Task Allocation in Robotic Swarms: Explicit Communication Based Approaches
This addresses task allocation for robotic swarms in confined, unknown settings, but it appears incremental as it builds on existing self-organized approaches.
The paper tackles the problem of multi-robot cooperative task allocation in an unknown environment with unknown task distributions, proposing four self-organized distributed methods to enable robots to discover and cover colored spots proportionally. The results include analysis of performance, scalability, and robustness against single points of failure in two experiments.
In this paper we study multi robot cooperative task allocation issue in a situation where a swarm of robots is deployed in a confined unknown environment where the number of colored spots which represent tasks and the ratios of them are unknown. The robots should cover this spots as far as possible to do cleaning and sampling actions desirably. It means that they should discover the spots cooperatively and spread proportional to the spots area and avoid from remaining idle. We proposed 4 self-organized distributed methods which are called hybrid methods for coping with this scenario. In two different experiments the performance of the methods is analyzed. We compared them with each other and investigated their scalability and robustness in term of single point of failure.