Multi-robot task allocation methods: A fuzzy optimization approach.

Response-threshold methods stand out among the different developed swarm-like methodologies that address the task allocation problem, which must be faced in multi-robot systems in order to assign to each robot the best task to perform at each instant of time. In many real missions the tasks have associated deadlines. However, the literature only contains a few swarm methodologies, and thus response-threshold methods, tackling tasks with deadlines. Motivated by this fact, in this paper, we propose a new task allocation strategy inspired by response-threshold methods which deals with tasks with time deadlines, models stimuli using fuzzy sets and, in addition, in which each robot makes the decision about the best task to perform through the celebrated Bellman-Zadeh fuzzy optimization technique. An extensive number of simulations have been carried out in order to evaluate the quantitative performance of the swarm system based on the new approach. The results confirm that the proposed mathematical approach is able to model the evolution of the system when tasks with deadlines are under consideration. We have also observed competitive performance on a fleet of real robots, which corroborates the results derived from the simulations. • A task-allocation strategy for RTM methods dealing with deadlines is proposed. • The robot stimuli are modeled using fuzzy sets and aggregation functions. • Robots decide the best task to go through a fuzzy optimization technique. • The strategy is compatible with swarm systems: low computing and communication costs. • Extensive number of experimental results confirm the utility of the new strategy. [ABSTRACT FROM AUTHOR]