Potentially Guided Bidirectionalized RRT* for Fast Optimal Path Planning in Cluttered Environments

TL;DR

This paper introduces PIB-RRT* and PB-RRT*, novel bidirectional RRT* variants with potential guidance, significantly enhancing convergence speed and memory efficiency in cluttered environments for optimal path planning.

Contribution

The paper proposes potentially guided bidirectional RRT* algorithms that improve convergence rate and memory use, addressing limitations of existing bidirectional RRT* variants in cluttered spaces.

Findings

Significant improvement in convergence rate over existing methods.

Enhanced efficiency and memory utilization demonstrated through experiments.

Proven effectiveness in complex, cluttered environments.

Abstract

Rapidly-exploring Random Tree star (RRT*) has recently gained immense popularity in the motion planning community as it provides a probabilistically complete and asymptotically optimal solution without requiring the complete information of the obstacle space. In spite of all of its advantages, RRT* converges to an optimal solution very slowly. Hence to improve the convergence rate, its bidirectional variants were introduced, the Bi-directional RRT* (B-RRT*) and Intelligent Bi-directional RRT* (IB-RRT*). However, as both variants perform pure exploration, they tend to suffer in highly cluttered environments. In order to overcome these limitations, we introduce a new concept of potentially guided bidirectional trees in our proposed Potentially Guided Intelligent Bi-directional RRT* (PIB-RRT*) and Potentially Guided Bi-directional RRT* (PB-RRT*). The proposed algorithms greatly improve the convergence rate and have a more efficient memory utilization. Theoretical and experimental evaluation of the proposed algorithms have been made and compared to the latest state of the art motion planning algorithms under different challenging environmental conditions and have proven their remarkable improvement in efficiency and convergence rate.