Download or read book Towards Neural Network Embeddings of Optimal Motion Planners written by Mayur Joseph Bency and published by . This book was released on 2018 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fast and efficient path generation is critical for robots operating in complex environments. This motion planning problem is often performed in a robot's actuation or configuration space, where popular pathfinding methods such as A*, RRT*, get exponentially more computationally expensive to execute as the dimensionality increases or the spaces become more cluttered and complex. On the other hand, if one were to save the entire set of paths connecting all pair of locations in the configuration space a priori, one would run out of memory very quickly. In this work, we introduce a novel way of producing fast and optimal motion plans by using a stepping neural network approach, called OracleNet. OracleNet uses Recurrent Neural Networks to determine end-to-end trajectories in an iterative manner that implicitly generates optimal motion plans with minimal loss in performance in a compact form. The algorithm is straightforward in implementation while consistently generating near-optimal paths in a single, iterative, end-to-end roll-out. In practice, OracleNet generally has fixed-time execution regardless of the configuration space complexity while outperforming popular pathfinding algorithms in complex environments and higher dimensions.

Download or read book Exploiting Direct Optimal Control for Motion Planning in Unstructured Environments written by Kristoffer Bergman and published by Linköping University Electronic Press. This book was released on 2021-03-16 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, motion planning for autonomous systems has become an important area of research. The high interest is not the least due to the development of systems such as self-driving cars, unmanned aerial vehicles and robotic manipulators. The objective in optimal motion planning problems is to find feasible motion plans that also optimize a performance measure. From a control perspective, the problem is an instance of an optimal control problem. This thesis addresses optimal motion planning problems for complex dynamical systems that operate in unstructured environments, where no prior reference such as road-lane information is available. Some example scenarios are autonomous docking of vessels in harbors and autonomous parking of self-driving tractor-trailer vehicles at loading sites. The focus is to develop optimal motion planning algorithms that can reliably be applied to these types of problems. This is achieved by combining recent ideas from automatic control, numerical optimization and robotics. The first contribution is a systematic approach for computing local solutions to motion planning problems in challenging unstructured environments. The solutions are computed by combining homotopy methods and direct optimal control techniques. The general principle is to define a homotopy that transforms, or preferably relaxes, the original problem to an easily solved problem. The approach is demonstrated in motion planning problems in 2D and 3D environments, where the presented method outperforms a state-of-the-art asymptotically optimal motion planner based on random sampling. The second contribution is an optimization-based framework for automatic generation of motion primitives for lattice-based motion planners. Given a family of systems, the user only needs to specify which principle types of motions that are relevant for the considered system family. Based on the selected principle motions and a selected system instance, the framework computes a library of motion primitives by simultaneously optimizing the motions and the terminal states. The final contribution of this thesis is a motion planning framework that combines the strengths of sampling-based planners with direct optimal control in a novel way. The sampling-based planner is applied to the problem in a first step using a discretized search space, where the system dynamics and objective function are chosen to coincide with those used in a second step based on optimal control. This combination ensures that the sampling-based motion planner provides a feasible motion plan which is highly suitable as warm-start to the optimal control step. Furthermore, the second step is modified such that it also can be applied in a receding-horizon fashion, where the proposed combination of methods is used to provide theoretical guarantees in terms of recursive feasibility, worst-case objective function value and convergence to the terminal state. The proposed motion planning framework is successfully applied to several problems in challenging unstructured environments for tractor-trailer vehicles. The framework is also applied and tailored for maritime navigation for vessels in archipelagos and harbors, where it is able to compute energy-efficient trajectories which complies with the international regulations for preventing collisions at sea.

Download or read book On Motion Planning Using Numerical Optimal Control written by Kristoffer Bergman and published by Linköping University Electronic Press. This book was released on 2019-05-28 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, motion planning for autonomous systems has become an important area of research. The high interest is not the least due to the development of systems such as self-driving cars, unmanned aerial vehicles and robotic manipulators. In this thesis, the objective is not only to find feasible solutions to a motion planning problem, but solutions that also optimize some kind of performance measure. From a control perspective, the resulting problem is an instance of an optimal control problem. In this thesis, the focus is to further develop optimal control algorithms such that they be can used to obtain improved solutions to motion planning problems. This is achieved by combining ideas from automatic control, numerical optimization and robotics. First, a systematic approach for computing local solutions to motion planning problems in challenging environments is presented. The solutions are computed by combining homotopy methods and numerical optimal control techniques. The general principle is to define a homotopy that transforms, or preferably relaxes, the original problem to an easily solved problem. The approach is demonstrated in motion planning problems in 2D and 3D environments, where the presented method outperforms both a state-of-the-art numerical optimal control method based on standard initialization strategies and a state-of-the-art optimizing sampling-based planner based on random sampling. Second, a framework for automatically generating motion primitives for lattice-based motion planners is proposed. Given a family of systems, the user only needs to specify which principle types of motions that are relevant for the considered system family. Based on the selected principle motions and a selected system instance, the algorithm not only automatically optimizes the motions connecting pre-defined boundary conditions, but also simultaneously optimizes the terminal state constraints as well. In addition to handling static a priori known system parameters such as platform dimensions, the framework also allows for fast automatic re-optimization of motion primitives if the system parameters change while the system is in use. Furthermore, the proposed framework is extended to also allow for an optimization of discretization parameters, that are are used by the lattice-based motion planner to define a state-space discretization. This enables an optimized selection of these parameters for a specific system instance. Finally, a unified optimization-based path planning approach to efficiently compute locally optimal solutions to advanced path planning problems is presented. The main idea is to combine the strengths of sampling-based path planners and numerical optimal control. The lattice-based path planner is applied to the problem in a first step using a discretized search space, where system dynamics and objective function are chosen to coincide with those used in a second numerical optimal control step. This novel tight combination of a sampling-based path planner and numerical optimal control makes, in a structured way, benefit of the former method’s ability to solve combinatorial parts of the problem and the latter method’s ability to obtain locally optimal solutions not constrained to a discretized search space. The proposed approach is shown in several practically relevant path planning problems to provide improvements in terms of computation time, numerical reliability, and objective function value.

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 Non learning Artificial Neural Network Approach to Real time Motion Planning for the Pioneer Robot written by David Ryan Erickson and published by . This book was released on 2003 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Mobile Robot Motion Planning written by G. N. Tripathi and published by . This book was released on 2012-10-29 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 From batch size 1 to serial production Adaptive robots for scalable and flexible production systems written by Mohamad Bdiwi and published by Frontiers Media SA. This book was released on 2023-05-24 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robot Motion Planning in Unknown Environments Using Neural Networks written by Arno Jan Knobbe and published by . This book was released on 1996 with total page 6 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "We present two approaches to the motion planning problem for car-like robots using an extended Kohonen Self-Organizing Map (SOM). No prior knowledge about the positions of obstacles is assumed. We incrementally build a path from the starting point of the robot towards the goal, using the SOM as a situation-action map. The first approach uses a trial and error strategy to train the SOM. This method is simple but is not always able to escape from dead-end situations. As an improvement a new training-algorithm is proposed that uses edge detection on the visible objects to generate possible motions. Backtracking is used to choose from different possibilities. Experiments show that this new method realizes a considerable increase in performance and speed."

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 Index to IEEE Publications written by Institute of Electrical and Electronics Engineers and published by . This book was released on 1998 with total page 1234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues for 1973- cover the entire IEEE technical literature.

Download or read book Scientific and Technical Aerospace Reports written by and published by . This book was released on 1995 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lists citations with abstracts for aerospace related reports obtained from world wide sources and announces documents that have recently been entered into the NASA Scientific and Technical Information Database.

Download or read book Advanced Planning Control and Signal Processing Methods and Applications in Robotic Systems written by Zhan Li and published by Frontiers Media SA. This book was released on 2022-02-22 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Reinforcement Learning and Optimal Control written by Dimitri P. Bertsekas and published by . This book was released on 2020 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Bio Inspired Robotics written by Toshio Fukuda and published by MDPI. This book was released on 2018-11-07 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Bio-Inspired Robotics" that was published in Applied Sciences

Download or read book Documentation Abstracts written by and published by . This book was released on 1997 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.