LES: Locally Exploitative Sampling for Robot Path Planning

Sampling-based algorithms solve the path plan- ning problem by generating random samples in the search- space and incrementally growing a connectivity graph or a tree. Conventionally, the sampling strategy used in these algorithms is biased towards exploration to acquire information about the search-space. In contrast, this work proposes an optimization- based procedure that generates new samples so as to improve the cost-to-come value of vertices in a given neighborhood. The application of the proposed algorithm adds an exploitative- bias to sampling and results in a faster convergence to the optimal solution compared to other state-of-the-art sampling techniques. This is demonstrated using benchmarking experi- ments performed for 7 DOF Panda and 14 DOF Baxter robots.