Jumping pattern optimization for a serial link robot through soft computing technique

抄録

In this paper, we focus on the maximization of the height of jump of a serial link robot. The jump height maximization problem is formulated as a nonlinear programming problem, where torque patterns to drive joints in the robot are decision variables and the objective function is an inexplicit function whose value is obtained as an output of a simulator. As a previous reasearch, an approximate solution method using a genetic algorithm was proposed. In the research, some interesting joint drive torque patterns were found by the method, but it costed much time to obtain a drive torque pattern. In order to shorten the computational time, in this paper, we propose a new solution method using a particle swarm optimization (PSO) technique.