Fuzzy Inverse Kinematic Mapping: Rule Generation, Efficiency, and Implementation

Abstract

Inverse kinematics is computationally expensive and can result in significant control delays in real time. For a redundant robot, additional computations are required for the inverse kinematic solution through optimization schemes. Based on the fact that humans do not compute exact inverse kinematics, but can do precise positioning from heuristics, we developed an inverse kinematic mapping through fuzzy logic. The implementation of the proposed scheme has demonstrated that it is feasible for both redundant and nonredundant cases, and that it is very computationally efficient. The result provides sufficient precision, and transient tracking error can be controlled based on a fuzzy adaptive scheme proposed in this paper. This paper discusses (1) the automatic generation of the Fuzzy Inverse Kinematic Mapping (FIKM) from specification of the DH parameters, (2) the efficiency of the scheme in comparison to conventional approaches, and (3) the implementation results for both redundant and nonredundant robots.