MS-CRL: Multi-Scale Global Path Planning with Progressive Curriculum Reinforcement Learning

Abstract

Global path planning provides high-level guidance for autonomous navigation, supplying reference paths for downstream navigation and control modules. Deep Reinforcement Learning (DRL) has shown strong potential in this domain, but existing methods struggle with multi-scale map inputs. This limitation arises from inconsistent representations across different map sizes and trajectory length variations, which hinder feature extraction, destabilize policy learning. To address these challenges, we propose the Progressive Multi-Scale Curriculum Reinforcement Learning (MS-CRL) framework. MS-CRL incorporates a progressive curriculum reinforcement learning algorithm (ProgCRL) that mitigates instability from trajectory length discrepancies, a unified multi-scale representation (UniMS) that normalizes spatial scales and resolves representation inconsistencies, and a Global-Local Fusion Network (GLFNet) that fully extracts both global and local features from the new representation for robust cross-scale policy learning. Extensive experiments on multi-scale map datasets demonstrate that MS-CRL enables effective global path planning, stabilizes policy learning, and achieves superior performance in path success rate, path quality, and planning efficiency, while significantly improving training efficiency and cross-scale adaptability compared with state-of-the-art baselines.