Title :
Evolving predator control programs for an actual hexapod robot predator
Author :
Parker, Gary ; Gulcu, Basar
Author_Institution :
Dept. of Comput. Sci., Connecticut Coll., New London, CT, USA
Abstract :
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.
Keywords :
collision avoidance; control engineering computing; genetic algorithms; learning systems; legged locomotion; multi-robot systems; predator-prey systems; robot programming; CGA learning system; autonomous robots; capture behavior; chase behavior; control program learning systems; cyclic genetic algorithm; evolutionary computation; evolving predator control programs; hexapod robot predator; predator-prey problem; search behavior; sensor states; simulated predator robot; simulated prey robot; Biological cells; Genetic algorithms; Robot sensing systems; Sociology; Sonar; Statistics; autonomous agent learning; cyclic genetic algorithm; evolutionary robotics; genetic algorithm; robotics;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4673-1713-9
Electronic_ISBN :
978-1-4673-1712-2
DOI :
10.1109/ICSMC.2012.6377699