APT*: Asymptotically optimal motion planning via adaptively prolated elliptical r-nearest neighbors
Zhang, Liding Wang, Sicheng Cai, Kuanqi Bing, Zhenshan Wu, Fan Wang, Chaoqun Haddadin, Sami Knoll, Alois
Zhang, Liding
Wang, Sicheng
Cai, Kuanqi
Bing, Zhenshan
Wu, Fan
Wang, Chaoqun
Haddadin, Sami
Knoll, Alois
Supervisor
Department
Robotics
Embargo End Date
Type
Journal article
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Optimal path planning aims to determine a sequence of states from a start to a goal while accounting for planning objectives. Popular methods often integrate fixed batch sizes and neglect information on obstacles, which is not problem-specific. This study introduces Adaptively Prolated Trees (APT*), a novel sampling-based motion planner that extends based on Force Direction Informed Trees (FDIT*), integrating adaptive batch-sizing and elliptical r-nearest neighbor modules to dynamically modulate the path searching process based on environmental feedback. APT* adjusts batch sizes based on the hypervolume of the informed sets and considers vertices as electric charges that obey Coulomb's law to define virtual forces via neighbor samples, thereby refining the prolate nearest neighbor selection. These modules employ non-linear prolate methods to adaptively adjust the electric charges of vertices for force definition, thereby improving the convergence rate with lower solution costs. Comparative analyses show that APT* outperforms existing single-query sampling-based planners in dimensions from R4 to R16, and it was further validated through a real-world robot manipulation task
Citation
L. Zhang et al., "APT*: Asymptotically Optimal Motion Planning via Adaptively Prolated Elliptical R-Nearest Neighbors," in IEEE Robotics and Automation Letters, vol. 10, no. 10, pp. 10242-10249, Oct. 2025, doi: 10.1109/LRA.2025.3598616
Source
IEEE Robotics and Automation Letters
Conference
Keywords
Sampling-based Path Planning, Elliptical r-Nearest Neighbor, Adaptive Batch-size, Optimal Path Planning
Subjects
Source
Publisher
IEEE