Towards Efficient Trajectory Generation for Ground Robots beyond 2D Environment
Jingping Wang, Long Xu, Haoran Fu, Zehui Meng, Chao Xu, Yanjun Cao, Ximin Lyu, Fei Gao
Abstract
With the development of robotics, ground robots are no longer limited to planar motion. Passive height variation due to complex terrain and active height control provided by special structures on robots require a more general navigation planning framework beyond 2D. Existing methods rarely considers both simultaneously, limiting the capabilities and applications of ground robots. In this paper, we proposed an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis. The proposed planner first constructs a penalty field for chassis motion constraints defined in \mathbb{R}^{3}\mathbb{R}^{3} such that the optimal solution space of the trajectory is continuous, resulting in a high-quality smooth chassis trajectory. Also, by constructing custom constraints in the z-axis direction, it is possible to plan trajectories for different types of ground robots which have z-axis degree of freedom. We performed simulations and real-world experiments to verify the efficiency and trajectory quality of our algorithm.
BibTeX
@inproceedings{icra2023_towardsefficient,
title = {Towards Efficient Trajectory Generation for Ground Robots beyond 2D Environment},
author = {Jingping Wang and Long Xu and Haoran Fu and Zehui Meng and Chao Xu and Yanjun Cao and Ximin Lyu and Fei Gao},
booktitle = {ICRA 2023},
year = {2023}
}