A novel trajectory planning method for robotic deburring of automotive castings considering adaptive weights

Abstract

Robotic deburring of automotive castings with irregular geometry poses a great challenge to trajectory planning, as this operation highly determines the machining efficiency and quality consistency. To address the limitation of the existing trajectory planning methods applicable for automotive castings, in this paper a novel trajectory planning method is presented by considering the adaptive weights. Based on the interval mutation rate, LS-gradient (Line Surface-gradient) and stiffness performance, an adaptive weight function is constructed at first to quantify the path complexity as well as the required robot stability during deburring. Then, the robot trajectory is generated by using a cubic polynomial, which aims primarily at the continuous angular acceleration without sudden impacts on the joint angles. Next, by taking the machining efficiency and machining stability as the objective functions, an adaptive weight-based trajectory planning model is established for accurate control of the robot trajectory, and this model is further solved by the evolutionary COVIDOA algorithm which is not easy to fall into local optimum. Finally, comparative experiments on robotic deburring of automotive engine flywheel shell with four diverse paths are conducted under three conditions: machining without trajectory planning algorithm, machining with fixed weight-based trajectory planning algorithm, and machining with adaptive weight-based trajectory planning algorithm. The experimental results demonstrate the effectiveness of the proposed algorithm in terms of machining adaptability and machining effects. The novel trajectory planning method also can effectively enhance both the machining efficiency and machining stability, and shows great application potential in robotic deburring of automotive castings.