Srinivas Akella and published by Springer Science & Business Media. This book was released on 2008-07-10 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms are a fundamental component of robotic systems: they control or reason about motion and perception in the physical world. They receive input from noisy sensors, consider geometric and physical constraints, and operate on the world through imprecise actuators. The design and analysis of robot algorithms therefore raises a unique combination of questions in control theory, computational and differential geometry, and computer science. This book contains the proceedings from the 2006 Workshop on the Algorithmic Foundations of Robotics. This biannual workshop is a highly selective meeting of leading researchers in the field of algorithmic issues related to robotics. The 32 papers in this book span a wide variety of topics: from fundamental motion planning algorithms to applications in medicine and biology, but they have in common a foundation in the algorithmic problems of robotic systems.
Download or read book Probabilistic Robotics written by Sebastian Thrun and published by MIT Press. This book was released on 2005-08-19 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the techniques and algorithms of the newest field in robotics. Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty. Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.
Download or read book Sampling based Algorithms for Optimal Path Planning Problems written by Sertac Karaman and published by . This book was released on 2012 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sampling-based motion planning received increasing attention during the last decade. In particular, some of the leading paradigms, such the Probabilistic RoadMap (PRM) and the Rapidly-exploring Random Tree (RRT) algorithms, have been demonstrated on several robotic platforms, and found applications well outside the robotics domain. However, a large portion of this research effort has been limited to the classical feasible path planning problem, which asks for finding a path that starts from an initial configuration and reaches a goal configuration while avoiding collision with obstacles. The main contribution of this dissertation is a novel class of algorithms that extend the application domain of sampling-based methods to two new directions: optimal path planning and path planning with complex task specifications. Regarding the optimal path planning problem, we first show that the existing algorithms either lack asymptotic optimality, i. e., almost-sure convergence to optimal solutions, or they lack computational efficiency: on one hand, neither the RRT nor the k-nearest PRM (for any fixed k) is asymptotically optimal; on the other hand, the simple PRM algorithm, where the connections are sought within fixed radius balls, is not computationally as efficient as the RRT or the efficient PRM variants. Subsequently, we propose two novel algorithms, called PRM* and RRT*, both of which guarantee asymptotic optimality without sacrificing computational efficiency. In fact, the proposed algorithms and the most efficient existing algorithms, such as the RRT, have the same asymptotic computational complexity. Regarding the path planning problem with complex task specifications, we propose an incremental sampling-based algorithm that is provably correct and probabilistically complete, i.e., it generates a correct-by-design path that satisfies a given deterministic pt-calculus specification, when such a path exists, with probability approaching to one as the number of samples approaches infinity. For this purpose, we develop two key ingredients. First, we propose an incremental sampling-based algorithm, called the RRG, that generates a representative set of paths in the form of a graph, with guaranteed almost-sure convergence towards feasible paths. Second, we propose an incremental local model-checking algorithm for the deterministic p-calculus. Moreover, with the help of these tools and the ideas behind the RRT*, we construct algorithms that also guarantee almost sure convergence to optimal solutions.
Download or read book Coordinated Path Planning for Multiple Robots written by Petr S̆vestka and published by . This book was released on 1996 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "We present a new approach to the multi-robot path planning problem, where a number of robots are to change their positions through feasible motions in the same static environment. Rather than the usual decoupled planning, we use a coordinated approach. As a result we can show that the method is probabilistically complete, that is, any solvable problem will be solved within a finite amount of time. A data- structure storing multi-robot motions is built in two steps. First, a roadmap is constructed for just one robot. For this we use the Probabilistic Path Planner, which guarantees that the approach can be easily applied to different robot types. In the second step, a number of these simple roadmaps are combined into a roadmap for the composite robot. This data-structure can be used for retrieving multi-robot paths. We have applied the method to car-like robots, and simulation results are presented which show that problems involving up to 5 car-like robots in complex environments are solved successfully in computation times in the order of seconds, after a preprocessing step (the construction of the data structure) that consumes, at most, a few minutes. Such a preprocessing step however needs to be performed just once, for a given static environment."
Download or read book Algorithmic and Computational Robotics written by Bruce Donald and published by CRC Press. This book was released on 2001-04-21 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms that control the computational processes relating sensors and actuators are indispensable for robot navigation and the perception of the world in which they move. Therefore, a deep understanding of how algorithms work to achieve this control is essential for the development of efficient and usable robots in a broad field of applications.
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 Marine Robot Autonomy written by Mae L. Seto and published by Springer Science & Business Media. This book was released on 2012-12-09 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomy for Marine Robots provides a timely and insightful overview of intelligent autonomy in marine robots. A brief history of this emerging field is provided, along with a discussion of the challenges unique to the underwater environment and their impact on the level of intelligent autonomy required. Topics covered at length examine advanced frameworks, path-planning, fault tolerance, machine learning, and cooperation as relevant to marine robots that need intelligent autonomy.
Download or read book Principles of Robot Motion written by Howie Choset and published by MIT Press. This book was released on 2005-05-20 with total page 642 pages. Available in PDF, EPUB and Kindle. Book excerpt: A text that makes the mathematical underpinnings of robot motion accessible and relates low-level details of implementation to high-level algorithmic concepts. Robot motion planning has become a major focus of robotics. Research findings can be applied not only to robotics but to planning routes on circuit boards, directing digital actors in computer graphics, robot-assisted surgery and medicine, and in novel areas such as drug design and protein folding. This text reflects the great advances that have taken place in the last ten years, including sensor-based planning, probabalistic planning, localization and mapping, and motion planning for dynamic and nonholonomic systems. Its presentation makes the mathematical underpinnings of robot motion accessible to students of computer science and engineering, rleating low-level implementation details to high-level algorithmic concepts.
Download or read book An Obstacle based Probabilistic Roadmap Method for Path Planning written by Yan Wu and published by . This book was released on 1996 with total page 136 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robotics Research written by Sebastian Thrun and published by Springer Science & Business Media. This book was released on 2007-02-05 with total page 582 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains 50 papers presented at the 12th International Symposium of Robotics Research, which took place October 2005 in San Francisco, CA. Coverage includes: physical human-robot interaction, humanoids, mechanisms and design, simultaneous localization and mapping, field robots, robotic vision, robot design and control, underwater robotics, learning and adaptive behavior, networked robotics, and interfaces and interaction.
Download or read book Probability based Path Planning for Stochastic Nonholonomic Systems with Obstacle Avoidance written by Jianping Lin and published by . This book was released on 2015 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: There exist various path planning methods in robotics. The Probabilistic Roadmap and Rapidly-exploring Random Tree (RRT) became popular in recent decades. It is known that the RRT is more suitable for nonholonomic systems. The RRT is a sampling-based algorithm which is designed for path planning problem and is efficient to handle high-dimensional configuration space (C-space) and nonholonomic constraints. Under the constraints, the RRT can generate paths between an initial state and a goal state while avoiding obstacles. However it does not guarantee that the resulting path is optimal. In systems with stochasticity, targeting error and closeness of the obstacle to the planned path can be considered to obtain the optimal path. In this thesis, the targeting error is defined as the root-mean-square (RMS) distance from the path samples to the desired target and the closeness is defined as probability of obstacle collision. Then, a cost function is defined as a sum of the targeting error and the obstacle closeness, and numerically minimized to find the path. The RRT result serves as an initial starting point for this subsequent optimization.
Download or read book Path Planning and Evolutionary Optimization of Wheeled Robots written by Daljeet Singh and published by . This book was released on 2013 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Probabilistic roadmap methods (PRM) have been a well-known solution for solving motion planning problems where we have a fixed set of start and goal configurations in a workspace. We define a configuration space with static obstacles. We implement PRM to find a feasible path between start and goal for car-like robots. We further extend the concept of path planning by incorporating evolutionary optimization algorithms to tune the PRM parameters. The theory is demonstrated with simulations and experiments. Our results show that there is a significant improvement in the performance metrics of PRM after optimizing the PRM parameters using biogeography-based optimization, which is an evolutionary optimization algorithm. The performance metrics (namely path length, number of hops, number of loops and fail-rate) show 34.91%, 23.18%, 52.21% and 21.21% improvement after using optimized PRM parameters. We also experimentally demonstrate the application of path planning using PRM to mobile car-like robots.
