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Author: Sandeep Singh Chahal
Publisher:
ISBN:
Category :
Languages : en
Pages : 64
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Book Description
Planning algorithms have attained omnipresent successes in several fields including robotics, animation, manufacturing, drug design, computational biology and aerospace applications. Path Planning is an essential component for autonomous robots. The problem involves searching the configuration space and constructing a desired collision-free path that connects two states (the start and the goal) for a robot to gradually navigate from one state to another. In global path planners, the complete path is computed prior to the robot set off. Sampling based planning like Rapidly Expanding Random Trees (RRT) and Probabilistic Road Maps (PRM) used for single or multi-query planning has gained popularity since it is probabilistic complete and scales well to complex configuration spaces. However, re-planning (re-calculating the complete path) is almost unavoidable as path execution is inherently uncertain since a robot will deviate from the path due to slippage and other uncertainties in the environment. Local path planners which only calculate the path direction at the current location partially alleviate this problem since they do not pre-calculate a complete path and are thus less affected by deviations. However, local path planners are either not complete or, if they use navigation functions, do not scale well to complex environments. To address this, this work presents an approach that combines the advantages of sampling-based global path planning with the benefit of a local, navigation function-based path planning on the generated sample space. This reduces the need for re-planning if the robot diverges from the original path by utilizing a harmonic function potential field computed over the RRT sample set and directional nearest neighbors. The proposed work derives the samples in the environment using a simple randomized algorithm and systematically sampled obstacles that are hit during random sampling of the space. It therefore avoids sampling of the complete space. Additionally, samples generated during one planning phase can be exploited further for new goals in the environment.
Author: Sandeep Singh Chahal
Publisher:
ISBN:
Category :
Languages : en
Pages : 64
Get Book Here
Book Description
Planning algorithms have attained omnipresent successes in several fields including robotics, animation, manufacturing, drug design, computational biology and aerospace applications. Path Planning is an essential component for autonomous robots. The problem involves searching the configuration space and constructing a desired collision-free path that connects two states (the start and the goal) for a robot to gradually navigate from one state to another. In global path planners, the complete path is computed prior to the robot set off. Sampling based planning like Rapidly Expanding Random Trees (RRT) and Probabilistic Road Maps (PRM) used for single or multi-query planning has gained popularity since it is probabilistic complete and scales well to complex configuration spaces. However, re-planning (re-calculating the complete path) is almost unavoidable as path execution is inherently uncertain since a robot will deviate from the path due to slippage and other uncertainties in the environment. Local path planners which only calculate the path direction at the current location partially alleviate this problem since they do not pre-calculate a complete path and are thus less affected by deviations. However, local path planners are either not complete or, if they use navigation functions, do not scale well to complex environments. To address this, this work presents an approach that combines the advantages of sampling-based global path planning with the benefit of a local, navigation function-based path planning on the generated sample space. This reduces the need for re-planning if the robot diverges from the original path by utilizing a harmonic function potential field computed over the RRT sample set and directional nearest neighbors. The proposed work derives the samples in the environment using a simple randomized algorithm and systematically sampled obstacles that are hit during random sampling of the space. It therefore avoids sampling of the complete space. Additionally, samples generated during one planning phase can be exploited further for new goals in the environment.
Author: Steven M. LaValle
Publisher: Cambridge University Press
ISBN: 9780521862059
Category : Computers
Languages : en
Pages : 844
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Book Description
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.
Author: John Canny
Publisher: MIT Press
ISBN: 9780262031363
Category : Computers
Languages : en
Pages : 220
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Book Description
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.
Author: Jean-Claude Latombe
Publisher: Springer Science & Business Media
ISBN: 1461540224
Category : Technology & Engineering
Languages : en
Pages : 668
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Book Description
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.
Author: Richard M. Murray
Publisher: CRC Press
ISBN: 1351469797
Category : Technology & Engineering
Languages : en
Pages : 503
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Book Description
A Mathematical Introduction to Robotic Manipulation presents a mathematical formulation of the kinematics, dynamics, and control of robot manipulators. It uses an elegant set of mathematical tools that emphasizes the geometry of robot motion and allows a large class of robotic manipulation problems to be analyzed within a unified framework. The foundation of the book is a derivation of robot kinematics using the product of the exponentials formula. The authors explore the kinematics of open-chain manipulators and multifingered robot hands, present an analysis of the dynamics and control of robot systems, discuss the specification and control of internal forces and internal motions, and address the implications of the nonholonomic nature of rolling contact are addressed, as well. The wealth of information, numerous examples, and exercises make A Mathematical Introduction to Robotic Manipulation valuable as both a reference for robotics researchers and a text for students in advanced robotics courses.
Author: Mark W Spong
Publisher: John Wiley & Sons
ISBN: 9788126517800
Category : Robots
Languages : en
Pages : 356
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Book Description
This self-contained introduction to practical robot kinematics and dynamics includes a comprehensive treatment of robot control. It provides background material on terminology and linear transformations, followed by coverage of kinematics and inverse kinematics, dynamics, manipulator control, robust control, force control, use of feedback in nonlinear systems, and adaptive control. Each topic is supported by examples of specific applications. Derivations and proofs are included in many cases. The book includes many worked examples, examples illustrating all aspects of the theory, and problems.
Author: Antonio Bicchi
Publisher: Springer Science & Business Media
ISBN: 354036224X
Category : Technology & Engineering
Languages : en
Pages : 283
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Book Description
The ?eld of robotics continues to ?ourish and develop. In common with general scienti?c investigation, new ideas and implementations emerge quite spontaneously and these are discussed, used, discarded or subsumed at c- ferences, in the reference journals, as well as through the Internet. After a little more maturity has been acquired by the new concepts, then archival publication as a scienti?c or engineering monograph may occur. The goal of the Springer Tracts in Advanced Robotics is to publish new developments and advances in the ?elds of robotics research – rapidly and informally but with a high quality. It is hoped that prospective authors will welcome the opportunity to publish a structured presentation of some of the emerging robotics methodologies and technologies. The edited volume by Antonio Bicchi, Henrik Christensen and Domenico Prattichizzo is the outcome of the second edition of a workshop jointly sponsored by the IEEE Control Systems Society and the IEEE Robotics and Automation Society. Noticeably, the previous volume was published in the Springer Lecture Notes on Control and Information Sciences. The authors are recognised as leading scholars internationally. A n- ber of challenging control problems on the forefront of today’s research in robotics and automation are covered, with special emphasis on vision, sensory-feedback control, human-centered robotics, manipulation, planning, ?exible and cooperative robots, assembly systems.
Author: John Monteith
Publisher: Butterworth-Heinemann
ISBN: 9780713129311
Category : Nature
Languages : en
Pages : 308
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Book Description
Thoroughly revised and up-dated edition of a highly successful textbook.
Author: Kikuo Fujimura
Publisher: Springer Science & Business Media
ISBN: 4431681655
Category : Computers
Languages : en
Pages : 190
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Book Description
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.
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319296590
Category : Computers
Languages : en
Pages : 518
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Book Description
This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.
