2021/02/25 by Sagar Suhas Joshi, Joshi, Sagar Suhas, Seth Hutchinson +3
Computer Science · Engineering · #FOS: Computer and information sciences #Modular Robots and Swarm Intelligence #Robotic Path Planning Algorithms #Robotics (cs.RO) #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.2102.13064
openalex publication_date 2021/02/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Sampling-based algorithms solve the path planning 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 to improve the cost-to-come value of vertices in a neighborhood. The application of 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 experiments performed fora variety of higher dimensional robotic planning tasks.