Download or read book Differentiable Neural Motion Planning Under Task Constraints written by Ahmed Hussain Qureshi and published by . This book was released on 2021 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous robots will soon play a significant role in various domains, such as search-and-rescue, agriculture farms, homes, offices, transportation, and medical surgery, where fast, safe, and optimal response to different situations will be critical. However, to do so, these robots need fast algorithms to plan their motion sequences in real-time with limited perception and battery life. The field of motion planning and control addresses this challenge of coordinating robot motions and enabling them to interact with their environments for performing various challenging tasks under constraints. Planning algorithms for robot control have a long history ranging from methods with complete to probabilistically complete worst-case theoretical guarantees. However, despite having deep roots in artificial intelligence and robotics, these methods tend to be computationally inefficient in high-dimensional problems. On the other hand, machine learning advancements have led toward systems that can directly perform complex decision-making from raw sensory information. This thesis introduces a new class of planning methods called Neural Motion Planners that emerged from the cross-fertilization of classical motion planning and machine learning techniques. These methods can achieve unprecedented speed and robustness in planning robot motion sequences in complex, cluttered, and partially observable environments. They exhibit worst-case theoretical guarantees and solve a broad range of motion planning problems under geometric collision-avoidance, kinodynamic, non-holonomic, and hard kinematic manifold constraints. Another challenge towards deploying robots into our natural world is the tedious process of defining objective functions for underlying motion planners and transferring and composing their motion skills into new skills for a combinatorial outburst in robot's skillset for solving unseen practical problems. To address these challenges, this thesis introduces novel methods, i.e., variational inverse reinforcement learning and compositional reinforcement learning approaches. These methods learn unknown constraint functions and their motion skills directly from expert demonstrations for NMPs and compose them into new complex skills for solving more complicated problems across different domains. Finally, this thesis also presents a model-free neural task planning algorithm that works with never-before-seen objects and generalizes to real world environments. It generates task plans for underlying motion planning and control approaches and solves challenging rearrangement tasks in unknown environments.

Download or read book Toward More Efficient Motion Planning with Differential Constraints written by Maciej Kalisiak and published by . This book was released on 2008 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Agents with differential constraints, although common in the real world, pose a particular difficulty for motion planning algorithms. Methods for solving such problems are still relatively slow and inefficient. In particular, current motion planners generally can neither "see" the world around them, nor generalize from experience. That is, their reliance on collision tests as the only means of sensing the environment yields a tactile, myopic perception of the world. Such short-sightedness greatly limits any potential for detection, learning, or reasoning about frequently encountered situations. In result these methods solve each problem in exactly the same way, whether the first or the hundredth time they attempt it, each time none the wiser. The key component of this thesis proposes a general approach for motion planning in which local sensory information, in conjunction with prior accumulated experience, are exploited to improve planner performance. The approach relies on learning viability models for the agent's "perceptual space", and the use thereof to direct planning effort. In addition, a method is presented for improving runtimes of the RRT motion planning algorithm in heavily constrained search-spaces, a common feature for agents with differential constraints. Finally, the thesis explores the use of viability models for maintaing safe operation of user-controlled agents, a related application which could be harnessed to yield additional, more "natural" experience data for further improving motion planning.

Download or read book Sample based Motion Planning in High dimensional and Differentially constrained Systems written by Alexander C. Shkolnik and published by . This book was released on 2010 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: State of the art sample-based path planning algorithms, such as the Rapidly-exploring Random Tree (RRT), have proven to be effective in path planning for systems subject to complex kinematic and geometric constraints. The performance of these algorithms, however, degrade as the dimension of the system increases. Furthermore, sample-based planners rely on distance metrics which do not work well when the system has differential constraints. Such constraints are particularly challenging in systems with non-holonomic and underactuated dynamics. This thesis develops two intelligent sampling strategies to help guide the search process. To reduce sensitivity to dimension, sampling can be done in a low-dimensional task space rather than in the high-dimensional state space. Altering the sampling strategy in this way creates a Voronoi Bias in task space, which helps to guide the search, while the RRT continues to verify trajectory feasibility in the full state space. Fast path planning is demonstrated using this approach on a 1500-link manipulator. To enable task-space biasing for underactuated systems, a hierarchical task space controller is developed by utilizing partial feedback linearization. Another sampling strategy is also presented, where the local reachability of the tree is approximated, and used to bias the search, for systems subject to differential constraints. Reachability guidance is shown to improve search performance of the RRT by an order of magnitude when planning on a pendulum and non-holonomic car. The ideas of task-space biasing and reachability guidance are then combined for demonstration of a motion planning algorithm implemented on LittleDog, a quadruped robot. The motion planning algorithm successfully planned bounding trajectories over extremely rough terrain.

Download or read book Robot Motion Planning Under Topological Constraints written by Soonkyum Kim and published by . This book was released on 2013 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Motion planning with Inertial Constraints written by Courant Institute of Mathematical Sciences. Computer Science Department and published by . This book was released on 1986 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Nonholonomic Motion Planning written by Zexiang Li and published by Springer. This book was released on 2012-10-30 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nonholonomic Motion Planning grew out of the workshop that took place at the 1991 IEEE International Conference on Robotics and Automation. It consists of contributed chapters representing new developments in this area. Contributors to the book include robotics engineers, nonlinear control experts, differential geometers and applied mathematicians. Nonholonomic Motion Planning is arranged into three chapter groups: Controllability: one of the key mathematical tools needed to study nonholonomic motion. Motion Planning for Mobile Robots: in this section the papers are focused on problems with nonholonomic velocity constraints as well as constraints on the generalized coordinates. Falling Cats, Space Robots and Gauge Theory: there are numerous connections to be made between symplectic geometry techniques for the study of holonomies in mechanics, gauge theory and control. In this section these connections are discussed using the backdrop of examples drawn from space robots and falling cats reorienting themselves. Nonholonomic Motion Planning can be used either as a reference for researchers working in the areas of robotics, nonlinear control and differential geometry, or as a textbook for a graduate level robotics or nonlinear control course.

Download or read book Efficiency and Abstraction in Task and Motion Planning written by William Robert Vega-Brown and published by . This book was released on 2020 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern robots are capable of complex and highly dynamic behaviors, yet the decisionmaking algorithms that drive them struggle to solve problems involving complex behaviors like manipulation. The combination of continuous and discrete dynamics induced by contact creates severe computational challenges, and most known practical approaches rely on hand-designed discrete representations to mitigate computational issues. However, the relationship between the discrete representation and the physical robot is poorly understood and cannot easily be empirically verified, and so many planning systems are brittle and prone to failure when the robot encounters situations not anticipated by the model designer. This thesis addresses the limitations of conventional representations for task and motion planning by introducing a constraint-based representation that explicitly places continuous and discrete dynamics on equal footing. We argue that the challenges in modelling problems with both discrete and continuous dynamics can be reduced to a trade-off between model complexity and empirical accuracy. We propose the use of abstraction to combine models that balance those two constraints differently, and we claim that by using abstraction we can build systems that reliably generate high-quality plans, even in complex domains with many objects. Using our representation, we construct and analyze several new algorithms, providing new insight into long-standing open problems about the decidability and complexity of motion planning. We describe algorithms for sampling-based planning in hybrid domains, and show that these algorithms are complete and asymptotically optimal for systems that can defined by analytic constraints. We also show that the reachability problem can be decided using polynomial space for systems described by polynomial constraints satisfying a certain technical conditions. This class of systems includes many important robotic planning problems, and our results show that the decision problem for several benchmark task and motion planning languages is PSPACE-complete.

Download or read book Learning to Guide Task and Motion Planning written by Beomjoon Kim and published by . This book was released on 2020 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: How can we enable robots to efficiently reason both at the discrete task-level and the continuous motion-level to achieve high-level goals such as tidying up a room or constructing a building? This is a challenging problem that requires integrated reasoning about the combinatoric aspects of the problem, such as deciding which object to manipulate, and continuous aspects of the problem, such as finding collision-free manipulation motions, to achieve goals. The classical robotics approach is to design a planner that, given an initial state, goal, and transition model, computes a plan. The advantage of this approach is its immense generalization capability. For any given state and goal, a planner will find a solution if there is one. The inherent drawback, however, is that a planner does not typically make use of planning experience, and computes a plan from scratch every time it encounters a new problem. For complex problems, this renders planners extremely inefficient. Alternatively, we can take a pure learning approach where the system learns, from either reinforcement signals or demonstrations, a policy that maps states to actions. The advantage of this approach is that computing the next action to execute becomes much cheaper than pure planning because it is simply making a prediction using a function approximator. The drawback, however, is that it is brittle. If a policy encounters a state that is very different from the ones seen in the training set, then it is likely to make mistakes and might get into a situation from which it does not know how to proceed. Our approach is to take the middle ground between these two extremes. More concretely, this thesis introduces several algorithms that learn to guide a planner from planning experience. We propose state representations, neural network architectures, and data-efficient algorithms for learning to perform both task and motion level reasoning using neural networks. We then use these neural networks to guide a planner and show that it performs more efficiently than pure planning and pure learning algorithms.

Download or read book Advanced planning control and signal processing methods and applications in robotic systems volume II written by Zhan Li and published by Frontiers Media SA. This book was released on 2023-05-25 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Algorithmic Foundations of Robotics V written by Jean-Daniel Boissonnat and published by Springer Science & Business Media. This book was released on 2003-09-11 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: Selected contributions to the Workshop WAFR 2002, held December 15-17, 2002, Nice, France. This fifth biannual Workshop on Algorithmic Foundations of Robotics focuses on algorithmic issues related to robotics and automation. The design and analysis of robot algorithms raises fundamental questions in computer science, computational geometry, mechanical modeling, operations research, control theory, and associated fields. The highly selective program highlights significant new results such as algorithmic models and complexity bounds. The validation of algorithms, design concepts, or techniques is the common thread running through this focused collection.

Download or read book Advances in Human Factors in Simulation and Modeling written by Daniel N. Cassenti and published by Springer. This book was released on 2017-06-13 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on computational modeling and simulation research that advances the current state-of-the-art regarding human factors in simulation and applied digital human modeling. It reports on cutting-edge simulators such as virtual and augmented reality, on multisensory environments, and on modeling and simulation methods used in various applications, such as surgery, military operations, occupational safety, sports training, education, transportation and robotics. Based on the AHFE 2017 International Conference on Human Factors in Simulation and Modeling, held on July 17–21, 2017, in Los Angeles, California, USA, the book is intended as a timely reference guide for researchers and practitioners developing new modeling and simulation tools for analyzing or improving human performance. It also offers a unique resource for modelers seeking insights into human factors research and more feasible and reliable computational tools to foster advances in this exciting research field.

Download or read book Experimental Robotics written by Oussama Khatib and published by Springer. This book was released on 2013-08-20 with total page 919 pages. Available in PDF, EPUB and Kindle. Book excerpt: Incorporating papers from the 12th International Symposium on Experimental Robotics (ISER), December 2010, this book examines the latest advances across the various fields of robotics. Offers insights on both theoretical concepts and experimental results.

Download or read book Kinematic Control of Redundant Robot Arms Using Neural Networks written by Shuai Li and published by John Wiley & Sons. This book was released on 2019-02-11 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents pioneering and comprehensive work on engaging movement in robotic arms, with a specific focus on neural networks This book presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more specific level, it deals with the dynamic-neural-network based kinematic control of redundant robot arms by using theoretical tools and simulations. Kinematic Control of Redundant Robot Arms Using Neural Networks is divided into three parts: Neural Networks for Serial Robot Arm Control; Neural Networks for Parallel Robot Control; and Neural Networks for Cooperative Control. The book starts by covering zeroing neural networks for control, and follows up with chapters on adaptive dynamic programming neural networks for control; projection neural networks for robot arm control; and neural learning and control co-design for robot arm control. Next, it looks at robust neural controller design for robot arm control and teaches readers how to use neural networks to avoid robot singularity. It then instructs on neural network based Stewart platform control and neural network based learning and control co-design for Stewart platform control. The book finishes with a section on zeroing neural networks for robot arm motion generation. Provides comprehensive understanding on robot arm control aided with neural networks Presents neural network-based control techniques for single robot arms, parallel robot arms (Stewart platforms), and cooperative robot arms Provides a comparison of, and the advantages of, using neural networks for control purposes rather than traditional control based methods Includes simulation and modelling tasks (e.g., MATLAB) for onward application for research and engineering development By focusing on robot arm control aided by neural networks whilst examining central topics surrounding the field, Kinematic Control of Redundant Robot Arms Using Neural Networks is an excellent book for graduate students and academic and industrial researchers studying neural dynamics, neural networks, analog and digital circuits, mechatronics, and mechanical engineering.

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 Emerging Trends in Mechanical Engineering written by L. Vijayaraghavan and published by Springer Nature. This book was released on 2019-12-11 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises select proceedings of the International Conference on Emerging Trends in Mechanical Engineering (ICETME 2018). The book covers various topics of mechanical engineering like computational fluid dynamics, heat transfer, machine dynamics, tribology, and composite materials. In addition, relevant studies in the allied fields of manufacturing, industrial and production engineering are also covered. The applications of latest tools and techniques in the context of mechanical engineering problems are discussed in this book. The contents of this book will be useful for students, researchers as well as industry professionals.

Download or read book Geometric and Numerical Foundations of Movements written by Jean-Paul Laumond and published by Springer. This book was released on 2017-05-02 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims at gathering roboticists, control theorists, neuroscientists, and mathematicians, in order to promote a multidisciplinary research on movement analysis. It follows the workshop “ Geometric and Numerical Foundations of Movements ” held at LAAS-CNRS in Toulouse in November 2015[1]. Its objective is to lay the foundations for a mutual understanding that is essential for synergetic development in motion research. In particular, the book promotes applications to robotics --and control in general-- of new optimization techniques based on recent results from real algebraic geometry.