Download or read book Reasoning about Uncertainty in Robot Motion Planning written by Anthony Lazanas and published by . This book was released on 1994 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimentation with the algorithm using a real mobile robot has been successful. By engineering the workspace, we have been able to satisfy all the assumptions of our planning model. As a result, the robot has been able to operate for long periods of time with no failures.

Download or read book Reasoning with Uncertainty in Robotics written by Leo Dorst and published by Lecture Notes in Artificial Intelligence. This book was released on 1996-06-12 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the refereed proceedings of the International Workshop on Reasoning with Uncertainty in Robotics, RUR'95, held in Amsterdam, The Netherlands, in December 1995. The book contains 13 revised full papers carefully selected for presentation during the workshop together with six invited papers. Also included are two comprehensive tutorial texts and an introduction by the volume editors. Thus the book is both a competent state-of-the-art report on current research and development and a valuable survey and introduction for researchers entering the area or professionals interested in the application of up-to-date techniques.

Download or read book Planning and Decision Making for Aerial Robots written by Yasmina Bestaoui Sebbane and published by Springer Science & Business Media. This book was released on 2014-01-10 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the emerging field of planning and decision making for aerial robots. An aerial robot is the ultimate form of Unmanned Aerial Vehicle, an aircraft endowed with built-in intelligence, requiring no direct human control and able to perform a specific task. It must be able to fly within a partially structured environment, to react and adapt to changing environmental conditions and to accommodate for the uncertainty that exists in the physical world. An aerial robot can be termed as a physical agent that exists and flies in the real 3D world, can sense its environment and act on it to achieve specific goals. So throughout this book, an aerial robot will also be termed as an agent. Fundamental problems in aerial robotics include the tasks of spatial motion, spatial sensing and spatial reasoning. Reasoning in complex environments represents a difficult problem. The issues specific to spatial reasoning are planning and decision making. Planning deals with the trajectory algorithmic development based on the available information, while decision making determines priorities and evaluates potential environmental uncertainties. The issues specific to planning and decision making for aerial robots in their environment are examined in this book and categorized as follows: motion planning, deterministic decision making, decision making under uncertainty and finally multi-robot planning. A variety of techniques are presented in this book, and a number of relevant case studies are examined. The topics considered in this book are multidisciplinary in nature and lie at the intersection of Robotics, Control Theory, Operational Research and Artificial Intelligence.

Download or read book Robot Motion Planning written by Jean-Claude Latombe and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 668 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the ultimate goals in Robotics is to create autonomous robots. Such robots will accept high-level descriptions of tasks and will execute them without further human intervention. The input descriptions will specify what the user wants done rather than how to do it. The robots will be any kind of versatile mechanical device equipped with actuators and sensors under the control of a computing system. Making progress toward autonomous robots is of major practical inter est in a wide variety of application domains including manufacturing, construction, waste management, space exploration, undersea work, as sistance for the disabled, and medical surgery. It is also of great technical interest, especially for Computer Science, because it raises challenging and rich computational issues from which new concepts of broad useful ness are likely to emerge. Developing the technologies necessary for autonomous robots is a formidable undertaking with deep interweaved ramifications in auto mated reasoning, perception and control. It raises many important prob lems. One of them - motion planning - is the central theme of this book. It can be loosely stated as follows: How can a robot decide what motions to perform in order to achieve goal arrangements of physical objects? This capability is eminently necessary since, by definition, a robot accomplishes tasks by moving in the real world. The minimum one would expect from an autonomous robot is the ability to plan its x Preface own motions.

Download or read book Error Detection and Recovery for Robot Motion Planning with Uncertainty written by Bruce Randall Donald and published by . This book was released on 1987 with total page 618 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robots must plan and execute tasks in the presence of uncertainty. Uncertainty arises from sensing errors, controls errors, and uncertainty in the geometry of the environment. The last, which is called model error, has received little previous attention. This document presents a framework for computing motion strategies that are guaranteed to succeed in the presence of all three kinds of uncertainty. The motion strategies comprise sensor-based gross motions, complaint motions, and simple pushing motions. It is not always possible to find plans that are guaranteed to succeed. For example, if tolerancing errors render an assembly infeasible, the plan executor should stop and signal failure. In such cases the insistence on guaranteed success is to restrictive. For this reason Error Detection and Recovery (EDR) strategies, there is no possibility that the plan will fail without the executor realizing it. The EDR framework fills a gap when guaranteed plans cannot be found or do not exist: it provides a technology for constructing plans that might work, but fail in a reasonable way when they cannot. Keywords: Robotics; Mobility.

Download or read book Algorithmic Foundations of Robotics X written by Emilio Frazzoli and published by Springer. This book was released on 2013-02-14 with total page 625 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithms are a fundamental component of robotic systems. Robot algorithms process inputs from sensors that provide noisy and partial data, build geometric and physical models of the world, plan high-and low-level actions at different time horizons, and execute these actions on actuators with limited precision. The design and analysis of robot algorithms raise a unique combination of questions from many elds, including control theory, computational geometry and topology, geometrical and physical modeling, reasoning under uncertainty, probabilistic algorithms, game theory, and theoretical computer science. The Workshop on Algorithmic Foundations of Robotics (WAFR) is a single-track meeting of leading researchers in the eld of robot algorithms. Since its inception in 1994, WAFR has been held every other year, and has provided one of the premiere venues for the publication of some of the eld's most important and lasting contributions. This books contains the proceedings of the tenth WAFR, held on June 13{15 2012 at the Massachusetts Institute of Technology. The 37 papers included in this book cover a broad range of topics, from fundamental theoretical issues in robot motion planning, control, and perception, to novel applications.

Download or read book Robot Motion Planning microform a Geometric Reasoning Approach written by King Sun Ma and published by National Library of Canada = Bibliothèque nationale du Canada. This book was released on 1994 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Incrementally Increasing the Uncertainty tolerance of Robotic Manipulation Plans written by Scott Bennett and published by . This book was released on 1991 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: Last, unguaranteed but practical plans can be generated by the incremental approach when they lie outside the scope of the guaranteed planner. To demonstrate our approach we describe an implemented system called GRASPER which learns to grasp novel objects given only imprecise television camera input. No prior model of the objects is assumed, nor are the objects required to satisfy a priori constraints on their shapes. Robustness of the system's grasping improves with experience."

Download or read book Robot Motion Planning with Uncertainty in Control and Sensing written by Jean-Claude Latombe and published by . This book was released on 1989 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we consider the problem of planning motion strategies in the presence of uncertainty in both control and sensing for simple robots described in a two dimensional configuration space. We consider the preimage backchaining approach to this problem. which was first proposed by Lozano-Perez. Mason and Taylor (1984). Although attractive, the approach raises several difficult computational issues. One of them, which is directly addressed in this paper. is preimage computation. We describe two practical methods for computing preimages, which we call backprojection from sticking edges and backprojection from goal kernel. Also discussed non-implemented improvements of this planner and additional results.

Download or read book The Complexity of Robot Motion Planning written by John Canny and published by MIT Press. This book was released on 1988 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Complexity of Robot Motion Planning makes original contributions both to roboticsand to the analysis of algorithms. In this groundbreaking monograph John Canny resolveslong-standing problems concerning the complexity of motion planning and, for the central problem offinding a collision free path for a jointed robot in the presence of obstacles, obtains exponentialspeedups over existing algorithms by applying high-powered new mathematical techniques.Canny's newalgorithm for this "generalized movers' problem," the most-studied and basic robot motion planningproblem, has a single exponential running time, and is polynomial for any given robot. The algorithmhas an optimal running time exponent and is based on the notion of roadmaps - one-dimensionalsubsets of the robot's configuration space. In deriving the single exponential bound, Cannyintroduces and reveals the power of two tools that have not been previously used in geometricalgorithms: the generalized (multivariable) resultant for a system of polynomials and Whitney'snotion of stratified sets. He has also developed a novel representation of object orientation basedon unnormalized quaternions which reduces the complexity of the algorithms and enhances theirpractical applicability.After dealing with the movers' problem, the book next attacks and derivesseveral lower bounds on extensions of the problem: finding the shortest path among polyhedralobstacles, planning with velocity limits, and compliant motion planning with uncertainty. Itintroduces a clever technique, "path encoding," that allows a proof of NP-hardness for the first twoproblems and then shows that the general form of compliant motion planning, a problem that is thefocus of a great deal of recent work in robotics, is non-deterministic exponential time hard. Cannyproves this result using a highly original construction.John Canny received his doctorate from MITAnd is an assistant professor in the Computer Science Division at the University of California,Berkeley. The Complexity of Robot Motion Planning is the winner of the 1987 ACM DoctoralDissertation Award.

Download or read book Learning and Leveraging Kinematics for Robot Motion Planning Under Uncertainty written by Ajinkya Jain and published by . This book was released on 2021 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: Service robots that can assist humans in performing day-to-day tasks will need to be general-purpose robots that can perform a wide array of tasks without much supervision from end-users. As they will be operating in unstructured and ever-changing human environments, they will need to be capable of adapting to their work environments quickly and learning to perform novel tasks within a few trials. However, current robots fall short of these requirements as they are generally highly specialized, can only perform fixed, predefined tasks reliably, and need to operate in controlled environments. One of the main reasons behind this big gap is that the current robots require complete and accurate information about their surroundings to function effectively, whereas, in human environments, robots will only have access to limited information about their tasks and environments. With incomplete information about its surroundings, a robot using pre-programmed or pre-learned motion policies will fail to adapt to the novel situations encountered during operation and fall short in completing its tasks. Online motion generation methods that do not reason about the lack of information will not suffice either, as the developed policies may be unreliable under incomplete information. Reasoning about the lack of information becomes critical for manipulation tasks a service robot would have to perform. These tasks will often require interacting with multiple objects that make or break contacts during the task. A contact between objects can significantly alter their subsequent motion and lead to sudden transitions in their dynamics. Under these sudden transitions, even minor errors in estimating object poses can cause drastic deviations from the robot's initial motion plan for the task and lead the robot to failure in completing the tasks. Hence, service robots need methods that generate motion policies for manipulation tasks efficiently while accounting for the uncertainty due to incomplete or partial information. Partially Observable Markov Decision Processes (POMDPs) is one such mathematical framework that can model and plan for tasks where the agent lacks complete information about the task. However, POMDPs incur exponentially increasing computational costs with planning time horizon, which restricts the current POMDP-based planning methods to problems having short time horizons. Another challenge for planning-based approaches is that they require a state transition function for the world they are operating in to develop motion plans, which may not always be available to the robot. In control theory terms, a state transition function for the world is analogous to its system plant. In this dissertation, we propose to address these challenges by developing methods that can learn state transition functions for robot manipulation tasks directly from observations and later use them to generate long-horizon motion plans to complete the task under uncertainty. We first model the world state transition functions for robot manipulation tasks involving sudden transitions, such as due to contacts, using hybrid models and develop a novel hierarchical POMDP-planner that leverages the representational power of hybrid models to develop motion plans for long-horizon tasks under uncertainty. Next, we address the requirement of planning-based methods to have access to world state transition functions. We introduce three novel methods for learning kinematic models for articulated objects directly from observations and present an algorithm to construct the state transition functions from the learned kinematics models for manipulating these objects. We focus on learning models for articulated objects as they form one of the biggest sets of household objects that service robots will frequently interact with. The first method, MICAH, focuses on learning kinematic models for articulated objects that exhibit configuration-dependent articulation properties, such as a refrigerator door that stays closed magnetically, from unsegmented sequences of observations of object part poses. Next, we introduce ScrewNet, which removes the requirement of object pose estimation of MICAH and learns articulation properties of objects directly from raw sensory data available to the robot (depth images) without knowing their articulation model category a priori. Extending it further, we introduce DUST-net, which learns distributions over articulation model parameters for objects indicating the network's confidence over the estimated parameters directly from raw depth images. Combining these methods, in this dissertation, we introduce a unified framework that can enable a robot to learn state transition functions for manipulation tasks from observations and later use them to develop long-horizon plans even under uncertainty

Download or read book Theoretical Aspects of Reasoning About Knowledge written by Ronald Fagin and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical Aspects of Reasoning About Knowledge contains the proceedings of the Fifth Conference on Theoretical Aspects of Reasoning About Knowledge (TARK 1994) held in Pacific Grove, California, on March 13-16, 1994. The conference provided a forum for discussing the theoretical aspects of reasoning about knowledge and tackled topics ranging from the logic of iterated belief revision and backwards forward induction to information acquisition from multi-agent resources, infinitely epistemic logic, and coherent belief revision in games. Comprised of 23 chapters, this book begins with a review of situation calculus and a solution to the frame problem, along with the use of a regression method for reasoning about the effect of actions. A novel programming language for high-level robotic control is described, along with a knowledge-based framework for belief change. Subsequent chapters deal with consistent belief reasoning in the presence of inconsistency; an epistemic logic of situations; an axiomatic approach to the logical omniscience problem; and an epistemic proof system for parallel processes. Inductive learning, knowledge asymmetries, and convention are also examined. This monograph will be of interest to both students and practitioners in the fields of artificial intelligence and computer science.

Download or read book Robot Motion Planning for Sensor Based Control with Uncertainty written by Lance A.. Page and published by . This book was released on 1996 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spatial Reasoning and Planning written by Jiming Liu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial reasoning and planning is a core constituent in robotics, graphics, computer-aided design, and geographic information systems. After a review of previous work in the related areas, Liu and Daneshmend present a unified framework for qualitative spatial representation and reasoning. This paves the way for a generation of solutions to spatial problems where the geometric knowledge is imprecise. Many graphical illustrations and detailed algorithm descriptions help the reader to comprehend the solution paths and to develop their own applications. The book is written as a self-contained text for researchers and graduate students. The methodologies, algorithmic details, and case studies presented can be used as course material as well as a convenient reference.

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 Robotics Research written by Yoshiaki Shirai and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Eighth International Symposium of Robotics Research was held in Kanagawa, Japan, on October 4-7 1997; Robotics Research presents the findings of this symposium. The papers, written by international specialists in the field, cover the many topics concerning advanced robotics today, ranging from practical system design to theoretical reasoning and planning. They assess the state of the field and discuss all the current and emerging trends dealing with, amongst many other topics, mobile robotics, manufacturing, learning from humans, autonomous land vehicles, humanoid robots, future robots, and new components. The reader will share with the attendees the meaningful steps forward in building the emerging body of concepts, methods, scientific and technical knowledge that shape modern day robotics.