Affective Task Allocation for Distributed Multi-Robot Teams

This article presents a novel emotion-based recruitment approach to the multi-robot task allocation problem. This approach requires less communication bandwidth than auction methods, enabling it to scale to large team sizes, and making it appropriate for low-power or stealth applications. Affective recruitment is tolerant of unreliable communications channels, and can find better solutions than simple greedy schedulers (based on experimental metrics of the time necessary to complete recruitment and the total number of messages transmitted). Experimental results in simulation and on three UGVs and one UAV in a mine-detection task show that affective recruitment succeeds with network failure rates up to 25% and requires 32% fewer transmissions compared to existing methods on average. Affective recruitment also scales better with team size, requiring up to 61% fewer transmissions than a greedy instantaneous scheduler that has an O(n) communications complexity, without a significant increase in allocation time.