Optimal Path Planning of Manipulator Using Optimal Control Combined with Genetic Algorithm

This paper aims at presenting a new two-stage algorithm to find or approaching global minimum for manipulator in optimization problems. It uses a combination of mathematical optimization method and an evolutionary approach. The mathematical method is based on the indirect solution of open loop optimal control problem and the evolutionary method is based on Genetic Algorithm (GA). Some initial guesses generated by GA to produce optimal solution by optimal control are used. Then, the cost function is calculated for every optimal solution and the best solutions are chosen for the next step. In the next step of the algorithm, these solutions are used to produce the new initial guesses. Optimal control problem is then solved for each guess again, and its cost is calculated. This process continues until the minimum cost value is achieved. In order to improve performance of the algorithm, a new GA operator is introduced in addition to the conventional GA ones to select the pair chromosomes for crossover. The proposed method eliminates the problem of optimal control which is trapped in local optimal point and tries to obtain the global minimum. The effectiveness of the method is shown by simulation.