Posture optimization for improving the kinematics performance of robotic polishing under combined constraints by using a KC-ADP method

Abstract

The flexibility of industrial robots in posture adjustment has driven their widespread adoption in polishing applications, providing an effective means to enhance machining performance. To satisfy the growing industrial demand for high-quality and high-efficiency machining of complex surfaces, posture optimization must address multiple constraints, including interference-free operation, singularity avoidance, stiffness performance index limits, and the kinematic parameter limits. The simultaneous consideration of all these factors poses a significant challenge, and existing methods do not adequately address the global optimality and the convergence of the solution process. This paper proposes a novel posture optimization model to improve the comprehensive performance of robotic postures by integrating enhancements in both kinematic and stiffness performance under fundamental geometric constraints. An efficient and stable algorithm, designated as Kinematics-Constrained Adaptive Dynamic Programming (KC-ADP), is developed to solve the optimization problem. First, the combined constraints are modeled based on the robotic polishing system. Next, feasible posture solutions corresponding to different redundant parameters are collected according to machining requirements and transformed into a directed graph using the proposed Multi-Constraints Search Space Generation (MCSSG) algorithm. The optimal posture sequence is then obtained through adaptive dynamic programming, ensuring the availability of feasible postures at each step and resolving the conflict between multi-order kinematic constraints and the objective function. A series of simulations and experiments were conducted to validate the proposed method and the results demonstrate that the proposed approach significantly improves machining performance.