Multi Layer Approach To Motion Planning In Obstacle Rich Environment
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Multi-layer Approach to Motion Planning in Obstacle Rich Environment
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.
Applications of Computational Methods in Manufacturing and Product Design
Author: B. B. V. L. Deepak
language: en
Publisher: Springer Nature
Release Date: 2022-05-04
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.
Selected papers from the 2nd International Symposium on UAVs, Reno, U.S.A. June 8-10, 2009
Author: Kimon P. Valavanis
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-04-11
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.