In the human–robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results.

Three dimensional (3D) robot measurement systems, with binocular planar structured light cameras (3D cameras) installed at the terminal flange of robots, are widely used to measure complete workpieces by stitching point clouds obtained from various sampled poses. To ensure accurate stitching, hand–eye calibration is necessary. However, 3D cameras often demonstrate low precision in measuring jumping edges and vertices, resulting in challenges when selecting appropriate calibrators and calibration methods for hand–eye calibration. We propose a simple and high‐precision hand–eye calibration method for 3D robot measurement systems. Initially, a single sphere is utilized as the calibrator, and its poses are observed using specific robotic motions. Subsequently, the hand–eye calibration problem is formulated as a well‐known equation, , relying solely on the high‐precision relative pose of the robot. Finally, a novel simultaneous solution for and the corresponding closed‐form initial solution are introduced. Simulations and experiments confirm that the proposed method exhibits high noise resistance and achieves high‐precision calibration, even with a limited calibration data quantity. Compared with traditional methods, the fitting error of the proposed method can be reduced from over 0.9 mm to less than 0.6 mm.

This study proposes a pose and path planning method based on direction fields, addressing challenges in industrial robot surface machining. These challenges include limited machining accuracy and difficult path planning arising from the robot's low stiffness and high nonlinearity between joint and operational spaces. The method consists of two key components. Firstly, a smooth pose planning method is proposed, independent of specific machining paths. It guides robot pose by constructing the smooth 1-direction field on the freeform surface, ensuring smooth pose transitions between adjacent machining points. Secondly, a path planning method based on a 2-direction field is proposed. The approach generates a 2-direction field by considering the robot's performance and task requirements. After integrating the 2-direction field to obtain a scalar field, iso-lines are extracted as the machining paths. The results demonstrate that the proposed method can generate smooth machining poses and continuous paths, even for complex surfaces where other methods may fail to generate fully reachable machining paths. The proposed method could achieve different path performances, such as high manipulator-velocity-ratio (MVR), high tangential stiffness, and high machining efficiency, by tracking specific 2-direction fields.

Robot measurement systems with a binocular planar structured light camera (3D camera) installed on a robot end-effector are often used to measure workpieces’ shapes and positions. However, the measurement accuracy is jointly influenced by the robot kinematics, camera-to-robot installation, and 3D camera measurement errors. Incomplete calibration of these errors can result in inaccurate measurements. This paper proposes a joint calibration method considering these three error types to achieve overall calibration. In this method, error models of the robot kinematics and camera-to-robot installation are formulated using Lie algebra. Then, a pillow error model is proposed for the 3D camera based on its error distribution and measurement principle. These error models are combined to construct a joint model based on homogeneous transformation. Finally, the calibration problem is transformed into a stepwise optimization problem that minimizes the sum of the relative position error between the calibrator and robot, and analytical solutions for the calibration parameters are derived. Simulation and experiment results demonstrate that the joint calibration method effectively improves the measurement accuracy, reducing the mean positioning error from over 2.5228 mm to 0.2629 mm and the mean distance error from over 0.1488 mm to 0.1232 mm.