Comparison of intelligent control planning algorithms for robot's part micro-assembly task
Two intelligent control (path or motion) planning algorithms, based on a neural network and a fuzzy set theory, related to a robotic quasi-static part micro-assembly task are introduced. The part micro-assembly considered in this paper consists in a mating a part with an assembly hole or a receptacle (target) without a jamming. These algorithms are then compared through the utilization of experimentally measured data as well as simulations and a set of criteria. 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. 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 motion planning, pick and place operations, and part mating with various shaped parts.
control planning neural network part micro-assembly (part mating) fuzzy coordinator optimality criterion (fuzzy entropy) uncertainty (=fuzziness) machine intelligence vision and force sensors