Bio-inspired Decentralized Self-coordination Algorithms for Multi-heterogeneous Specialized Tasks Distribution in Multi-Robot Systems

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots, as opposed to the usual multi-tasks allocation problem in multi-robot systems in which an external controller distributes the existing tasks among the individual robots. We are rather interested on decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we establish an experimental scenario and we propose a bio-inspired solution based on threshold models to solve the corresponding multi-tasks distribution problem. The paper ends with a critical discussion of the experimental results.

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