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 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 Bidirectional
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.
