Resilient Real-Time Decision-Making for Autonomous Mobile Robot Path Planning in Complex Dynamic Environments

Abstract

We propose a hierarchical dual-layer decision-making framework to address challenges associated with autonomous mobile robot (AMR) path planning in complex and dynamic campus environments. The upper-layer global planning is formulated as a multiobjective optimization model, where a multiobjective sheep flock migrate optimization algorithm (MOSFMO) is proposed to generate Pareto front solutions by optimizing path length and path safety jointly. In the lower layer, the autonomy of the AMR is enhanced through deep reinforcement learning (DRL) training with a composite reward scheme designed to enable resilient real-time decisions for avoiding unexpected pedestrians while achieving global objectives. Effective coordination is achieved through the availability of multiple candidate paths and a time-oriented deadlock detection mechanism, enabling uninterrupted task execution despite encountered blockage challenges. The proposed methods are validated through numerical simulations and real-world experiments, achieving on-time arrival rates of up to 99% in dynamic environments.