期刊
ROBOTICS AND AUTONOMOUS SYSTEMS
卷 60, 期 12, 页码 1625-1639出版社
ELSEVIER
DOI: 10.1016/j.robot.2012.09.021
关键词
Foraging; Swarm robotics; Parameter adjustment; Fuzzy logic; Context-based information; Adaptive behavior
资金
- Portuguese Foundation for Science and Technology (FCT) [SFRH/BD/73382/2010]
- Institute of Systems and Robotics (ISR)
- research project CHOPIN [PTDC/EEA-CRO/119000/2010]
- FCT
The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario. (C) 2012 Elsevier B.V. All rights reserved.
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