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Robot Swarms Break Decision Deadlocks in Collective Perception Through Cross-Inhibition

par Zakir, Raina ;Dorigo, Marco ;Reina, Andreagiovanni
Référence Lecture notes in computer science, 13491, page (209–221)
Publication Publié, 2022-10-29

Article révisé par les pairs

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Résumé :

We study how robot swarms can achieve a consensus on the best among a set of n possible options available in the environment. While the robots rely on local communication with one another, follow simple rules, and make estimates of the option’s qualities subject to measurement errors, the swarm as a whole is able to make accurate collective decisions. We compare the performance of two prominent decision-making algorithms that are based, respectively, on the direct-switching and the cross-inhibition models, both of which are well-suited for simplistic robots. Most studies used these models to let robots achieve consensus by solely relying on social interactions and ignored the aspect of enabling robots to self-source information from the environment. However, in order to select the best option, we deem sampling environmental information crucial for the successful performance of the task. Through robot-swarm simulations, we show that swarms programmed with the direct-switching model are only able to make consensus decisions in asymmetric environments where options have different quality values. Instead, using cross-inhibition, the robot swarm can also break decision deadlocks and reach a consensus in symmetric environments with equal quality options. We investigate the mechanistic causes of such differences and we find that the time the robots spend in a state of indecision is a key parameter to break the symmetry. This research highlights the importance of considering both social and environmental information when studying collective decision-making.