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arXiv · 2111.08364

Learning to Navigate in a VUCA Environment: Hierarchical Multi-expert Approach

Wenqi Zhang·Kai Zhao·Peng Li·Xiao Zhu·Faping Ye·Weijie Jiang·Huiqiao Fu·Tao Wang
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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.

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Wenqi Zhang, Kai Zhao, Peng Li, Xiao Zhu, Faping Ye, Weijie Jiang, Huiqiao Fu, Tao Wang. 2021-11-16. Learning to Navigate in a VUCA Environment: Hierarchical Multi-expert Approach. https://doi.org/10.1109/iros51168.2021.9636370

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