Integrated Optimization for Service Robotic Arms Involving Workspace, Drive Train, Structural Stiffness and Lightweight

To achieve an optimal design, the multi-objective optimization method is usually used. For robotics, the complex structure, multiple parameters and performances bring challenges to perform an integrated optimization. Here, a completely integrated optimization approach for a service robotic arm is proposed. A co-simulation platform is built to analyze the robotic performances from design variables, including the parameterized geometry, kinematics, dynamics, finite element analysis. Each module adequately considers the balance of physical reality and computational cost. Design variables are the length and offset of links, key structural dimensions, and drive train selections. The lightweight and workspace are the optimization objectives. The constraints include the foldability, structural stiffness, collision-free condition and joint torque requirements. Genetic algorithm is used to search the Perato front of the optimization problem. Finally, the results provide the quantitative design suggestion that minor modifications can significantly improve the robot, which demonstrates the application of the proposed approach in the optimal design of robotic arms.