Elliptical K-Nearest Neighbors - Path Optimization via Coulomb’s Law and Invalid Vertices in C-space Obstacles
Year: 2024
Type: conference-paper
Abstract: Path planning has long been an important and active research area in robotics. To address challenges in high-dimensional motion planning, this study introduces the Force Direction Informed Trees (FDIT*), a sampling-based planner designed to enhance speed and cost-effectiveness in pathfinding. FDIT* builds upon the state-of-the-art informed sampling planner, the Effort Informed Trees (EIT*), by capitalizing on often-overlooked information in invalid vertices. It incorporates principles of physical force, particularly Coulomb’s law. This approach proposes the elliptical k-nearest neighbors search method, enabling fast convergence navigation and avoiding high solution cost or infeasible paths by exploring more problem-specific search-worthy areas. It demonstrates benefits in search efficiency and cost reduction, particularly in confined, high-dimensional environments. It can be viewed as an extension of nearest neighbors search techniques. Fusing invalid vertex data with physical dynamics facilitates force-direction-based search regions, resulting in an improved convergence rate to the optimum. FDIT* outperforms existing single-query, sampling-based planners on the tested problems in ℝ4to ℝ16and has been demonstrated on a real-world mobile manipulation task.
Authors: Liding Zhang, Zhenshan Bing, Yu Zhang, Kuanqi Cai, Lingyun Chen, Fan Wu, Sami Haddadin, Alois Knoll
Institution: Technical University of Munich
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405786701