Concurrent optimization on the powertrain of robot manipulators for optimal motor selection and control in a point-to-point trajectory planning

Abstract

A multi-objective optimization method is proposed for optimal motor selection, control, and planning for a point-to-point trajectory of a robot manipulator, where three objective functions are proposed to be minimized: the total weight of actuators, the execution time and velocity transitions between planned points, and the tracking error of the task. A concurrent approach is proposed where the powertrain dynamics of the robot is taken into account, that is, motor, gearbox, and load at each actuated joint. To solve the concurrent optimization problem, a genetic algorithm is used, where a representative set of non-dominated solutions form the Pareto-front. The method is tested for a 3-degree-of-freedom manipulator by selecting a particular solution.