Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems

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• 作者：Javier de Lope^a ; ^bAuthor Vitae ; Darí ; o Maravall^aAuthor Vitae ; Yadira Quiñ ; onez^a ; ^{ay.quinonez@alumnos.upm.es}Author Vitae
• 关键词：Multi-robot systems ; Bio-inspired threshold models ; Stochastic learning automata ; Multi-task distribution ; Self-coordination of multiple robots ; Multi-heterogeneous specialized tasks distribution
• 刊名：Robotics and Autonomous Systems
• 出版年：2013
• 出版时间：July, 2013
• 年：2013
• 卷：61
• 期：7
• 页码：714-720
• 全文大小：1009 K

文摘

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot¡¯s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.