Creative Control for Intelligent Autonomous Mobile Robots

For intelligent robots to accomplish tasks in an unstructured environment, the adaptive critic algorithm has been shown to provide useful approximations or even optimal control policies to non-linear systems. The purpose of this paper is to explore the use of new learning control methods defined as Creative Learning or Creative Control that goes beyond the adaptive critic method for unstructured environments. The creative controller like the adaptive critic controller has information stored in a dynamic database (DB), plus a dynamic task control center (TCC) that functions as a command center to decompose tasks into sub-tasks with different dynamic models and multi-criteria functions. The simulation results based on adaptive critic learning are discussed in this paper. The significance of this paper is to better understand the adaptive critic learning theory and move forward to develop more human-intelligencelike components into the intelligent robot controller. Moreover, it should extend to other applications.