Research on multi-objective optimization in improving the position and attitude accuracy of industrial robots

In the field of industrial production, improving the pose accuracy of industrial robots is crucial for ensuring product quality and production efficiency. This paper focuses on enhancing the pose accuracy of industrial robots through multi-objective optimization methods. Taking the PRRR industrial robot as an example, this paper first constructs an error model based on MDH to make its error characteristics clear at a glance; then, it separately builds error fitness functions for position and orientation, introducing the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to accurately identify kinematic parameters; finally, experiments strongly demonstrate the feasibility and effectiveness of the MOPSO algorithm, reducing the average position error by 81.98% and the average orientation error by 66.66%. Compared with single-objective optimization algorithms such as Least Squares (LM) and Ant Colony Search (BSO), the MOPSO optimization algorithm performs better in optimizing both position and orientation errors.