Double-parameter regression design of drive trains for lightweight robotic arms

Abstract

This paper presents a new design approach for lightweight robotic arms. In this method, the drive trains and structural dimensions are parameterized as design variables, and a major objective is to minimize the total mass of robotic arms satisfying the constraint conditions. To solve the optimization problem, the relationship among mass, the moment of inertia and torque of drive trains are introduced as their power-density curves, which is the basis of the double-parameter regression design. In this design approach, there are two modules: structure optimization and drive trains optimization. The orthogonal design method is adopted to implement the structure optimization. The double-parameter regression design is used for drive trains optimization. Finally, a design example for a four degree of freedom (DOF) robotic arm is demonstrated to verify the validity of the proposed scheme.