Comparison of optimal motion planning algorithms for intelligent control of robotic part micro-assembly task

Two intelligent motion (or path) planning algorithms, one based on a neural network and the other based on a fuzzy coordinator, to mate a part with an assembly hole or a receptacle (target) without a jamming related to a robotic quasi-static part micro-assembly task are introduced. These algorithms are then compared by experiment results and several criteria. In the first algorithm, a neural network control strategy with a fuzzy entropy measure for avoiding jamming during the part micro-assembly is presented. An entropy function, which is a useful measure of the variability and the information in terms of uncertainty, is introduced to measure its overall performance of a task execution related to the part micro-assembly task. In the second algorithm, a learning control strategy with a fuzzy coordinator to minimize entropy and eliminate unneeded events in the plan related to avoiding jamming is described. Fuzzy set theory is introduced to address the uncertainty associated with the part micro-assembly procedure. The degree of uncertainty associated with the part micro-assembly is used as an optimality criterion, e.g. minimum fuzzy entropy, for a specific task execution. It is shown that the machine organizer using a sensor system can intelligently determine an optimal control value, based on explicit performance criteria. The algorithms utilize knowledge processing functions such as machine reasoning, planning, inferencing, learning, and decision-making. The results show the effectiveness of the proposed approaches. The proposed techniques are applicable to a wide range of robotic tasks including pick and place operations, motion planning, and part mating with various shaped parts.