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The sampling based motion planning algorithm known as Rapidly-exploringRandom Trees (RRT) has gained the attention of many researchers due to theircomputational efficiency and effectiveness. Recently, a variant of RRT calledRRT* has been proposed that ensures asymptotic optimality. Subsequently itsbidirectional version has also been introduced in the literature known asBidirectional-RRT* (B-RRT*). We introduce a new variant called IntelligentBidirectional-RRT* (IB-RRT*) which is an improved variant of the optimal RRT*and bidirectional version of RRT* (B-RRT*) algorithms and is specially designedfor complex cluttered environments. IB-RRT* utilizes the bidirectional treesapproach and introduces intelligent sample insertion heuristic for fastconvergence to the optimal path solution using uniform sampling heuristics. Theproposed algorithm is evaluated theoretically and experimental results arepresented that compares IB-RRT* with RRT* and B-RRT*. Moreover, experimentalresults demonstrate the superior efficiency of IB-RRT* in comparison with RRT*and B-RRT in complex cluttered environments.

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