Learning to Navigate in a VUCA Environment: Hierarchical Multi-expert Approach
This addresses the problem of autonomous robot navigation in challenging real-world scenarios, representing an incremental improvement with a novel hybrid approach.
The paper tackles robot navigation in volatile, uncertain, complex, and ambiguous (VUCA) environments by proposing a hierarchical multi-expert learning framework inspired by the central nervous system, and it demonstrates that the algorithm outperforms existing methods in task achievement, time efficiency, and security in simulations and real-world experiments.
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.