In this paper, a distributed algorithm for adaptive task allocation and adaptable partitioning of a swarm into different work-groups is proposed and used in a swarm of underwater robots. The algorithm is based on local interactions of agents and is inspired by honeybee age-polyethism. It is adaptive to changes in the swarm size workforce and relative demands workload for different tasks and it limits the number of switchings of agents between different tasks enabling specialization of agents. The preliminary version of the algorithm was introduced previously. Here a fully decentralized version of the algorithm is proposed that improves the previous version by removing the need for global information. The algorithm is successfully implemented in swarms of physically-embodied underwater robots while the swarm size and the demands for the tasks change over the course of the experiments.
['Payam Zahadat', 'Thomas Schmickl']
Division of labor in a swarm of autonomous underwater robots by improved partitioning social inhibition