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Abstract

This paper proposes a hybrid planning framework that generates complex dynamic motion plans for jumping legged robots to traverse challenging terrains. By employing a motion primitive, the original problem is decoupled as path planning followed by a trajectory optimization (TO) module that handles dynamics. A variant of a kinodynamic Rapidlyexploring Random Trees (RRT) planner finds a path as a parabola sequence between stance phases. To make this fast, a reachability informed control sampling scheme leverages a precomputed velocity reachability map. The path is postprocessed to eliminate redundant jumps and passed to the TO module to find a dynamically feasible trajectory. Simulation results are presented where the proposed hybrid planner solves challenging terrains by executing multiple consecutive jumps, producing novel strategies to leap over large gaps by leveraging dynamics. In a physical experiment, the hybrid planner is tested on a real robot successfully traversing a challenging terrain.

Citation

@inproceedings{ding2021hybrid,
title={Hybrid sampling/optimization-based planning for agile jumping robots on challenging terrains},
author={Ding, Yanran and Zhang, Mengchao and Li, Chuanzheng and Park, Hae-Won and Hauser, Kris},
booktitle={2021 IEEE International Conference on Robotics and Automation (ICRA)},
pages={2839–2845},
year={2021},
organization={IEEE}
}