Deep reinforcement learning path planning and task allocation for multi-robot collaboration

In the current technological landscape, Multi-Robot Systems (MRS) have become crucial for complex tasks, with applications in industrial automation, search and rescue, and intelligent transportation. However, existing techniques face challenges in path planning and task allocation, particularly regarding adaptability, real-time decision-making, and efficiency. Deep Reinforcement Learning (DRL) has emerged as a promising solution due to its robust learning capabilities. To address these challenges, we propose an innovative DRL-MPCGNNs model that integrates Deep Reinforcement Learning, Model Predictive Control (MPC), and Graph Neural Networks (GNNs). Our model aims to optimize path planning and task allocation in multi-robot systems. Through rigorous experiments in simulated environments, we validated our model's effectiveness, demonstrating significant improvements in path planning precision, task allocation efficiency, and inter-robot collaboration performance. These results highlight our model's practicality and offer new insights for future research and applications in multi-robot systems. Overall, our integrated model addresses key issues in multi- robot collaboration, contributing an innovative solution to the field's development. This research provides a novel approach for path planning and task allocation in multi-robot systems, laying a solid foundation for deploying intelligent robotic systems in complex and dynamic environments.