Learning sampling distribution for motion planning with local reconstruction-based self-organizing incremental neural network

Abstract

For sampling-based motion planners (e.g., PRM and RRT*), collision detection dominates the asymptotic running time and reduces the execution efficiency. The reason of this problem is that obtaining a high-dimensional implicit representation (i.e., configuration space distribution) of the state space is not easy, especially in the complicated environment with various obstacles. Though sampling-based planning algorithms and their variants perform well, most of these algorithms have strict restrictions and narrow applications. A possible ideal solution is to design a non-uniform sampling strategy to ensure the sampling process only occurs in collision-free region $\chi _{free}$ but not in collision region $\chi _{col}$. Therefore, we propose a new methodology to learn the sampling distribution for non-uniform sampling. The sampling distribution is learned through a local reconstruction-based self-organizing incremental neural network and allows to generate samples from the learned latent distribution. Besides, our method can adapt well to environmental non-vigorous changes and adjust the learned distribution quickly. The method can effectively exploit the underlying structure of the planning problem and be spread for general use in combination with any sampling-based planning algorithms. Specifically, we use two typical planning problems to show that the proposed method can effectively learn and update the sampling distribution from the high-dimensional configuration space in the changed environment, resulting in a dominant performance in terms of the cost, running time and success rate.