An Adaptable Oscillator-Based Controller for Autonomous Robots
This paper proposes a unique oscillator-based robot controller with learning abilities to effectively guide a team of robots operating in uncertain environments. To verify this, we designed four separate controllers and compared their performance in a series of tests in several different environments. The experiments used a team of three robots to explore arenas with variable lighting and different obstacle patterns, with a goal of having the team as a whole absorb as much light as possible. The four controllers were: a reactive controller, an oscillator with fixed parameters, an oscillator whose parameters changed based on the pattern of sensor information received, and an oscillator-based controller that used reinforcement learning. Experiments confirmed that the proposed method outperforms the others in all environments tested.
reinforcement learning coupled oscillators nonlinear systems autonomous vehicles control systems