An obstacle-responsive technique for the management and distribution of local rapidly-exploring random trees

The evolution of sampling-based planning has introduced a number of novel algorithms and modifications that allow for various adaptations to the local environment. This paper presents a revision to the Path-length Annexed Random Tree (PART) that allocates and regulates local exploration in an adaptive manner. The improved algorithm eliminates the need to choose and tune an additional threshold parameter while providing denser coverage in potentially complex regions and a natural means of connection for either unidirectional or bidirectional planning. The robustness of this algorithm and the performance implications of the threshold parameter are demonstrated in 2D and SE(3) experiments.