Time-Jerk Synthetic Optimal Trajectory Planning of Robot Based on Fuzzy Genetic Algorithm

A new approach based on fuzzy genetic algorithm is developed to find the time-jerk synthetic optimal trajectory of robot with a joint space scheme using cubic splines. In order to get the optimal trajectory, cubic splines are employed and derived under the constraint condition of velocity and acceleration of the first and last point, which assure overall continuity of velocity and accelerations on robot motion. Based on cubic splines, the mathematic model of time-jerk synthetic optimal trajectory planning is built taking into account both the execution time and minimax approach of jerk with kinematics constraints expressed as upper bounds on the absolute values of velocity and acceleration. For solving the mathematic model, we designed the set of fuzzy control rules and fuzzy genetic algorithm, using real-coding and elitism approach. Finally, the proposed optimal technique is tested in simulation on a three-degrees-of-freedom glass substrate handling robot. The simulation results show the effectiveness of the algorithm to solve the contradictory problem between high production efficiency and low arm vibration.