Using a fuzzy supervisor to optimize multiple criteria in redundant robots

Abstract

Kinematically redundant robots are robots that have more degrees of freedom than necessary to complete a desired task. Traditionally, the extra degrees of freedom have been used to optimize a single criterion such as joint torques minimization, obstacle avoidance, or minimization of flexible base vibrations. Because these approaches do not consider hardware limitations such as joint and rate limits, optimizing a single criterion often leads to high joint velocities and instabilities. To overcome these problems, it has been suggested that multiple criteria be optimized. Although optimizing multiple criteria offers the possibility of stabilizing joint solutions, it is difficult to choose the required weights associated with each criterion. This paper presents results using a fuzzy logic supervisor to decide the relative importance of each criterion and compute time-varying weights.