Hierarchical trajectory planning of redundant manipulators with structured intelligence
This paper deals with trajectory generation for redundant manipulators with structured intelligence. Recently, behavior engineering for robotic systems has been discussed as a new technological discipline. The intelligence of a robot depends on the structure of hardware and software for processing information, i.e. the structure determines the potentiality of intelligence. This paper proposes a robotic system with structured intelligent based on subsumption-like architecture. Based on perceptual information, a robot with structured intelligence makes decisions and takes 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 to trajectory planning for redundant manipulators with structured intelligence. Furthermore, we discuss its effectiveness through computer simulation results.
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