Name
Abstract | ||
|---|---|---|
Despite decades of efforts, robot navigation in a real scenario with volatility, uncertainty, complexity, and ambiguity (VUCA for short), remains a challenging topic. Inspired by the central nervous system (CNS), we propose a hierarchical multi-expert learning framework for autonomous navigation in a VUCA environment. With a heuristic exploration mechanism considering target location, path cost, and safety level, the upper layer performs simultaneous map exploration and route-planning to avoid trapping in a blind alley, similar to the cerebrum in the CNS. Using a local adaptive model fusing multiple discrepant strategies, the lower layer pursuits a balance between collision-avoidance and go-straight strategies, acting as the cerebellum in the CNS. We conduct simulation and real-world experiments on multiple platforms, including legged and wheeled robots. Experimental results demonstrate our algorithm outperforms the existing methods in terms of task achievement, time efficiency, and security. A video of our results is available at https://youtu.be/lAnW4QIWDoU. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/IROS51168.2021.9636370 | 2021 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) |
DocType | ISSN | Citations |
Conference | 2153-0858 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenqi Zhang | 1 | 0 | 0.68 |
| Kai Zhao | 2 | 0 | 1.01 |
| Peng Li | 3 | 0 | 1.01 |
| Xiao Zhu | 4 | 0 | 0.68 |
| Faping Ye | 5 | 0 | 0.34 |
| Weijie Jiang | 6 | 0 | 0.34 |
| Huiqiao Fu | 7 | 0 | 0.34 |
| Tao Wang | 8 | 0 | 0.34 |