Evolution of behaviors in autonomous robot using artificial neural network and genetic algorithm

  • Authors:

  • Malrey Lee

  • Affiliations:

  • Department of Multimedia, School of Multimedia, Yosu National University, San 96-1, Dunduckdong, Yosu, JunNam 550-749, South Korea

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2003

Quantified Score

Hi-index 0.08

Visualization

Abstract

In distributed autonomous robot (agents) systems, each robot (predator or prey) must behave by itself according to its states and environments, and if necessary, must cooperate with other robots in order to carry out a given task. Therefore it is essential that each robot have both learning and evolution ability to adapt to dynamic environment. This paper proposes a pursuing system utilizing the artificial life concept where autonomous mobile robots emulate social behaviors of animals and insects and realize their group behaviors. Each robot contains sensors to perceive other robots in several directions and decides its behavior based on the information obtained by the sensors. In this paper, a neural network is used for behavior decision controller. The input of the neural network is decided by the existence of other robots and the distance to the other robots. The output determines the directions in which the robot moves. The connection weight values of this neural network are encoded as genes, and the fitness individuals are determined using a genetic algorithm. Here, the fitness values imply how much group behaviors fit adequately to the goal and can express group behaviors. The validity of the system is verified through simulation. Besides, in this paper, we could have observed the robots' emergent behaviors during simulation.