AN ADAPTIVE GENETIC ALGORITHM FOR REAL-TIME ROBOTIC TRAJECTORY TRACKING

The paper presents a genetic algorithm approach to real-time trajectory tracking of redundant and non-redundant manipulators. The joint angle trajectories are found by applying genetic operators to a set of suitably generated configurations so that the end-effector follows a desired workspace trajectory accurately. The probability of applying a particular genetic operator is adapted on-line to achieve fast convergence to the solution. The adaptation is based on two measures, namely, diversity and fitness of the generated configurations. In order to achieve fast tracking, special provisions are made so that only an appropriate small region in the joint space is searched. The tracking problem is solved at the position rather the then velocity level. As such the proposed method does not use the manipulator Jacobian inverse or pseudo-inverse matrix and is shown to be free from problems such as excessive joint velocities due to singularities. Simulation results are presented for the 6-DOF Puma and the 7-DOF Robotic that show good tracking accuracy and reasonable joint velocities.