Closed-loop time-optimal path planning using a multi-objective diversity control oriented genetic algorithm

摘要

This paper presents the use of a multi-objective diversity control oriented genetic algorithm (MODCGA) for solving a closed-loop time-optimal path planning problem. The MODCGA Is a result of the integration between two types of genetic algorithm: a multi-objective genetic algorithm (MOGA) and a diversity control oriented genetic algorithm (DCGA). The MODCGA is benchmarked against the MOGA and a random search in the path planning problem which Is treated as a multiobjective optimisation problem. In this case, the planning problem is represented by a position control task which is given to a 3-dof revolute joint robot. From the optimisation viewpoint, the decision variables consist of the magnitude of torque limits for each joint and the Initial and final positions of a filed length path at which the robot endeffector has to track. The corresponding search objectives are thus expressed in terms of the position tracking error and trajectory time. Two chromosome coding schemes are explored in this investigation: Gray and integer-based coding schemes. The simulation results suggest that the Integer-based coding scheme is more suitable at representing the decision variables. In addition, the use of diversity control in conjunction with the integer-based coding scheme can further improve the search results.