A Comparative Study of Game-theoretical and Markov-chain-based Approaches to Division of Labour in a Robotic Swarm

Abstract This paper compares two swarm intelligence frameworks that we previously proposed for multi-robot task allocation problems: the game-theoretical approach based on anonymous hedonic games, called GRAPE , and the Markov-Chain-based approach under local information consistency assumption, called LICA-MC . We implement both frameworks into swarm distribution guidance problem, the objective of which is to distribute a swarm of robots into a set of tasks in proportion to the tasks’ demands, and then we perform extensive numerical experiments with various environmental settings. The statistical results show that LICA-MC provides excellent scalability regardless of the number of robots, whereas GRAPE is more efficient in terms of convergence time (especially when accommodating a moderate number of robots) as well as total travelling costs. Furthermore, this study investigates other implicit advantages of the frameworks such as mission suitability, additionally-built-in decision-making functions, and sensitivity to traffic congestion or robots’ mobility.