Control program learning systems for autonomous robots are important to assist in their development and to allow them to adapt to changes in their capabilities and/or the environment. A common method for learning in robotics is Evolutionary Computation (EC) and a good problem to demonstrate the effectiveness of a learning system is the predator/prey problem. In previous research, we used a Cyclic Genetic Algorithm (CGA), a form of EC, to evolve the control program for a predator robot with a simple sensor configuration of 4 binary sensors, which yielded 16 possible sensor states. In this paper, we present the use of a CGA to learn control for a predator robot with a more complicated sensor setup, which yields 64 sensor states. The learning system successfully evolved a control program that produced search, chase, and capture behavior in the simulated predator robot.
Parker, G.; Gulcu, B., "Evolving predator control programs for a hexapod robot pursuing a prey," Automation Congress, 2008. WAC 2008. World , vol., no., pp.1,7, Sept. 28 2008-Oct. 2 2008
The views expressed in this paper are solely those of the author.