Efficient Swept Volume-Based Trajectory Generation for Arbitrary-Shaped Ground Robot Navigation
Yisheng Li, Longji Yin, Yixi Cai, Jianheng Liu, Fangcheng Zhu, Mingpu Ma, Siqi Liang, Haotian Li
Abstract
Navigating an arbitrary-shaped ground robot safely in cluttered environments remains a challenging problem. The existing trajectory planners that account for the robot’s physical geometry severely suffer from the intractable runtime. To achieve both computational efficiency and Continuous Collision Avoidance (CCA) of arbitrary-shaped ground robot planning, we proposed a novel coarse-to-fine navigation framework that significantly accelerates planning. In the first stage, a sampling-based method selectively generates distinct topological paths that guarantee a minimum inflated margin. In the second stage, a geometry-aware front-end strategy is designed to discretize these topologies into full-state robot motion sequences while concurrently partitioning the paths into SE(2) sub-problems and simpler ℝ<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> sub-problems for back-end optimization. In the final stage, an SVSDF-based optimizer generates trajectories tailored to these sub-problems and seamlessly splices them into a continuous final motion plan. Extensive benchmark comparisons show that the proposed method is one to several orders of magnitude faster than the cutting-edge methods in runtime while maintaining a high planning success rate and ensuring CCA.
BibTeX
@inproceedings{iros2025_efficientsweptvo,
title = {Efficient Swept Volume-Based Trajectory Generation for Arbitrary-Shaped Ground Robot Navigation},
author = {Yisheng Li and Longji Yin and Yixi Cai and Jianheng Liu and Fangcheng Zhu and Mingpu Ma and Siqi Liang and Haotian Li and Fu Zhang},
booktitle = {IROS 2025},
year = {2025}
}