Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments

The sampling-based motion planning algorithm known as Rapidly-exploring Random Trees (RRT) has gained the attention of many researchers due to their computational efficiency and effectiveness. Recently, a variant of RRT called RRT* has been proposed that ensures asymptotic optimality. Subsequently i...

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Bibliographic Details
Published in:Robotics and autonomous systems Vol. 68; pp. 1 - 11
Main Authors: Qureshi, Ahmed Hussain, Ayaz, Yasar
Format: Journal Article
Language:English
Published: Elsevier B.V 01.06.2015
Subjects:
Bidirectional trees
Motion planning
Optimal path planning
RRT
Sampling-based algorithms
Motion planning
Optimal path planning
RRT
Sampling-based algorithms
Bidirectional trees
ISSN:0921-8890, 1872-793X
Online Access:Get full text
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Summary:The sampling-based motion planning algorithm known as Rapidly-exploring Random Trees (RRT) has gained the attention of many researchers due to their computational efficiency and effectiveness. Recently, a variant of RRT called RRT* has been proposed that ensures asymptotic optimality. Subsequently its bidirectional version has also been introduced in the literature known as Bidirectional-RRT* (B-RRT*). We introduce a new variant called Intelligent Bidirectional-RRT* (IB-RRT*) which is an improved variant of the optimal RRT* and bidirectional version of RRT* (B-RRT*) algorithms and is specially designed for complex cluttered environments. IB-RRT* utilizes the bidirectional trees approach and introduces intelligent sample insertion heuristic for fast convergence to the optimal path solution using uniform sampling heuristics. The proposed algorithm is evaluated theoretically and experimental results are presented that compares IB-RRT* with RRT* and B-RRT*. Moreover, experimental results demonstrate the superior efficiency of IB-RRT* in comparison with RRT* and B-RRT in complex cluttered environments. •Intelligent sample insertion.•Non-greedy trees connection.•Improves the convergence rate.
ISSN:0921-8890
1872-793X
DOI:10.1016/j.robot.2015.02.007