A classifier system in real applications for robot navigation
Renato Reder Cazangi, Fernando José Von Zuben, M. Figueiredo · 2003
This paper presents an autonomous evolutionary system applied to control a mobile robot in unknown environments. The navigation system learns efficiently to deal with situations where the robot must capture targets avoiding collisions with obstacles. Toward this end, robot direction and speed must be properly defined. The evolutionary approach is based on a version of classifier systems, responsible for the proposition of a competitive process involving rules of elementary behaviour. A virtual environment is used to evolve the controller, a Khepera II robot is submitted to real navigation tasks, with no significant degradation in performance. As an additional experiment, the controller is also evolved in a real environment, and validated in a different and more complex environment, not previously experimented, attesting the generalization capability of the proposal.