In the development of autonomous robots, control program learning systems are important since they allow the robots to adapt to changes in their surroundings. Evolutionary Computation (EC) is a method that is used widely in learning systems. In previous research, we used a Cyclic Genetic Algorithm (CGA), a form of EC, to evolve a simulated predator robot to test the effectiveness of a learning system in the predator/prey problem. The learned control program performed search, chase, and capture behavior using 64 sensor states relative to the nearest obstacle and the target, a simulated prey robot. In this paper, we present the results of a new set of trials, which were tested on the actual robots. The actual robots successfully performed desired behaviors, showing the effectiveness of the CGA learning system.
Parker, G.; Gulcu, B., "Evolving predator control programs for an actual hexapod robot predator," Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference, vol., no., pp.196-201, 14-17 Oct. 2012 doi: 10.1109/ICSMC.2012.6377699
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