Potential Functions based Sampling Heuristic For Optimal Path Planning

TL;DR

This paper introduces P-RRT*, a new sampling heuristic that integrates potential functions into RRT* to improve convergence speed and efficiency in optimal path planning, especially under complex constraints.

Contribution

The paper proposes P-RRT*, a novel algorithm combining potential functions with RRT* to enhance convergence and reduce computational resources in path planning.

Findings

P-RRT* converges faster than RRT* in various environments.

P-RRT* uses fewer iterations and less memory.

The algorithm performs well under non-holonomic constraints.

Abstract

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 obstacle's geometry in a given environment. However, one of the limitations in the RRT* algorithm is slow convergence to optimal path solution. As a result, it consumes high memory as well as time due to a large number of iterations utilised in achieving optimal path solution. To overcome these limitations, we propose the Potential Function Based-RRT* (P-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.

Full text

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