Journal
ROBOTICA
Volume 32, Issue 2, Pages 193-208Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/S026357471300101X
Keywords
DARS2012; Multi-robot systems; Mobile robots; Particle Swarm Optimization; Distributed learning
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Funding
- Swiss National Science Foundation through the National Center of Competence in Research Robotics
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Evaluative techniques offer a tremendous potential for online controller design. However, when the optimization space is large and the performance metric is noisy, the overall adaptation process becomes extremely time consuming. Distributing the adaptation process reduces the required time and increases robustness to failure of individual agents. In this paper, we analyze the role of the four algorithmic parameters that determine the total evaluation time in a distributed implementation of a Particle Swarm Optimization (PSO) algorithm. For an obstacle avoidance case study using up to eight robots, we explore in simulation the lower boundaries of these parameters and propose a set of empirical guidelines for choosing their values. We then apply these guidelines to a real robot implementation and show that it is feasible to optimize 24 control parameters per robot within 2 h, a limited amount of time determined by the robots' battery life. We also show that a hybrid simulate-and-transfer approach coupled with a noise-resistant PSO algorithm can be used to further reduce experimental time as compared to a pure real-robot implementation.
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