Download or read book Probabilistic Motion Planning and Optimization Incorporating Chance Constraints written by Siyu Dai (S.M.) and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: For high-dimensional robots, motion planning is still a challenging problem, especially for manipulators mounted to underwater vehicles or human support robots where uncertainties and risks of plan failure can have severe impact. However, existing risk-aware planners mostly focus on low-dimensional planning tasks, meanwhile planners that can account for uncertainties and react fast in high degree-of-freedom (DOF) robot planning tasks are lacking. In this thesis, a risk-aware motion planning and execution system called Probabilistic Chekov (p-Chekov) is introduced, which includes a deterministic stage and a risk-aware stage. A systematic set of experiments on existing motion planners as well as p-Chekov is also presented. The deterministic stage of p-Chekov leverages the recent advances in obstacle-aware trajectory optimization to improve the original tube-based-roadmap Chekov planner. Through experiments in 4 common application scenarios with 5000 test cases each, we show that using sampling-based planners alone on high DOF robots can not achieve a high enough reaction speed, whereas the popular trajectory optimizer TrajOpt with naive straight-line seed trajectories has very high collision rate despite its high planning speed. To the best of our knowledge, this is the first work that presents such a systematic and comprehensive evaluation of state-of-the-art motion planners, which are based on a significant amount of experiments. We then combine different stand-alone planners with trajectory optimization. The results show that the deterministic planning part of p-Chekov, which combines a roadmap approach that caches the all pair shortest paths solutions and an online obstacle-aware trajectory optimizer, provides superior performance over other standard sampling-based planners' combinations. Simulation results show that, in typical real-life applications, this "roadmap + TrajOpt" approach takes about 1 s to plan and the failure rate of its solutions is under 1%. The risk-aware stage of p-Chekov accounts for chance constraints through state probability distribution and collision probability estimation. Based on the deterministic Chekov planner, p-Chekov incorporates a linear-quadratic Gaussian motion planning (LQG-MP) approach into robot state probability distribution estimation, applies quadrature-sampling theories to collision risk estimation, and adapts risk allocation approaches for chance constraint satisfaction. It overcomes existing risk-aware planners' limitation in real-time motion planning tasks with high-DOF robots in 3- dimensional non-convex environments. The experimental results in this thesis show that this new risk-aware motion planning and execution system can effectively reduce collision risk and satisfy chance constraints in typical real-world planning scenarios for high-DOF robots. This thesis makes the following three main contributions: (1) a systematic evaluation of several state-of-the-art motion planners in realistic planning scenarios, including popular sampling-based motion planners and trajectory optimization type motion planners, (2) the establishment of a "roadmap + TrajOpt" deterministic motion planning system that shows superior performance in many practical planning tasks in terms of solution feasibility, optimality and reaction time, and (3) the development of a risk-aware motion planning and execution system that can handle high-DOF robotic planning tasks in 3-dimensional non-convex environments.

Download or read book Probabilistic and Randomized Methods for Design under Uncertainty written by Giuseppe Calafiore and published by Springer Science & Business Media. This book was released on 2006-03-06 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Probabilistic and Randomized Methods for Design under Uncertainty is a collection of contributions from the world’s leading experts in a fast-emerging branch of control engineering and operations research. The book will be bought by university researchers and lecturers along with graduate students in control engineering and operational research.

Download or read book Planning Under Uncertainty in Resource constrained Systems written by Daniel DeWitt Strawser and published by . This book was released on 2019 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: As autonomous systems become integrated into the real world, planning under uncertainty is a critical task. The real world is incredibly complex and systems must reason about factors such as uncertainty in their movements, environments, and human behavior. In the face of this uncertainty, agents must compute control trajectories and policies that enable them to maximize their expected performance while respecting probabilities on mission failure. The task is difficult because systems must reason about large numbers of scenarios concerning what may happen. Compounding the difficulty, many systems must reason about uncertainty while being resource-constrained. Autonomous cars and robots are time-limited because they must react quickly to their environment. Applications such as smart grids are computationally-limited because they require low-cost hardware in order to keep energy cheap. Because of its importance to real world applications, a large amount of work has been devoted to planning under uncertainty. However, few applications focus on the resource-limited case. In time-constrained applications such as motion planning under uncertainty, methods typically focus on simplistic cost functions and models of the environment, dynamics, and stochasticity. In computation-constrained applications such as resource allocation for smart grids, approaches require computationally-intensive solvers that are unsuitable when system cost must be kept to a minimum. In both cases, the prior art is inadequate for real world demands. In this thesis, I develop a series of algorithms that enable resource-constrained systems to better plan under uncertainty. First, my work enables time-constrained systems to better approximate expected cost, search in non-convex regions, and reason about more complicated environmental geometries and agent dynamics. Additionally, I propose algorithms that enable resource allocation under uncertainty on ultra low-cost hardware by distributing computation and approximating state uncertainty through a discretization. In the time-constrained case, I model the problem of motion planning under uncertainty as a hybrid search that consists of an upper level region planner and a lower level motion planner. First, I present a method for quickly computing expected path cost and that is able to vary the precision with which the stochastic model is evaluated as required by the search. Next, I present the Fast Obstacle eXploration (FOX) algorithm that quickly generates a constraint graph for the region planner from complex obstacle geometries. I present a method for integrating CDF-based chance constraints with FOX as well as a method to model the collision probability of agents with non-trivial geometry. The latter algorithm transforms a complex problem in numerical integration into a simpler problem in computational geometry; importantly, the algorithm allows for massive parallelization on GPUs. Finally, this part concludes with the Shooting Method Monte Carlo (SMMC) algorithm. This algorithm models the chance constraint of dynamical systems with non-Gaussian state uncertainty. SMMC combines a shooting trajectory optimization with Monte Carlo simulation to approximate the collision probability for nonlinear dynamical systems. In the computation-constrained case, I develop a set of resource-allocation algorithms that are able to reason about uncertainty while being implementable on ultra low-cost hardware. A market for reliability algorithm is presented that solves resource allocation under uncertainty by allowing a distributed set of agents to bid for reliability. A centralized planner is also presented where the resource allocation problem is modeled as a Markov Decision Process and a stochastic local search used to compute good policies. The approaches for resource-constrained planning under uncertainty are bench-marked against a variety of test cases. In the motion planning domain, test cases are presented using linear dynamical systems, a Dubins car model, and a Slocum glider AUV. The method for bounding expected cost is shown to perform well against a sampling-based approach. The approach to dealing with complex obstacle geometry is shown to reach better solutions more quickly than a sampling-based RRT approach and disjunctive linear program. The sampling-based collocation method is shown to better approximate trajectory risk in simulation than a CDF-based model. Likewise, Shooting Method Monte Carlo is shown to better approximate trajectory risk than a sampling-based collocation method. In both sampling-based cases, GPU parallelization is shown to help scale computation of the chance constraint to large numbers of samples versus CPU-based computation. In the resource allocation case, the distributed and centralized contingency planners are benchmarked against mixed integer linear programs (MILP) and are shown to achieve comparable performance with a much smaller computational footprint.

Download or read book Advanced Trajectory Optimization Guidance and Control Strategies for Aerospace Vehicles written by Runqi Chai and published by Springer Nature. This book was released on 2023-10-29 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the design and application of advanced trajectory optimization and guidance and control (G&C) techniques for aerospace vehicles. Part I of the book focuses on the introduction of constrained aerospace vehicle trajectory optimization problems, with particular emphasis on the design of high-fidelity trajectory optimization methods, heuristic optimization-based strategies, and fast convexification-based algorithms. In Part II, various optimization theory/artificial intelligence (AI)-based methods are constructed and presented, including dynamic programming-based methods, model predictive control-based methods, and deep neural network-based algorithms. Key aspects of the application of these approaches, such as their main advantages and inherent challenges, are detailed and discussed. Some practical implementation considerations are then summarized, together with a number of future research topics. The comprehensive and systematic treatment of practical issues in aerospace trajectory optimization and guidance and control problems is one of the main features of the book, which is particularly suitable for readers interested in learning practical solutions in aerospace trajectory optimization and guidance and control. The book is useful to researchers, engineers, and graduate students in the fields of G&C systems, engineering optimization, applied optimal control theory, etc.

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 Tuning Based Algorithms for Chance Constrained Optimization in Power Systems Applications written by Ashley Mimi Hou and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: As climate change unfolds, the path to a more sustainable future necessitates the development and effective integration of renewable energy sources, such as wind and solar generation, into existing power grids. However, the inherent intermittency, variability, and unpredictability of renewable generation pose critical challenges to the reliability of power systems operation. This dissertation addresses the technical considerations of explicitly incorporating these uncertainties into power systems operational planning via the development of chance-constrained optimal power flow (OPF) methods. Chance-constrained models guarantee system security by limiting the violation probability of uncertain constraints. Existing methods often face a trade off between solution quality, feasibility, and computational tractability. This thesis addresses this trade off by proposing a data-driven tuning-based algorithm that obtains desirable solutions to chance-constrained problems by using feedback from sample-based evaluations to iteratively adapt simple, approximate optimization models. We further develop a theoretical framework for obtaining probabilistic feasibility guarantees for general tuning-based stochastic optimization methods by proposing a two-step tuning methodology that adds an a posteriori solution verification step. Not only does our method retain computational tractability by decoupling the solving of the optimization problem from the consideration of the uncertainty, but it also provides guarantees on solution feasibility without introducing excess conservatism or relying on assumptions on the uncertainty distribution. Our tuning method is applied to two variants of the OPF problem. We identify approximate models, solution evaluation procedures, and tuning schemes suitable for the structure and properties of each problem setting. We first consider a DC linearized formulation of OPF for dispatching generation in transmission grids under renewable generation and load forecast uncertainty. Here, existing analytical chance constraint reformulations are leveraged and a bisection search is used to iteratively tune an identified single-dimensional safety parameter. In a case study, we observe the method achieves non-conservative solutions that meet desired violation probabilities for not only the single chance-constrained case, but also the significantly more challenging joint chance-constrained case. We then shift focus to the distribution grid setting, where uncertainty arises from distributed energy resources (DERs) and load variability. In this setting, the three-phase unbalanced AC OPF problem is used to find optimal reactive power set points for solar PV inverters while minimizing voltage unbalance. For the single-chance constrained problem, we adapt the aforementioned approximate model and bisection search heuristic to this more complicated setting, where the uncertainty enters the constraints non-linearly and implicitly. The case studies use realistic solar PV and load data to demonstrate that while the algorithm is able to enforce single chance constraints, the use of a single-dimensional tuning parameter is insufficiently flexible for the joint chance-constrained case. We subsequently propose a risk allocation-based scheme that enables multi-dimensional tuning. Empirical results demonstrate that the method successfully limits the joint violation probability as well as achieves probabilistic feasibility guarantees when used as a component of the two-step tuning methodology.

Download or read book Chance constrained Path Planning in Unstructured Environments written by Rachit Aggarwal and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The objective of this dissertation is to develop a framework for chance-constrained path planning in autonomous agents operating in evolving unstructured environments. Path Planning is an important problem in many fields such as robotic manipulators, mobile robotics, scheduling, flight planning, and autonomous cars and aircraft. Often, the presence of external disturbances, measurement errors and/or inadequately modeled processes in the environment can cause uncertainty in characterization of the obstacles' shape, size and location. Traditionally, such unstructured environments are typically modeled using conservative safety margins and posed as constraints or included in the cost function as a penalty. There exist no systematic methods to tune the margins or the cost function with disparate physical meaning, e.g. travel time and safety margin. In this work, the inherent uncertainty in the obstacles is posed as chance-constraints (CC) bounded by the risk of violation of those constraints in an optimal control problem for path planning. Pseudospectral discretization methods are used to transcribe the optimal control problem to a nonlinear program (NLP) which is solved using off-the-shelf optimization solvers. The constrained optimization problems are heavily dependent on a suitable initial guess provided to the solver, which affects both the computation time and optimality of the solution. Triangulation and grid based discrete optimization methods are studied for their merits and employed to generate the initial guesses. It is shown that by varying the risk of violation of obstacle boundaries, a family of solutions can be generated signifying the risk associated with each solution. This approach enables the decision maker to be 'risk-aware' by providing the methodical approach to undertake missions based-on its 'risk-appetite' in the given situation. This idea is then extended to recursive planning for evolving environments. An in-depth example for path planning for small unmanned aerial vehicles (UAVs) flying in a spreading wildfire for situational awareness is studied. An extension to multi-agent operations is also developed. To validate the efficacy of the path planner in real wildfire, a modular multirotor experimental testbed was designed and developed. Field tests demonstrate the validation of the design goals and several performance objectives.

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 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 Robot Motion Planning and Control written by Jean-Paul Laumond and published by Springer. This book was released on 1998 with total page 366 pages. Available in PDF, EPUB and Kindle. Book excerpt: Content Description #Includes bibliographical references.

Download or read book Probability based Path Planning for Stochastic Nonholonomic Systems with Obstacle Avoidance written by Jianping Lin and published by . This book was released on 2015 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt: There exist various path planning methods in robotics. The Probabilistic Roadmap and Rapidly-exploring Random Tree (RRT) became popular in recent decades. It is known that the RRT is more suitable for nonholonomic systems. The RRT is a sampling-based algorithm which is designed for path planning problem and is efficient to handle high-dimensional configuration space (C-space) and nonholonomic constraints. Under the constraints, the RRT can generate paths between an initial state and a goal state while avoiding obstacles. However it does not guarantee that the resulting path is optimal. In systems with stochasticity, targeting error and closeness of the obstacle to the planned path can be considered to obtain the optimal path. In this thesis, the targeting error is defined as the root-mean-square (RMS) distance from the path samples to the desired target and the closeness is defined as probability of obstacle collision. Then, a cost function is defined as a sum of the targeting error and the obstacle closeness, and numerically minimized to find the path. The RRT result serves as an initial starting point for this subsequent optimization.

Download or read book Motion Planning in Dynamic Environments written by Kikuo Fujimura and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Advanced Vehicle Control written by Johannes Edelmann and published by CRC Press. This book was released on 2016-12-19 with total page 726 pages. Available in PDF, EPUB and Kindle. Book excerpt: The AVEC symposium is a leading international conference in the fields of vehicle dynamics and advanced vehicle control, bringing together scientists and engineers from academia and automotive industry. The first symposium was held in 1992 in Yokohama, Japan. Since then, biennial AVEC symposia have been established internationally and have considerably contributed to the progress of technology in automotive research and development. In 2016 the 13th International Symposium on Advanced Vehicle Control (AVEC’16) was held in Munich, Germany, from 13th to 16th of September 2016. The symposium was hosted by the Munich University of Applied Sciences. AVEC’16 puts a special focus on automatic driving, autonomous driving functions and driver assist systems, integrated control of interacting control systems, controlled suspension systems, active wheel torque distribution, and vehicle state and parameter estimation. 132 papers were presented at the symposium and are published in these proceedings as full paper contributions. The papers review the latest research developments and practical applications in highly relevant areas of vehicle control, and may serve as a reference for researchers and engineers.

Download or read book Proceedings of 2022 International Conference on Autonomous Unmanned Systems ICAUS 2022 written by Wenxing Fu and published by Springer Nature. This book was released on 2023-03-10 with total page 3985 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes original, peer-reviewed research papers from the ICAUS 2022, which offers a unique and interesting platform for scientists, engineers and practitioners throughout the world to present and share their most recent research and innovative ideas. The aim of the ICAUS 2022 is to stimulate researchers active in the areas pertinent to intelligent unmanned systems. The topics covered include but are not limited to Unmanned Aerial/Ground/Surface/Underwater Systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial Intelligence Algorithm/Bionic Technology and Its Application in Unmanned Systems. The papers showcased here share the latest findings on Unmanned Systems, Robotics, Automation, Intelligent Systems, Control Systems, Integrated Networks, Modeling and Simulation. It makes the book a valuable asset for researchers, engineers, and university students alike.

Download or read book Field and Service Robotics written by Shin'ichi Yuta and published by Springer. This book was released on 2006-07-11 with total page 543 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique collection is the post-conference proceedings of the 4th "International Conference on Field and Service Robotics" (FSR). This book has authoritative contributors and presents current developments and new directions in field and service robotics. The book represents a cross-section of the current state of robotics research from one particular aspect: field and service applications, and how they reflect on the theoretical basis of subsequent developments.

Download or read book 2021 21st International Conference on Control Automation and Systems ICCAS written by and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.