Automatic generation of fuzzy rulebase for robot arm posture selection

When performing a manipulation task, through experience we place our arm in a posture which is best suited for performing that task. Although indices for quantifying the optimality of robot arm postures with respect to task requirements have been formulated in the past, they have not been used in practice for posture selection. The primary reason for this lack of use is that finding the optimal posture according to these indices is a highly nonlinear, high dimensional optimization problem. There are many local minima that are difficult to avoid unless the initial posture (i.e., initial conditions) is already near the true optimum; and there are no guidelines for choosing initial postures. Using a cluster estimation method, we have automatically generated a fuzzy rulebase for selecting the initial posture for a redundant robot arm. From 10,000 training data relating arm posture to arm performance characteristics, 138 rules were generated. In essence, the rulebase of 138 protypical postures; each posture has a fuzzy range of performance characteristics. Given a set of desired performance characteristics (e.g., move fast in the x direction, exert force in y direction) as the input to the rulebase, the degree of fulfillment of each rule is the degree to which the posture´s performance characteristics matches the task requirements. The posture with the highest degree of match is selected as the initial posture