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

TL;DR

This paper introduces a hierarchical multi-expert learning framework inspired by the CNS for autonomous robot navigation in complex VUCA environments, combining map exploration, route planning, and adaptive local control.

Contribution

The paper presents a novel hierarchical multi-expert approach that integrates exploration, planning, and adaptive control for navigation in VUCA environments, inspired by biological CNS functions.

Findings

Outperforms existing methods in task success rate

Demonstrates improved time efficiency in navigation tasks

Shows enhanced safety and security in real-world experiments

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.