  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.
