Intelligent jamming region division with machine learning and fuzzy optimization for control of robots part micro-manipulative task
An algorithm for an intelligent jamming region division with machine learning and fuzzy optimization for the control of a robots part micro-manipulative task is introduced. A comparison with existing works and the advantages of the proposed algorithm in this paper are described. A quasi-static part mating (micro-assembly) is accomplished using a fuzzy coordinator combined with a learning algorithm of the jamming region division while avoiding jamming. Depending on the positional relationships between a part and an assembly hole (target) in a workspace, a specific rule base for avoiding jamming is activated. The region division algorithm merges all adjoining subregions, of which the quad-tuple control values describe similar jamming states, into one region and the weights of the subregions are adjusted. A fuzzy entropy, which is a useful tool for measuring variability and information in terms of uncertainty, is used to measure the degree of uncertainty related to an execution of the part micro-assembly task. A degree of uncertainty associated with a task execution of the part micro-assembly is used as a criterion of optimality, e.g. minimum fuzzy entropy. Through a decision-making procedure, the most appropriate quad-tuple control value with the lowest fuzzy entropy in each region is chosen as a final control value to carry out an assigned task. The proposed technique is applicable to a wide range of the robots tasks, including choosing and placing operations, manufacturing tasks, part mating with various shaped parts, etc.
intelligent jamming region division fuzzy optimization part mating subregion weight machine intelligence