Title :
Parallel learning in heterogeneous multi-robot swarms
Author :
Pugh, Jim ; Martinoli, Alcherio
Author_Institution :
Ecole Polytech. Fed. de Lausanne, Lausanne
Abstract :
Designing effective behavioral controllers for mobile robots can be difficult and tedious; this process can be circumvented by using unsupervised learning techniques which allow robots to evolve their own controllers in an automated fashion. In multi-robot systems, robots learning in parallel can share information to dramatically increase the evolutionary rate. However, manufacturing variations in robotic sensors may result in perceptual differences between robots, which could impact the learning process. In this paper, we explore how varying sensor offsets and scaling factors affects parallel swarm-robotic learning of obstacle avoidance behavior using both Genetic Algorithms and Particle Swarm Optimization. We also observe the diversity of robotic controllers throughout the learning process in an attempt to better understand the evolutionary process.
Keywords :
genetic algorithms; multi-robot systems; particle swarm optimisation; behavioral controllers; genetic algorithms; heterogeneous multirobot swarms; multirobot systems; obstacle avoidance behavior; parallel learning; particle swarm optimization; Automatic control; Genetic algorithms; Manufacturing processes; Mobile robots; Multirobot systems; Parallel robots; Robot control; Robot sensing systems; Robotics and automation; Unsupervised learning;
Conference_Titel :
Evolutionary Computation, 2007. CEC 2007. IEEE Congress on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-1339-3
Electronic_ISBN :
978-1-4244-1340-9
DOI :
10.1109/CEC.2007.4424971