TMG-MADRL: a transformer-based meta-graph multi-agent deep reinforcement learning framework for robot path planning in dynamic environments

Abstract

Robot path planning in dynamic environments is a critical research domain in autonomous robotics, focusing on safe and efficient navigation under uncertain and continuously changing conditions. The presence of moving obstacles, unpredictable environmental variations, and real-time decision-making constraints makes traditional path planning methods less effective. To address these challenges, this study proposes a novel Transformer-guided Meta-learning and Graph-enhanced Multi-Agent Deep Reinforcement Learning (TMG-MADRL) framework for intelligent robot navigation. The framework integrates Vision Transformer (ViT) for extracting global environmental and obstacle features, Graph Attention Network (GAT) for spatial relationship modelling, Model-Agnostic Meta-Learning (MAML) for rapid adaptation to unseen environments, Multi-Agent Proximal Policy Optimization (MAPPO) and Soft Actor-Critic (SAC) for cooperative policy optimization, and Temporal Graph Transformer Network (TGTN) for future obstacle trajectory prediction. The work aims to achieve robust path optimization, adaptive decision-making, and proactive collision avoidance in dynamic scenarios. The framework is trained and evaluated using the Robot Path Planning Navigation Dataset, where navigation states are classified into Collision and Safe categories. Experimental results achieved 98.24% accuracy, 98.17% precision, 98.42% recall, 98.06% specificity, and 97.91% MCC, validating superior navigation efficiency and collision classification performance.