Opinion-Based Task Allocation for Distributed Mobile Robot Swarms under Minimum Robot Requirements

We introduce an opinion dynamics-based approach to Multi-Robot Task Allocation (MRTA), modeling task assignment as a convergence process of continuously evolving opinions. Our method integrates special task requirements, ensuring that each task receives a minimum number of mobile robots while dynamically adapting to system changes. We also introduce a two-time-scale framework where opinion dynamics operate on a faster time scale to facilitate rapid task allocation, while mobile robots move on a slower time scale, maintaining a stable communication graph during opinion convergence. Additionally, we develop a specialized velocity controller that utilizes location and task assignment information from neighboring robots, promoting the reassignment of robots to under-selected tasks. We validate our strategy through simulations, demonstrating its effectiveness across diverse robot and task settings. Our findings highlight the potential of opinion dynamics in enhancing the scalability, efficiency, and reliability of distributed MRTA systems for mobile robot swarms.