Synergizing Evolutionary Task Allocation with Learning-Driven Path Planning

Task allocation and path planning in multi-robot systems have been widely applied in various storage and logistics scenarios. In recent years, an increasing number of learning-based methods have been employed in the field of path planning, enabling robots to plan paths with limited perception. However, current research integrating multi-robot task allocation with path planning still relies on methods that require centralized planning, and the cost estimation methods used for task allocation do not consider the time consumed for mutual collision avoidance among multiple robots, limiting both the scale of task handling and the accuracy of task allocation. To address this challenge, this paper proposes a framework that utilizes reinforcement learning (RL) and genetic algorithms (GA) to integrate task allocation and path planning. By implementing this framework, tasks can be allocated more appropriately and collision-free optimal paths can be generated. Experimental results demonstrate that the proposed algorithm achieves higher efficiency in complex multi-robot scenarios by effectively managing congestion and optimizing task allocation.