This paper presents a novel modeling and compensation method for the volumetric errors of a six-axis gantry automated fiber placement (AFP) machine. Based on the screw theory, the forward and inverse kinematics models of the AFP machine are established. In order to improve the accuracy of the inverse kinematics solution, the Paden-Kahan sub-problem method is used to perform the inverse kinematics solution for the simplified topology of the rotary axes. Using error motion twist to establish a volumetric error transfer model for 54 geometric errors. According to the measurement data of a laser tracker and the Levenberg-Marquardt method to identify the geometric error parameters. The explicit formula of the inverse kinematics solution is used to obtain the error compensation of each motion axis, and the G code of the laying path is modified by the iterative method to realize the compensation of the volumetric errors. By comparing the positions of the tool center point before and after the error compensation, the practicability of the volumetric error modeling and iterative compensation methods are verified, and the geometric accuracy of the AFP machine can be effectively improved.

This paper presents a novel kinematic modeling and error parameter identification method for a six-axis gantry automated fiber placement (AFP) machine. Multi-body system theory is used to model the kinematics of the AFP machine, and then use the homogeneous transformation matrix to represent the transfer relationship of the coordinate system. Based on 54 geometric errors of the AFP machine, a volumetric error transfer model is established. To eliminate the training bias caused by sampling randomness, 10-fold cross-validation combined with the Levenberg–Marquardt method is used to train the kinematic model of the AFP machine. Based on the coordinates of random points in space measured by a laser tracker, all the position-dependent geometric errors and position-independent geometric errors of the linear and rotary axes can be identified simultaneously. After the error parameters are identified, the average error of the random points in the X-direction is reduced from 0.72 to 0.08 mm, a drop of 88.9%. Corresponding experiments are carried out on the body diagonals and the winglet placement path. The experimental results show that the volumetric error transfer model and error parameter identification method proposed in the article can accurately predict the volumetric errors of the AFP machine, and can also meet the requirements of the AFP machine placement accuracy.

Visual tracking with a mobile manipulator is required to continuously keep a moving target near the center of the camera’s field of view. Conventional methods generally employ either the mobile base or the manipulator alone for target tracking. However, base-only tracking provides limited task-space motion capability, whereas arm-only tracking is restricted by the finite workspace and joint limits of the manipulator. To address this problem, this paper proposes integrating base–camera alignment and gain-scheduling visual servoing controller into a unified whole-body control frame to generate coordinated motion of the mobile base and the robotic arm for visual tracking. The mobile base and six-joint manipulator are represented as an eight-degree of freedom kinematic system, and their velocities are determined simultaneously by a quadratic program incorporating joint and velocity constraints. Auxiliary objectives regulate arm manipulability, transient motion allocation, and base–camera alignment. The proposed method enables the robot to reactively track a moving target in a three-dimensional workspace while maintaining the desired target distance. Experiments on a mobile manipulator demonstrate coordinated base–arm motion for the tested target-tracking tasks. In an illustrative controller-level step response, gain scheduling reduces the settling time from 7.99 s to 2.39 s relative to the previous fixed-gain configuration.