Trajectory Planning and Learning of A Redundant Manipulator with Structured Intelligence

Abstract This paper deals with trajectory planning and motion learning for a redundant manipulator. Recently, behavior engineering for robotic systems has been discussed as a new technological discipline. A robotic system requires the whole structure of intelligence, and acquires skill and knowledge through interaction with a dynamic environment. Consequently, the whole structure determines the potentiality of intelligence. This paper proposes a robotic system with structured intelligence. Based on perceptual information, a robot with structured intelligence makes decision and action from four levels in parallel. In addition, the robot generates its motion through interaction with the environment, and at the same time, gradually acquires its skill based on the generated motion. To acquire skill and motion, the robot requires internal and external evaluations at least. This paper applies a virus-evolutionary genetic algorithm for trajectory planning and applies neural network for motion learning. Furthermore, we discuss its effectiveness through computer simulation results.