GPF-BG: A Hierarchical Vision-Based Planning Framework for Safe Quadrupedal Navigation
Shiyu Feng, Ziyi Zhou, Justin S. Smith, Max Asselmeier, Ye Zhao, Patricio A. Vela
Abstract
Safe quadrupedal navigation through unknown environments is a challenging problem. This paper proposes a hierarchical vision-based planning framework (GPF-BG) integrating our previous Global Path Follower (GPF) navigation system and a gap-based local planner using Bézier curves, so called BBézier Gap (BG). This BG-based trajectory synthesis can generate smooth trajectories and guarantee safety for point-mass robots. With a gap analysis extension based on non-point, rectangular geometry, safety is guaranteed for an idealized quadrupedal motion model and significantly improved for an actual quadrupedal robot model. Stabilized perception space improves performance under oscillatory internal body motions that impact sensing. Simulation-based and real experiments under different benchmarking configurations test safe navigation performance. GPF-BG has the best safety outcomes across all experiments.
BibTeX
@inproceedings{icra2023_gpfbgahierarchic,
title = {GPF-BG: A Hierarchical Vision-Based Planning Framework for Safe Quadrupedal Navigation},
author = {Shiyu Feng and Ziyi Zhou and Justin S. Smith and Max Asselmeier and Ye Zhao and Patricio A. Vela},
booktitle = {ICRA 2023},
year = {2023}
}