COMPARISON OF OPTIMAL PATH PLANNING ALGORITHMS FOR INTELLIGENT CONTROL OF ROBOTIC PART ASSEMBLY TASK
Two intelligent part-bringing (path planning or finding) algorithms, to bring a part from an initial position to an assembly hole or a receptacle (target or destination) for a purpose of part mating, related to a robotic part assembly task are introduced. These algorithms are then compared through simulations and several 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-bringing task. Fuzzy set theory, well-suited to a management of uncertainty, is introduced to address an uncertainty associated with the part-bringing procedure. A degree of uncertainty associated with a part-bringing is used as an optimality criterion, or cost function, 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, to overcome environmental uncertainty. The algorithms use knowledge processing functions such as machine reasoning, planning, inferencing, learning, and decision-making. The results show the effectiveness of the proposed approaches.
comparison path planning algorithms uncertainty fuzziness optimality criterion fuzzy entropy fuzzy theory versus neural network machine intelligence vision sensor