| Mobile robots are being used for an increasing array of tasks, from military reconnaissance to planetary exploration to urban search and rescue. As robots are deployed in increasingly complex domains, teams are called upon to perform tasks that exceed the capabilities of any particular robot. Thus, it becomes necessary for robots to cooperate, such that one robot can recruit another to jointly perform a task. Though techniques exist to allocate robots to tasks, either the communication overhead that these techniques require prevents them from scaling up to large teams, or assumptions are made that limit them to simple domains. This dissertation presents a novel emotion-based recruitment approach to the multi-robot task allocation problem. This approach requires less communication bandwidth than comparable 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 a simulated 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. |
