Distributed scalable multi-robot learning using particle swarm optimization |
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Authors: | Jim Pugh Alcherio Martinoli |
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Institution: | (1) Distributed Intelligent Systems and Algorithms Group, école Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland |
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Abstract: | Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented
by using online learning techniques which allow robots to generate their own controllers online in an automated fashion. In
multi-robot systems, robots operating in parallel can potentially learn at a much faster rate by sharing information amongst
themselves. In this work, we use an adapted version of the Particle Swarm Optimization algorithm in order to accomplish distributed
online robotic learning in groups of robots with access to only local information. The effectiveness of the learning technique
on a benchmark task (generating high-performance obstacle avoidance behavior) is evaluated for robot groups of various sizes,
with the maximum group size allowing each robot to individually contain and manage a single PSO particle. To increase the
realism of the technique, different PSO neighborhoods based on limitations of real robotic communication are tested and compared
in this scenario. We explore the effect of varying communication power for one of these communication-based PSO neighborhoods.
To validate the effectiveness of these learning techniques, fully distributed online learning experiments are run using a
group of 10 real robots, generating results which support the findings from our simulations. |
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Keywords: | Multi-robot systems Robotic learning Particle swarm optimization |
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