Inverse kinematics of redundant robots using genetic algorithms

Genetic algorithms, which are robust general-purpose optimization techniques, have been used to solve the inverse kinematics problem for redundant robots. A genetic algorithm (GA) was used to position a robot at a target location while minimizing the largest joint displacement from the initial position. As currently implemented, GAs are suitable for offline programming of a redundant robot in point-to-point positioning tasks. The GA solution needs only the forward kinematic equations (which are easily developed) and does not require any artificial constraints on the joint angles. The joint rotation limits which are present in any feasible robot design are handled directly; so any solution determined by the GA is physically realizable. Finally, the GA works with joint angles represented as digital values (not continuous real numbers), which is more representative for computer-controlled robot systems