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

• Published in: Robotics and autonomous systems 2013-07, Vol.61 (7), p.714-720
• Main Authors: de Lope, Javier, Maravall, Darío, Quiñonez, Yadira
• Format: Article
• Language: English
• Subjects: Algorithms; Autonomous; Bio-inspired threshold models; Decentralized; Learning; Mathematical models; Multi-heterogeneous specialized tasks distribution; Multi-robot systems; Multi-task distribution; Multiple robots; Self-coordination of multiple robots; Stochastic learning automata; Tasks; Thresholds
• ISSN: 0921-8890; 1872-793X
• DOI: 10.1016/j.robot.2012.07.008

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. ► An experimental scenario to solve the multi-task distribution problem is proposed. ► Evaluating the effectiveness of the methods for the optimal distribution of tasks. ► We employed two different mechanisms for the selection of tasks. ► We have analyzed the robustness of methods as regards the estimation error or noise. ► Generation of dynamic tasks over time to evaluate the performance of the approaches.