Flexible Informed Trees (FIT*): Adaptive Batch-Size Approach in Informed Sampling-Based Path Planning
Year: 2024
Type: conference-paper
Abstract: In path planning, anytime almost-surely asymptotically optimal planners dominate the benchmark of sampling-based planners. A notable example is Batch Informed Trees (BIT*), where planners iteratively determine paths to batches of vertices within the exploration area. However, utilizing a consistent batch size is inefficient for initial pathfinding and optimal performance, it relies on effective task allocation. This paper introduces Flexible Informed Trees (FIT*), a sampling-based planner that integrates an adaptive batch-size method to enhance the initial path convergence rate. FIT* employs a flexible approach in adjusting batch sizes dynamically based on the inherent dimension of the configuration spaces and the hypervolume of the n-dimensional hyperellipsoid. By applying dense and sparse sampling strategy, FIT* improves convergence rate while finding successful solutions faster with lower initial solution cost. This method enhances the planner’s ability to handle confined, narrow spaces in the initial finding phase and increases batch vertices sampling frequency in the optimization phase. FIT* outperforms existing single-query, sampling-based planners on the tested problems in R2to R8, and was demonstrated on a real-world mobile manipulation task.
Authors: Liding Zhang, Zhenshan Bing, Kejia Chen, Lingyun Chen, Kuanqi Cai, Yu Zhang, Fan Wu, Peter Krumbholz, Zhilin Yuan, Sami Haddadin, Alois Knoll
Institutions: Technical University of Munich, Linde (Germany)
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Ministry of Education
OpenAlex ID: W4405785118