Many applications of Swarm Robotic Systems (SRSs) require each robot to be able to discover its own position. To provide such capability, some localization methods have been proposed, in which the positions of the robots are estimated based on a set of reference nodes in the swarm. In this paper, a distributed and resilient localization algorithm is proposed based on the BSA–MMA algorithm, which uses the Backtracking Search Algorithm (BSA) and the Min–Max Area (MMA) confidence factor. It is designed in a novel four-stage approach, where a new method, called Multi-hop Collaborative Min–Max Localization (MCMM), is included to improve the resilience in case of failures during the recognition of the reference nodes. The results, obtained with real Kilobot robots, show 28–36% of performance improvement obtained by the MCMM. Also, it is shown that the final result of the localization process is better when the MCMM is executed than if it is not executed. The experiments outcomes demonstrate that the novel four-stage approach and the use of the MCMM algorithm represents a progress in the design of distributed localization algorithms for SRS, especially with regard to its resilience.
['Alan Oliveira de Sá', 'Nadia Nedjah', 'Luiza de Macedo Mourelle']
Distributed and resilient localization algorithm for Swarm Robotic Systems