[PDF] Multi Layer Approach To Motion Planning In Obstacle Rich Environment eBook

Author : Sung Hyun Kim
Publisher :
Page : pages
File Size : 44,89 MB
Release : 2010
Category :
ISBN :

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A widespread use of robotic technology in civilian and military applications has generated a need for advanced motion planning algorithms that are real-time implementable. These algorithms are required to navigate autonomous vehicles through obstacle-rich environments. This research has led to the development of the multilayer trajectory generation approach. It is built on the principle of separation of concerns, which partitions a given problem into multiple independent layers, and addresses complexity that is inherent at each level. We partition the motion planning algorithm into a roadmap layer and an optimal control layer. At the roadmap layer, elements of computational geometry are used to process the obstacle rich environment and generate feasible sets. These are used by the optimal control layer to generate trajectories while satisfying dynamics of the vehicle. The roadmap layer ignores the dynamics of the system, and the optimal control layer ignores the complexity of the environment, thus achieving a separation of concern. This decomposition enables computationally tractable methods to be developed for addressing motion planning in complex environments. The approach is applied in known and unknown environments. The methodology developed in this thesis has been successfully applied to a 6 DOF planar robotic testbed. Simulation results suggest that the planner can generate trajectories that navigate through obstacles while satisfying dynamical constraints.

Author : Kimon P. Valavanis
Publisher : Springer Science & Business Media
Page : 519 pages
File Size : 43,18 MB
Release : 2011-04-11
Category : Technology & Engineering
ISBN : 9048187648

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In the last decade, signi?cant changes have occurred in the ?eld of vehicle motion planning, and for UAVs in particular. UAV motion planning is especially dif?cult due to several complexities not considered by earlier planning strategies: the - creased importance of differential constraints, atmospheric turbulence which makes it impossible to follow a pre-computed plan precisely, uncertainty in the vehicle state, and limited knowledge about the environment due to limited sensor capabilities. These differences have motivated the increased use of feedback and other control engineering techniques for motion planning. The lack of exact algorithms for these problems and dif?culty inherent in characterizing approximation algorithms makes it impractical to determine algorithm time complexity, completeness, and even soundness. This gap has not yet been addressed by statistical characterization of experimental performance of algorithms and benchmarking. Because of this overall lack of knowledge, it is dif?cult to design a guidance system, let alone choose the algorithm. Throughout this paper we keep in mind some of the general characteristics and requirements pertaining to UAVs. A UAV is typically modeled as having velocity and acceleration constraints (and potentially the higher-order differential constraints associated with the equations of motion), and the objective is to guide the vehicle towards a goal through an obstacle ?eld. A UAV guidance problem is typically characterized by a three-dimensional problem space, limited information about the environment, on-board sensors with limited range, speed and acceleration constraints, and uncertainty in vehicle state and sensor data.

Author : B. B. V. L. Deepak
Publisher : Springer Nature
Page : 663 pages
File Size : 19,55 MB
Release : 2022-05-04
Category : Technology & Engineering
ISBN : 9811902968

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This book presents the select proceedings of the conference of Innovative Product Design and Intelligent Manufacturing System (IPDIMS 2020), held at the National Institute of Technology, Rourkela, India. The book addresses latest methods and advanced tools from different areas of design and manufacturing technology. The main topics covered include computational methods for robotics, mechatronics and human-computer interaction; computer-aided design, manufacturing and engineering; aesthetics, ergonomics and UX/UI design; smart manufacturing and expert systems. The contents of this book will be useful for researchers as well as professionals working in the areas of industrial design, mechatronics, robotics, and automation.

Author : A. Frank van der Stappen
Publisher :
Page : 40 pages
File Size : 38,69 MB
Release : 1995
Category : Computational complexity
ISBN :

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Abstract: "We present a simple and efficient paradigm for computing the exact solution of the motion planning problem in environments with a low obstacle density. Such environments frequently occur in practical instances of the motion planning problem. The complexity of the free space for such environments is known to be linear in the number of obstacles. Our paradigm is a new cell decomposition approach to motion planning and exploits properties that follow from the low density of the obstacles in the robot's workspace. These properties allow us to decompose the workspace, subject to some constraints, rather than to decompose the higher-dimensional free configuration space directly. A sequence of uniform steps transforms the workspace decomposition into a free space decomposition of asympototically the same size. The approach applies to robots with any fixed number of degrees of freedom and turns out to be eifficient in many cases: it leads to nearly optimal O(n log n) algorithms for motion planning in 2D, for motion planning in 3D amidst obstacles of comparable size, and for motion planning on a planar workfloor in 3D. In addition, we obtain algorithms for planning 3D motions among polyhedral obstacles, running in O(n2 log n) time, and among arbitrary obstacles, running in time O(n3). Several other interesting instances of the motion planning seem adequately solvable by the paradigm as well."

Author : Kikuo Fujimura
Publisher :
Page : 27 pages
File Size : 43,85 MB
Release : 1992
Category : Robotics
ISBN :

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Abstract: "Motion planning in the presence of time-dependent obstacles is studied. Much of the prior approaches to motion planning problems assume that the obstacles always exist in the environment, whether they are stationary or in motion. In this paper, we consider the case that the environment contains obstacles whose existing periods are dependent on time, i.e., they can appear and disappear in the environment. This formulation allows us to model a variety of time-varying situations that can arise in application domains. A concept similar to that of visibility is used to solve the problem. An algorithm is presented to generate a motion in such a dynamic domain, its time-minimality is proved, and computation time is analyzed."

Author : Kikuo Fujimura
Publisher : Springer Science & Business Media
Page : 190 pages
File Size : 17,86 MB
Release : 2012-12-06
Category : Computers
ISBN : 4431681655

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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.

Author : Aude Billard
Publisher : MIT Press
Page : 425 pages
File Size : 28,74 MB
Release : 2022-02-08
Category : Technology & Engineering
ISBN : 0262367017

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Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.

Author : Xiangrong Xu
Publisher :
Page : pages
File Size : 46,2 MB
Release : 2018
Category : Science
ISBN :

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This chapter introduces two kinds of motion path planning algorithms for mobile robots or unmanned ground vehicles (UGV). First, we present an approach of trajectory planning for UGV or mobile robot under the existence of moving obstacles by using improved artificial potential field method. Then, we propose an I-RRT* algorithm for motion planning, which combines the environment with obstacle constraints, vehicle constraints, and kinematic constraints. All the simulation results and the experiments show that two kinds of algorithm are effective for practical use.

Author : Hua-Tian Li
Publisher : Elsevier
Page : 127 pages
File Size : 42,40 MB
Release : 2014-07-04
Category : Technology & Engineering
ISBN : 1483298337

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Papers presented at the workshop are representative of the state-of-the art of artificial intelligence in real-time control. The issues covered included the use of AI methods in the design, implementation, testing, maintenance and operation of real-time control systems. While the focus was on the fundamental aspects of the methodologies and technologies, there were some applications papers which helped to put emerging theories into perspective. The four main subjects were architectural issues; knowledge - acquisition and learning; techniques; and scheduling, monitoring and management.

Author : Shaoshan Liu
Publisher : Morgan & Claypool Publishers
Page : 285 pages
File Size : 31,66 MB
Release : 2017-10-25
Category : Computers
ISBN : 1681731673

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This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.