Abstract
The disclosure relates generally to methods and systems for optimizing multi-robot task allocation through heuristic-guided reinforcement learning in dynamic environments. Conventional RL framework-based approaches focus on optimal task selection, neglecting task-to-robot assignment under the assumption of constant robot availability post-selection. The present disclosure solves the technical problems in the art through heuristic-guided reinforcement learning (RL) in dynamic environments. The methods and systems of the present disclosure (coined as HeuRAL-MATE) combine heuristic guidance with RL to address the multi-robot task allocation challenge in warehouse environments. The HeuRAL-MATE effectively manages real-time task selection, the allocation of tasks to robots, and the secure navigation of robots, by minimizing both the total travel distance of robots and the delay in task execution while considering practical charging/discharging constraints and collision-free navigation of the robots.