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Rapidly-exploring Random Tree star (RRT*) has recently gained immensepopularity in the motion planning community as it provides a probabilisticallycomplete and asymptotically optimal solution without requiring the completeinformation of the obstacle space. In spite of all of its advantages, RRT*converges to an optimal solution very slowly. Hence to improve the convergencerate, its bidirectional variants were introduced, the Bi-directional RRT*(B-RRT*) and Intelligent Bi-directional RRT* (IB-RRT*). However, as bothvariants perform pure exploration, they tend to suffer in highly clutteredenvironments. In order to overcome these limitations, we introduce a newconcept of potentially guided bidirectional trees in our proposed PotentiallyGuided Intelligent Bi-directional RRT* (PIB-RRT*) and Potentially GuidedBi-directional RRT* (PB-RRT*). The proposed algorithms greatly improve theconvergence rate and have a more efficient memory utilization. Theoretical andexperimental evaluation of the proposed algorithms have been made and comparedto the latest state of the art motion planning algorithms under differentchallenging environmental conditions and have proven their remarkableimprovement in efficiency and convergence rate.

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