Improved robotic assembly by position and controller optimization

A method for the planning of robotic assembly by numerical optimization of position and joint controller coefficients is proposed in this paper. Starting from a detailed modelling of the robot´s dynamics incorporating joint elasticities and damping, scalar optimization criteria are formulated, the minimization of which yields an improved performance during assembly. Together with constraints ensuring practical applicability a nonlinear vector optimization problem is stated for a peg-in-hole insertion task and solved using an SQP-algorithm. Discussion of the Pareto-optimal region for this example shows that the dynamic performance of the robot can be tuned within a wide range according to the specific properties of the mating process by adjusting the robot´s position and its joint controller coefficients. Although finding an absolute minimum of the cost functions requires rather strong restrictions, an automatic optimization routine can be expected to work more effectively than an interactive optimization procedure would do