A Fast and Efficient Double-Tree RRT$^*$-Like Sampling-Based Planner Applying on Mobile Robotic Systems

摘要

As a variant of rapidly exploring random tree (RRT), RRT$^*$is an important improvement of sampling-based algorithms. Although it can provide a feasible planning solution with a higher quality, more resources on optimization are required, resulting in a very slow convergence rate, which cannot satisfy the real-time requirements of most autonomous systems. In this paper, we propose a novel approach of RRT$^*$in collaboration with a double-tree structure to separate the extension and optimization procedure. In our algorithm, the original RRT is employed to explore the unknown environment and to search feasible connecting areas, represented by piecewise lines. Different from the method of anytime RRT$^*$, the RRT phase in our method is to find different homotopic paths during each iteration. Thereafter, a modified RRT$^*$is used to obtain an optimal solution. Simulation results on two benchmarks demonstrate an improved performance of our approach in comparison with the original RRT$^*$and its variants (e.g., DT-RRT). An additional evaluation on two real robotic systems further proves the efficiency of our approach.