An Exploration of Online Parallel Learning in Heterogeneous Multi-robot Swarms
作者： Jim Pugh , Alcherio Martinoli
DOI: 10.1007/978-3-540-89933-4_7
关键词: Process (engineering) 、 Perception 、 Robot 、 Evolutionary robotics 、 Particle swarm optimization 、 Obstacle avoidance 、 Unsupervised learning 、 Artificial intelligence 、 Mobile robot 、 Engineering
摘要: Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process circumvented by using unsupervised learning techniques which allow to evolve their own online in an automated fashion. In multi-robot systems, parallel share information dramatically increase the evolutionary rate. However, manufacturing variations robotic sensors may result perceptual differences between robots, could impact process. chapter, we explore how varying sensor offsets scaling factors affects swarm-robotic of obstacle avoidance behavior both Genetic Algorithms Particle Swarm Optimization. We also observe diversity throughout two different metrics attempt better understand
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