[PDF] Collision Avoidance Techniques And Optimal Synthesis For Motion Planning Applications eBook

Author : Andrei Marchidan
Publisher :
Page : 0 pages
File Size : 16,99 MB
Release : 2019
Category :
ISBN :

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This dissertation focuses on the problem of motion planning for autonomous agents that are required to perform fast and reactive maneuvers. In realistic situations, this problem needs to be solved in real-time for environments that are both dynamic and partially known. The success of the provided motion plans also relies on the agent’s ability to accurately perform the prescribed maneuvers and, as such, consideration of the input constraints is often times necessary. The problem can be posed in two different ways: as a controllability problem, where trajectory generation is only concerned with satisfying the given boundary conditions, system constraints (dynamic and input constraints) and state constraints (forbidden areas in the state space); or as an optimal control problem, where the trajectory is also required to optimize some performance measure. The main contributions of this dissertation are two-fold. First, a new numerical technique is proposed for solving time-optimal control problems for an agent moving in a spatiotemporal drift field. The solution technique computes the minimum time function and the corresponding time-optimal feedback control law, while using an extremal front expansion procedure to filter out sub-optimal solutions. This methodology can be applied for a rich class of time-optimal control problems where the control input structure is determined by a parameter family of differential equations. To demonstrate its applicability, the numerical technique is implemented for the Zermelo navigation problem on a sphere and for the steering problem of a self-propelled particle in a flow field. Next, in the second part of this dissertation, the controllability problem in the presence of obstacles can be solved using local reactive collision avoidance vector fields. The proposed approach uses the concept of local parametrized guidance vector fields that are generated directly from the agent model and encode collision avoidance behaviors. Their generation relies on a decomposition of agent kinematics and on a proximity-based velocity modulation determined by specific eigenvalue functions. Further exploiting the modulation properties arising from the nature of these eigenvalue functions, curvature constraints can be guaranteed. Closed-form steering laws are determined in accordance with the computed collision avoidance vector fields and can provide the necessary avoidance maneuvers to guarantee problem feasibility. Throughout this dissertation, examples and simulation results in different types of environments are presented and discussed. In the final part of this dissertation, the motion planning problem is tackled for more complex environments. The two proposed methodologies for optimal control and for collision avoidance are combined to yield a hybrid controller that generates near-optimal feasible plans in the presence of multiple static and moving obstacles and of spatiotemporal drift fields

Author : Jie Ji
Publisher : Springer Nature
Page : 144 pages
File Size : 14,77 MB
Release : 2022-06-01
Category : Technology & Engineering
ISBN : 303101507X

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In recent years, the control of Connected and Automated Vehicles (CAVs) has attracted strong attention for various automotive applications. One of the important features demanded of CAVs is collision avoidance, whether it is a stationary or a moving obstacle. Due to complex traffic conditions and various vehicle dynamics, the collision avoidance system should ensure that the vehicle can avoid collision with other vehicles or obstacles in longitudinal and lateral directions simultaneously. The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via Forward Collision Warning (FCW), Brake Assist System (BAS), and Autonomous Emergency Braking (AEB), which has been commercially applied in many new vehicles launched by automobile enterprises. But in lateral motion direction, it is necessary to determine a flexible collision avoidance path in real time in case of detecting any obstacle. Then, a path-tracking algorithm is designed to assure that the vehicle will follow the predetermined path precisely, while guaranteeing certain comfort and vehicle stability over a wide range of velocities. In recent years, the rapid development of sensor, control, and communication technology has brought both possibilities and challenges to the improvement of vehicle collision avoidance capability, so collision avoidance system still needs to be further studied based on the emerging technologies. In this book, we provide a comprehensive overview of the current collision avoidance strategies for traditional vehicles and CAVs. First, the book introduces some emergency path planning methods that can be applied in global route design and local path generation situations which are the most common scenarios in driving. A comparison is made in the path-planning problem in both timing and performance between the conventional algorithms and emergency methods. In addition, this book introduces and designs an up-to-date path-planning method based on artificial potential field methods for collision avoidance, and verifies the effectiveness of this method in complex road environment. Next, in order to accurately track the predetermined path for collision avoidance, traditional control methods, humanlike control strategies, and intelligent approaches are discussed to solve the path-tracking problem and ensure the vehicle successfully avoids the collisions. In addition, this book designs and applies robust control to solve the path-tracking problem and verify its tracking effect in different scenarios. Finally, this book introduces the basic principles and test methods of AEB system for collision avoidance of a single vehicle. Meanwhile, by taking advantage of data sharing between vehicles based on V2X (vehicle-to-vehicle or vehicle-to-infrastructure) communication, pile-up accidents in longitudinal direction are effectively avoided through cooperative motion control of multiple vehicles.

Author : Steven M. LaValle
Publisher : Cambridge University Press
Page : 844 pages
File Size : 43,11 MB
Release : 2006-05-29
Category : Computers
ISBN : 9780521862059

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Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. Written for computer scientists and engineers with interests in artificial intelligence, robotics, or control theory, this is the only book on this topic that tightly integrates a vast body of literature from several fields into a coherent source for teaching and reference in a wide variety of applications. Difficult mathematical material is explained through hundreds of examples and illustrations.

Author : Meiping Wu
Publisher : Springer Nature
Page : 3575 pages
File Size : 19,85 MB
Release : 2022-03-18
Category : Technology & Engineering
ISBN : 9811694923

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This book includes original, peer-reviewed research papers from the ICAUS 2021, 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 2021 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.

Author : Emilio Frazzoli
Publisher : Springer
Page : 625 pages
File Size : 47,70 MB
Release : 2013-02-14
Category : Technology & Engineering
ISBN : 3642362796

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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.

Author : Cherif Ahrikencheikh
Publisher : Wiley-Interscience
Page : 400 pages
File Size : 25,52 MB
Release : 1994-10-14
Category : Science
ISBN :

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The first handbook to the practical specifics of motion planning, Optimized-Motion Planning offers design engineers methods and insights for solving real motion planning problems in a 3-dimensional space. Complete with a disk of software programs, this unique guide allows users to design, test, and implement possible solutions, useful in a host of contexts, especially tool path planning. Beginning with a brief overview of the general class of problems examined within the book as well as available solution techniques, Part 1 familiarizes the reader with the conceptual threads that underlie each approach. This early discussion also considers the specific applications of each technique as well as its computational efficiency. Part 2 illustrates basic problem-solving methodology by considering the case of a point moving between stationary polygons in a plane. This section features algorithms for data organization and storage, the concepts of passage networks and feasibility charts, as well as the path optimization algorithm. Elaborating on the problematic model described in Part 2, Part 3 develops an algorithm for optimizing the motion of a point between stationary polyhedra in a 3-dimensional space. This algorithm is first applied to the case of nonpoint objects moving between obstacles that can be stationary or moving with known patterns. It's then used in connection with the extensively investigated problem of motion planning for multilink manipulators.

Author : Steven M. LaValle
Publisher : Springer Nature
Page : 581 pages
File Size : 41,49 MB
Release : 2021-02-08
Category : Technology & Engineering
ISBN : 3030667235

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This proceedings book helps bring insights from this array of technical sub-topics together, as advanced robot algorithms draw on the combined expertise of many fields—including control theory, computational geometry and topology, geometrical and physical modeling, reasoning under uncertainty, probabilistic algorithms, game theory, and theoretical computer science. Intelligent robots and autonomous systems depend on algorithms that efficiently realize functionalities ranging from perception to decision making, from motion planning to control. The works collected in this SPAR book represent the state of the art in algorithmic robotics. They originate from papers accepted to the 14th International Workshop on the Algorithmic Foundations of Robotics (WAFR), traditionally a biannual, single-track meeting of leading researchers in the field of robotics. WAFR has always served as a premiere venue for the publication of some of robotics’ most important, fundamental, and lasting algorithmic contributions, ensuring the rapid circulation of new ideas. Though an in-person meeting was planned for June 15–17, 2020, in Oulu, Finland, the event ended up being canceled owing to the infeasibility of international travel during the global COVID-19 crisis.

Author : Murat Cubuktepe
Publisher :
Page : 456 pages
File Size : 11,59 MB
Release : 2021
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ISBN :

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This dissertation studies the applicability of convex optimization to the formal verification and synthesis of systems that exhibit randomness or stochastic uncertainties. These systems can be represented by a general family of uncertain, partially observable, and parametric Markov decision processes (MDPs). These models have found applications in artificial intelligence, planning, autonomy, and control theory and can accurately characterize dynamic, uncertain environments. The synthesis of policies for this family of models has long been regarded theoretically and empirically intractable. The goal of this dissertation is to develop theoretically sound and computationally efficient synthesis algorithms that provably satisfy formal high-level task specifications in temporal logic. The first part is on developing convex-optimization-based techniques to parameter synthesis in parametric Markov decision processes where the values of the transitions are functions over real-valued parameters. The second part builds on the formulations of the first part and utilizes sampling-based methods for verification and optimization in uncertain MDPs that allow the probabilistic transition function to belong to a so-called uncertainty set. The third part develops inverse reinforcement learning algorithms in partially observable MDPs to several limitations of existing techniques that do not take the information asymmetry between the expert and the agent into account. Finally, the fourth part synthesizes policies for uncertain partially observable MDPs that allow both of the probabilistic transition and observation functions to be uncertain. In each part, a unifying theme is, the resulting algorithms approximate the underlying optimization problem as a convex optimization problem. Additionally, by combining techniques from convex optimization and formal methods, the algorithms bring strong performance guarantees with respect to task specifications. The computational efficiency and applicability of the resulting algorithms are demonstrated in numerous domains such as aircraft collision avoidance, spacecraft and unmanned aerial vehicle motion planning, and joint active perception and planning in urban environments