Potential functions based sampling heuristic for optimal path planning

Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slo...

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Bibliographic Details
Published in:Autonomous robots Vol. 40; no. 6; pp. 1079 - 1093
Main Authors: Qureshi, Ahmed Hussain, Ayaz, Yasar
Format: Journal Article
Language:English
Published: New York Springer US 01.08.2016
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Collision avoidance
Computer Imaging
Computer simulation
Control
Convergence
Engineering
Mechatronics
Path planning
Pattern Recognition and Graphics
Potential fields
Robotics
Robotics and Automation
Vision
Motion planning
Artificial potential fields
Sampling based algorithms
Convergence rate
Optimal path planning
ISSN:0929-5593, 1573-7527
Online Access:Get full text
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Summary:Rapidly-exploring Random Tree star (RRT*) is a recently proposed extension of Rapidly-exploring Random Tree (RRT) algorithm that provides a collision-free, asymptotically optimal path regardless of obstacles geometry in a given environment. However, one of the limitation in the RRT* algorithm is slow convergence to optimal path solution. As a result it consumes high memory as well as time due to the large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the potential function based-RRT* that incorporates the artificial potential field algorithm in RRT*. The proposed algorithm allows a considerable decrease in the number of iterations and thus leads to more efficient memory utilization and an accelerated convergence rate. In order to illustrate the usefulness of the proposed algorithm in terms of space execution and convergence rate, this paper presents rigorous simulation based comparisons between the proposed techniques and RRT* under different environmental conditions. Moreover, both algorithms are also tested and compared under non-holonomic differential constraints.
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ISSN:0929-5593
1573-7527
DOI:10.1007/s10514-015-9518-0