Precision Optimization and Path Planning of Industrial Robots Based on Force-Position Cooperative Control in Incremental Metal Forming

Abstract

Industrial robots face challenges in incremental forming, including insufficient precision, significant force fluctuations, and a lack of closed-loop path planning. To address these issues, this study presents an integrated approach combining force-position co-control with error-driven path improvement. First, a dynamic model is created, incorporating contact stiffness and elastic recovery to represent the coupling relationship between end-effector pose and forming force. Then, a discrete observer is formed based on the observability of the error, enabling real-time estimation of nodal normal errors. The estimation results are combined with piecewise segmentation of the forming force gradient and refinement of the error field to obtain an adaptive path planning framework. Experiments on a six-axis robot platform demonstrate that this method reduces the mean square error of the trajectory by approximately 40%, normal force fluctuation by approximately 35%, and contour error in high-curvature regions by approximately 45%. Furthermore, it maintains stable generalization performance under various material and plate thickness conditions, providing a feasible technical approach for high-precision and robust control of robot incremental forming.