Research on Informed-RRT* with Improved Initial Solution
Yang Zhao, Yiyang Liu, Hongwei Gao, Shuaihua Yan · 2022 4th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP) · 2022
RRT* is a common motion planning algorithm with asymptotic optimality, which is a variant of RRT (Rapidly-exploring Random Tree), but its convergence speed is slow. This paper proposes an improved Informed-RRT* algorithm to promote planning efficiency. Informed- RRT*samples within a hyper-ellipsoid built from an initial feasible solution, speeding up convergence to an optimal solution. To find the initial feasible solution more quickly, an improved Metropolis acceptance criterion for Informed-RRT* is introduced to filter the new nodes generated by each iteration. By comparing Informed-RRT* with the improved algorithm through numerical simulation, it is verified that the improved algorithm can find the initial solution efficiently. At the same time, the simulation results also verify that the improved algorithm can find the same solution in a shorter time, so it can be concluded that the algorithm is effective.