Sampling Based Motion Planning

Download Sampling Based Motion Planning full books in PDF, epub, and Kindle. Read online Sampling Based Motion Planning ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Robot Motion Planning

Author : Jean-Claude Latombe
Publisher : Springer Science & Business Media
ISBN 13 : 1461540224
Total Pages : 668 pages
Book Rating : 4.4/5 (615 download)

DOWNLOAD NOW!

Book Synopsis Robot Motion Planning by : Jean-Claude Latombe

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.

Planning Algorithms

Author : Steven M. LaValle
Publisher : Cambridge University Press
ISBN 13 : 9780521862059
Total Pages : 844 pages
Book Rating : 4.8/5 (62 download)

DOWNLOAD NOW!

Book Synopsis Planning Algorithms by : Steven M. LaValle

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.

Modern Robotics

Author : Kevin M. Lynch
Publisher : Cambridge University Press
ISBN 13 : 1107156300
Total Pages : 545 pages
Book Rating : 4.1/5 (71 download)

DOWNLOAD NOW!

Book Synopsis Modern Robotics by : Kevin M. Lynch

Download or read book Modern Robotics written by Kevin M. Lynch and published by Cambridge University Press. This book was released on 2017-05-25 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: A modern and unified treatment of the mechanics, planning, and control of robots, suitable for a first course in robotics.

Metrics for Sampling-based Motion Planning

Author : Marco Antonio Morales Aguirre
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (61 download)

DOWNLOAD NOW!

Book Synopsis Metrics for Sampling-based Motion Planning by : Marco Antonio Morales Aguirre

Download or read book Metrics for Sampling-based Motion Planning written by Marco Antonio Morales Aguirre and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A motion planner finds a sequence of potential motions for a robot to transit from an initial to a goal state. To deal with the intractability of this problem, a class of methods known as sampling-based planners build approximate representations of potential motions through random sampling. This selective random exploration of the space has produced many remarkable results, including solving many previously unsolved problems. Sampling-based planners usually represent the motions as a graph (e.g., the Probabilistic Roadmap Methods or PRMs), or as a tree (e.g., the Rapidly exploring Random Tree or RRT). Although many sampling-based planners have been proposed, we do not know how to select among them because their different sampling biases make their performance depend on the features of the planning space. Moreover, since a single problem can contain regions with vastly different features, there may not exist a simple exploration strategy that will perform well in every region. Unfortunately, we lack quantitative tools to analyze problem features and planners performance that would enable us to match planners to problems. We introduce novel metrics for the analysis of problem features and planner performance at multiple levels: node level, global level, and region level. At the node level, we evaluate how new samples improve coverage and connectivity of the evolving model. At the global level, we evaluate how new samples improve the structure of the model. At the region level, we identify groups or regions that share similar features. This is a set of general metrics that can be applied in both graph-based and tree-based planners. We show several applications for these tools to compare planners, to decide whether to stop planning or to switch strategies, and to adjust sampling in different regions of the problem.

Robotics Research

Author : Antonio Bicchi
Publisher : Springer
ISBN 13 : 3319609165
Total Pages : 712 pages
Book Rating : 4.3/5 (196 download)

DOWNLOAD NOW!

Book Synopsis Robotics Research by : Antonio Bicchi

Download or read book Robotics Research written by Antonio Bicchi and published by Springer. This book was released on 2017-07-24 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt: ISRR, the "International Symposium on Robotics Research", is one of robotics pioneering Symposia, which has established over the past two decades some of the field's most fundamental and lasting contributions. This book presents the results of the seventeenth edition of "Robotics Research" ISRR15, offering a collection of a broad range of topics in robotics. The content of the contributions provides a wide coverage of the current state of robotics research.: the advances and challenges in its theoretical foundation and technology basis, and the developments in its traditional and new emerging areas of applications. The diversity, novelty, and span of the work unfolding in these areas reveal the field's increased maturity and expanded scope and define the state of the art of robotics and its future direction.

Optimizations for Sampling-based Motion Planning Algorithms

Author : Joshua John Bialkowski
Publisher :
ISBN 13 :
Total Pages : 150 pages
Book Rating : 4.:/5 (879 download)

DOWNLOAD NOW!

Book Synopsis Optimizations for Sampling-based Motion Planning Algorithms by : Joshua John Bialkowski

Download or read book Optimizations for Sampling-based Motion Planning Algorithms written by Joshua John Bialkowski and published by . This book was released on 2014 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sampling-basedalgorithms solve the motion planning problem by successively solving several separate suproblems of reduced complexity. As a result, the efficiency of the sampling-based algorithm depends on the complexity of each of the algorithms used to solve the individual subproblems, namely the procedures GenerateSample, FindNearest, LocalPlan, CollisionFree, and AddToGraph. However, it is often the case that these subproblems are quite related, working on common components of the problem definition. Therefore, distinct algorithms and segregated data structures for solving these subproblems might be costing sampling-based algorithms more time than necessary. The thesis of this dissertation is the following: By taking advantage of the fact that these subproblems are solved repeatedly with similar inputs, and the relationships between data structures used to solve the subproblems, we may significantly reduce the practical complexity of sampling-based motion planning algorithms. Moreover, this reuse of information from components can be used to find a middle ground between exact motion planning algorithms which find an explicit representation ofthe collision-free space,and sampling-based algorithms which find no representation of the collision-free space, except for the zeromeasure paths between connected nodes in the roadmap.

The Complexity of Robot Motion Planning

Author : John Canny
Publisher : MIT Press
ISBN 13 : 9780262031363
Total Pages : 220 pages
Book Rating : 4.0/5 (313 download)

DOWNLOAD NOW!

Book Synopsis The Complexity of Robot Motion Planning by : John Canny

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.

Encyclopedia of Robotics

Author : Marcelo H. Ang
Publisher : Springer
ISBN 13 : 9783662437698
Total Pages : 4000 pages
Book Rating : 4.4/5 (376 download)

DOWNLOAD NOW!

Book Synopsis Encyclopedia of Robotics by : Marcelo H. Ang

Download or read book Encyclopedia of Robotics written by Marcelo H. Ang and published by Springer. This book was released on 2018-07-13 with total page 4000 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Encyclopedia of Robotics addresses the existing need for an easily accessible yet authoritative and granular knowledge resource in robotic science and engineering. The encyclopedia is a work that comprehensively explains the scientific, application-based, interactive and socio-ethical parameters of robotics. It is the first work that explains at the concept and fact level the state of the field of robotics and its future directions. The encyclopedia is a complement to Springer’s highly successful Handbook of Robotics that has analyzed the state of robotics through the medium of descriptive essays. Organized in an A-Z format for quick and easy understanding of both the basic and advanced topics across a broad spectrum of areas in a self-contained form. The entries in this Encyclopedia will be a comprehensive description of terms used in robotics science and technology. Each term, when useful, is described concisely with online illustrations and enhanced user interactivity (on SpringerReference.com).

Sampling-based Motion Planning

Author : Roland Jan Geraerts
Publisher :
ISBN 13 : 9789039342299
Total Pages : 185 pages
Book Rating : 4.3/5 (422 download)

DOWNLOAD NOW!

Book Synopsis Sampling-based Motion Planning by : Roland Jan Geraerts

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:

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

Download 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 PDF Online Free

Author : Troy McMahon
Publisher :
ISBN 13 : 9781638281351
Total Pages : 0 pages
Book Rating : 4.2/5 (813 download)

DOWNLOAD NOW!

Book Synopsis 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 by : Troy McMahon

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.

Autonomous Mobile Robots and Multi-Robot Systems

Author : Eugene Kagan
Publisher : John Wiley & Sons
ISBN 13 : 1119212863
Total Pages : 340 pages
Book Rating : 4.1/5 (192 download)

DOWNLOAD NOW!

Book Synopsis Autonomous Mobile Robots and Multi-Robot Systems by : Eugene Kagan

Download or read book Autonomous Mobile Robots and Multi-Robot Systems written by Eugene Kagan and published by John Wiley & Sons. This book was released on 2019-12-16 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Offers a theoretical and practical guide to the communication and navigation of autonomous mobile robots and multi-robot systems This book covers the methods and algorithms for the navigation, motion planning, and control of mobile robots acting individually and in groups. It addresses methods of positioning in global and local coordinates systems, off-line and on-line path-planning, sensing and sensors fusion, algorithms of obstacle avoidance, swarming techniques and cooperative behavior. The book includes ready-to-use algorithms, numerical examples and simulations, which can be directly implemented in both simple and advanced mobile robots, and is accompanied by a website hosting codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming consists of four main parts. The first looks at the models and algorithms of navigation and motion planning in global coordinates systems with complete information about the robot’s location and velocity. The second part considers the motion of the robots in the potential field, which is defined by the environmental states of the robot's expectations and knowledge. The robot's motion in the unknown environments and the corresponding tasks of environment mapping using sensed information is covered in the third part. The fourth part deals with the multi-robot systems and swarm dynamics in two and three dimensions. Provides a self-contained, theoretical guide to understanding mobile robot control and navigation Features implementable algorithms, numerical examples, and simulations Includes coverage of models of motion in global and local coordinates systems with and without direct communication between the robots Supplemented by a companion website offering codes, videos, and PowerPoint slides Autonomous Mobile Robots and Multi-Robot Systems: Motion-Planning, Communication and Swarming is an excellent tool for researchers, lecturers, senior undergraduate and graduate students, and engineers dealing with mobile robots and related issues.

Sampling-based Motion Planning Algorithms: Analysis and Development

Author : Nathan Alexander Wedge
Publisher :
ISBN 13 :
Total Pages : 172 pages
Book Rating : 4.:/5 (722 download)

DOWNLOAD NOW!

Book Synopsis Sampling-based Motion Planning Algorithms: Analysis and Development by : Nathan Alexander Wedge

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.

Sampling-Based Motion Planning Algorithms for Replanning and Spatial Load Balancing

Author : Beth Leigh Boardman
Publisher :
ISBN 13 :
Total Pages : 137 pages
Book Rating : 4.:/5 (11 download)

DOWNLOAD NOW!

Book Synopsis Sampling-Based Motion Planning Algorithms for Replanning and Spatial Load Balancing by : Beth Leigh Boardman

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.

Sampling-based Motion Planning Algorithms for Dynamical Systems

Author :
Publisher :
ISBN 13 :
Total Pages : 143 pages
Book Rating : 4.:/5 (939 download)

DOWNLOAD NOW!

Book Synopsis Sampling-based Motion Planning Algorithms for Dynamical Systems by :

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.

Principles of Robot Motion

Author : Howie Choset
Publisher : MIT Press
ISBN 13 : 9780262033275
Total Pages : 642 pages
Book Rating : 4.0/5 (332 download)

DOWNLOAD NOW!

Book Synopsis Principles of Robot Motion by : Howie Choset

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.

Autonomous Vehicle

Author : Andrzej Zak
Publisher : BoD – Books on Demand
ISBN 13 : 9535125842
Total Pages : 158 pages
Book Rating : 4.5/5 (351 download)

DOWNLOAD NOW!

Book Synopsis Autonomous Vehicle by : Andrzej Zak

Download or read book Autonomous Vehicle written by Andrzej Zak and published by BoD – Books on Demand. This book was released on 2016-09-07 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles, despite their relatively short history, have already found practical application in many areas of human activity. Such vehicles are usually replacing people in performing tasks that require long operating time and are held in inaccessible or hazardous environments. Nevertheless, autonomous robotics is probably the area that is being developed the most because of the great demand for such devices in different areas of our lives. This book is a collection of experiences shared by scientists from different parts of the world doing researches and daily exploiting autonomous systems. Giving this book in the hands of the reader, we hope that it will be a treasure trove of knowledge and inspiration for further research in the field of autonomous vehicles.

Active Sensor Planning for Multiview Vision Tasks

Author : Shengyong Chen
Publisher : Springer Science & Business Media
ISBN 13 : 3540770720
Total Pages : 270 pages
Book Rating : 4.5/5 (47 download)

DOWNLOAD NOW!

Book Synopsis Active Sensor Planning for Multiview Vision Tasks by : Shengyong Chen

Download or read book Active Sensor Planning for Multiview Vision Tasks written by Shengyong Chen and published by Springer Science & Business Media. This book was released on 2008-01-23 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique book explores the important issues in studying for active visual perception. The book’s eleven chapters draw on recent important work in robot vision over ten years, particularly in the use of new concepts. Implementation examples are provided with theoretical methods for testing in a real robot system. With these optimal sensor planning strategies, this book will give the robot vision system the adaptability needed in many practical applications.