Intelligent control planning strategies with neural network/fuzzy coordinator and sensor fusion for robotic part macro/micro-assembly tasks in a partially unknown environment

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This paper addresses the problems of the robotic part assembly in a partially unknown environment. The process can be broken down into two phases. First, a macro-assembly, locating various shaped assembly holes or receptacles (targets) in the workspace corresponding to the shapes of the parts and then bringing the part to the corresponding target, despite existing obstacles. This is accomplished by combining a neural network control strategy coordinating with a mobile multiple optical sensor as well as fuzzy optimal controls. Depending on topological relationships among the part’s present position, the position of obstacles, and the target position in the workspace, a specific rulebase from a family of distinct fuzzy rulebases for avoiding obstacles is activated. 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 assembly task. Second, a micro-assembly, placing the part at a position that is ready to mate successfully with the target without jamming. Depending on a mating type, namely, a rightside, a leftside, or a straight approach toward a target, which is determined by fusing sensor information obtained by optical sensors, a specific rulebase is activated. Fuzzy set theory is used to address the uncertainty associated with the macro and the micro-assembly procedures. Using the control of a robotic part assembly task as an example, a systematic method, not a heuristic one, that can determine an optimal rulebase among feasible fuzzy rulebases which can execute the part assembly task successfully, based on a fuzzy entropy is introduced. The degree of uncertainty associated with the part assembly tasks is used as an optimality criterion, e.g. minimum fuzzy entropy, for a specific task execution. The results show the effectiveness of the above methodologies. The proposed technique is applicable to a wide range of robotic tasks including part mating with various shaped parts, pick and place operations, and motion planning.