Evolutionary swarm robotics approach to a pursuit problem
Toshiyuki Yasuda, Kazuhiro Ohkura, Tosei Nomura, Yoshiyuki Matsumura · 2014
The pursuit problem is a conventional benchmark in distributed artificial intelligence research. The focal point of previous work in this domain has been the development of coordination mechanisms for predators that cooperatively hunt prey in a typically discrete grid world. This paper investigates a pursuit problem in a continuous torus field on the basis of swarm robotics. Twenty predator robots and three prey robots, each of which can be hunted by multiple predators, are assumed. Predators have a controller represented by evolving artificial neural networks (EANNs), and prey have a predetermined behavior rule for escaping predators. A series of computer simulations were conducted to compare three types of EANNs to determine the efficient artificial evolution of the predator robot controllers.