Robotic aero-engine pipe grasping posture and motion planning method in multi-stage processing based on multi-objective optimization

Abstract

In the aviation industry, the manufacturing of aero-engine bending pipes consists of multiple processes, e.g., straight pipe bending, allowance removal, precision chamfering, and inspection. Currently, the production of aero-engine bending pipes is carried out mainly manually, and uncontrollable human factors can greatly affect the manufacturing accuracy and efficiency. This paper introduces a grasping posture and motion planning method. It can efficiently and accurately perform multi-stage precision manufacturing tasks on aero-engine bending pipe production lines. First, we provide a gravity deformation model for flexible bending pipes and verify its accuracy through finite element simulations and experiments. It can be used to compensate accurately for the motion of bending pipes entering the processing hole along a straight path. We propose a coordinate transformation method and an accurate calibration method. It can achieve precise transformation of the original posture of the computer-aided design (CAD) model and the postures at the processing stations, e.g., the outlet of the clamping mold of the bending machine, the processing holes of the residual removal and the flat end chamfer. Next, we propose converting the linear paths into NURBS interpolation paths and using the S-shaped acceleration/deceleration (ACC/DEC) feed rate planning method. It can convert the velocity, acceleration, and jerk into the corresponding angular velocity, acceleration, and jerk of each joint in the joint space to ensure that processing and inspection tasks can be performed under appropriate kinematic constraints. Then, an improved NSGA-II algorithm is proposed, which can solve the trade-off minimization problem of processing time, energy consumption, and vibration by incorporating multi-objective optimization functions and motion constraints. The simulation and experimental results demonstrated the superiority of optimization method in terms of processing accuracy, precision, stability, and efficiency. The performance of the improved NSGA-II algorithm is compared with that of other multi-objective optimization algorithms, and the superior performance of the algorithm is verified.