Accurate measurement of panel and frame poses is essential for achieving automated and high-precision satellite assembly. Feature holes distributed across the satellite's mating surfaces typically serve as assembly and measurement reference points. However, the various types of feature holes, coupled with issues such as high reflectivity, edge occlusion, and surface defects, lead to the limitation of pose measurement accuracy and robustness based on feature holes. To address this challenge, a high-precision component pose measurement method based on stereo vision is proposed. First, a contour splitting and merging method based on geometric structural consistency (GSC) is developed to ensure low noise and high integrity of the contour. Next, to enhance feature-hole recognition accuracy and efficiency, a partitioned weighted sampling and iterative consistency (PWS-IC) strategy is introduced. Finally, to further improve measurement robustness, a component pose estimation method is proposed that effectively reduces the impact of outliers on pose estimation accuracy. Experimental results demonstrate that the measurement accuracy of feature holes reaches 0.03 mm, while the position and attitude estimation accuracy of satellite components attain 0.05 mm and $1.1<^>{\circ } \times 10<^>{-{5}}$ , respectively. Compared with existing methods, the proposed approach achieves substantial improvements in both accuracy and robustness. When applied to satellite assembly, the achieved mating accuracy reaches 0.15 mm, satisfying the technical requirement of 0.2 mm.

Taking human-robot collaborative assembly as an example, the methods based on contact forces can improve the assembly efficiency of industrial robots with large components in industrial manufacturing. However, due to the large size, high payload, assembly accuracy and dynamic changes in grip position, accurately estimating the contact forces between the payload and the operator becomes challenging when handling these large components. In this paper, a two-stage method is proposed for payload dynamic parameter identification. The parameter identification equation in the sensor coordinate system is initially established. Furthermore, the identification model of recursive restricted total least squares (RRTLS) based on total least squares (TLS) is constructed to achieve low-consumption online identification. According to the assembly requirements and payload characteristics, the posture coordinate system is designed for safety, including the feasible workspace for the robot. Subsequently, the static identification postures and dynamic excitation trajectory are planned to obtain static values and dynamic inertial parameters. In the end, a high-payload human-robot collaborative assembly system is built to validate the proposed method. Experimental results show that compared with the existing methods, the proposed approach can effectively identify and compensate the payload, leading to more accurate external force sensing.

In industrial robot visual servoing, the accuracy of end-effector pose directly affects the feedback quality and trajectory tracking performance of the visual servo system. To improve end-effector pose estimation accuracy, this paper establishes a propagation model from marker measurement errors to end-effector pose estimation errors and further derives the covariance expression of pose estimation errors. Based on this model, different marker placement factors affecting translational and rotational errors are analyzed, including marker-set spatial range, spatial distribution balance, and centroid offset distance. In addition, the influence of the number of markers on pose estimation errors is derived by adding a new marker to an existing point set. The accuracy of the analytical model is validated through Monte Carlo simulations and experiments, and guidelines for marker placement and marker number are provided.

The traditional curve parameter synchronization method cannot constrain the kinematic parameters of the linear motion and the angular motion of the industrial robot at the same time. This may cause sudden changes in angular motion, which in turn leads to vibration of the robot and affects machining quality. Therefore, a real-time synchronous velocity planning method for robots’ smooth trajectories is proposed in this paper to improve the shortcomings of the curve parameter synchronization method. The coordinate parameterization of angular motion is carried out by using the logarithmic quaternion description, which reduces the difficulty of quaternion planning. To avoid iterative interpolation of higher-order curves, the G2 continuous corner smooth curve is constructed by an arc-length parameterized clothoid curve. Based on the S-shaped velocity curve and time synchronization constraints, a time synchronization velocity planning method is proposed to improve the overall interpolation efficiency by avoiding excessive reduction of the start and end velocity of the synchronization segment. By combining independent velocity planning with synchronous velocity planning through the backtracking nesting method, this method not only eliminates the velocity fluctuation problem caused by the time rounding strategy but also realizes the synchronous velocity planning of linear and angular motions of continuous trajectories. Simulations and experiments show that the proposed method achieves more efficient synchronous velocity planning of linear and angular motion, avoids the velocity fluctuation problem caused by time rounding error, ensures that all kinematic parameters are within the constraint range, and effectively reduces the vibration at the end of the robot.

The use of multiple robots to manufacture composite components represents a critical development direction for fiber placement systems (FPSs). In multi-robotic fiber placement systems (MRFPSs) with heterogeneous mechanical structures, robots collaborate to perform fiber placement tasks. Consequently, robot synchronization emerges as a primary factor in determining the performance of the fiber placement process. However, the difficulty in establishing accurate system models and the presence of disturbances are two significant challenges to achieving precise robot synchronization. Additionally, the system is expected to exhibit desirable dynamic characteristics, such as finite-time error convergence. To address these issues and requirements, we propose a novel adaptive finite-time synchronization control (AFSC) algorithm for the system. Specifically, a finite-time sliding mode observer is developed to handle kinematic uncertainty. A novel fast non-singular terminal sliding mode (FNTSM) manifold is constructed in the AFSC algorithm. Moreover, the control algorithm integrates an adaptive law to handle dynamic uncertainty and an adaptive term to counteract disturbances. Performance analysis demonstrates that the AFSC ensures that the coupled, synchronization, and tracking errors converge to zero within finite time. Furthermore, simulations and experiments are conducted to validate the effectiveness of the AFSC algorithm.

With the evolution of human–robot collaboration in advanced manufacturing, multisensor integration has increasingly become a critical component for ensuring safety during human–robot interactions. Given the disparities in range scales, densities, and arrangement patterns among multisensor data, such as that from depth cameras and LiDAR, accurately fusing information from multiple sources has emerged as a pressing need to safeguard human–robot safety. This paper focuses on LiDAR and depth cameras, addressing the challenges posed by the differences in data collection range, point density, and distribution patterns which complicate information fusion. We propose a heterogeneous sensor information fusion method for human–robot collaborative environments. To solve the problem of substantial differences in point cloud range scales, a moving sphere space coarse localization algorithm is introduced, narrowing down the scale of interest based on similar features. Furthermore, to address the challenge of significant density differences and low overlap rates between point clouds, we present an improved FPFH coarse registration algorithm based on overlap ratio and an enhanced ICP fine registration algorithm based on the generation of corresponding points. The method proposed herein is applied to the fusion of information from a 64-line LiDAR and a depth camera within a human–robot collaboration scene. Experimental results demonstrate an absolute translational accuracy of 4.29 cm and an absolute rotational accuracy of 0.006 rad, meeting the requirements for heterogeneous sensor information fusion in the context of human–robot collaboration.

Manufacturing and positioning errors, as well as workpiece deformation, often lead to inadequate edge milling accuracy for large and complex curved thin-walled parts. To address this challenge, a novel robot edge milling method driven by measured data is proposed, which introduces two key innovations. First, a laser profile scanner is utilized to sequentially extract the edge profile features of the workpiece near the processing reference line, enabling real-time generation of robot milling trajectories based on the measured data. Second, advanced feature extraction techniques, including Harris corner detection and the gray barycentric method, are employed to accurately identify and process edge features. The milling path is segmented based on arc length, and the robot posture at each segment endpoint is calculated. A curve smoothing algorithm is then applied to generate the final robot edge milling trajectory. Experimental results demonstrate the effectiveness of the proposed method, with the spatial position error of the converted coordinates at the processing points being less than 0.2 mm, the contour accuracy of the robot edge milling better than ± 0.3 mm, and the normal accuracy within 3°. This method significantly enhances the processing efficiency and accuracy of large curved thin-walled parts, offering a new intelligent manufacturing solution for the aerospace industry.

Industrial robots are widely used due to their high flexibility and cost-effectiveness; however, the precision limitations hinder their applications in mechanical processing. A critical factor affecting precision is the joint transmission system, where error sources such as nonlinearity, backlash, and radial clearance remain insufficiently addressed. Based on mechanistic analysis and test validation of the residual errors after secondary encoder system (SES) compensation, this study proposes a composite control strategy that integrates SES feedback with crosstalk compensation to address dynamic disturbances. The approach begins with joint detection using high-precision gratings, establishing an angular measurement error compensation model to correct SES measurement errors. Subsequently, feedforward control is implemented based on the crosstalk error model to mitigate the impact of axis-to-axis crosstalk and joint radial clearance on end-effector positioning accuracy. Finally, experimental results demonstrate a significant enhancement in robotic accuracy, achieving multidirectional repeatability of +/- 0.08 mm and circular trajectory accuracy of +/- 0.2 mm. This study effectively addresses flexible deformation caused by rotational and radial clearances, enhancing robot positioning accuracy through an innovative crosstalk compensation mechanism and providing a novel solution for high-precision robotic machining.

To eliminate the tangential discontinuity of continuous line segments and time round-off error of velocity planning in numerical control machining, a dynamic velocity planning methodology incorporating round-off error elimination strategy for micro line segments is proposed in this paper. The corner smoothing curve is constructed by the arc-length parameterized clothoid curve to obtain the curve of continuous curvature. More importantly, the corner curve is dynamically reconstructed relying on the suboptimal solution of the best corner velocity to improve the planned corner velocity. Based on the asymmetric S-shape velocity curve, a round-off error elimination strategy with quadratic correction is proposed for the time rounding error in velocity planning. The interpolation time is adjusted to ensure it is an integer multiple of the interpolation period. By integrating the round-off error elimination strategy with the look-ahead velocity planning strategy, a look-ahead planning strategy with bidirectional backtracking is proposed to realize continuous velocity planning and solve the problem of jerk exceeding the limit caused by the round-off error elimination strategy. Simulation and experiment show that the algorithm proposed in this paper can effectively realize online corner smoothing and velocity planning of continuous line segments, eliminate velocity fluctuations caused by time rounding errors, and ensure that kinematic parameters do not exceed the allowable range.

Robotic manufacturing systems offer significant advantages, including increased flexibility and reduced costs. However, challenges in trajectory planning, error compensation, and system integration hinder their broader application in additive manufacturing. To address these issues, this paper proposes a generalized non-parametric trajectory planning method tailored for robotic additive manufacturing. The proposed trajectory planner incorporates chord error and speed continuity constraints and integrates the look-ahead planning with real-time interpolation in a parallel structure to ensure smooth transitions in the robot's trajectory. Additionally, a real-time path tracking control method is introduced, combining RBF neural network-based dynamic feedforward control with visual servoing-based feedback control. This control strategy significantly improves the robot's tracking accuracy, particularly for complex additive manufacturing paths that involve multiple short connected line segments and frequent speed variations. The effectiveness of the proposed methods is validated through experiments on a robotic additive manufacturing platform. The experimental results (line segment, circular arc segment, and continuous path tracking) show that the robot's tracking error remains within $\pm$ 0.15 mm and $\pm 0.05<^>{\circ }$ .

Due to deformation in large composite components and trajectory errors in industrial robots, it is difficult to meet the machining accuracy requirements for large composite components processed by edge milling robots. To address this issue, a strategy for enhancing the machining accuracy of large composite components based on a line laser profilometer and a binocular vision tracking system is proposed. To accurately extract the center of scale lines, a sliding window and model matching (SW-MM) extraction method is designed. A combined weighted random sampling consistency and least squares (WRANSAC-LS) method is developed for normal estimation to mitigate the impact of surface curvature and measurement noise on estimation accuracy. Subsequently, in terms of visual servo control, an adaptive sliding mode controller (ASMC) is designed to further enhance the trajectory accuracy of the milling robot. Finally, a series of validation experiments is conducted on the designed milling robot system platform. Experimental results demonstrate that the strategy proposed in this paper reduces machining errors in large composite components to +/- 0.2 mm, achieving a 90 % improvement in precision, meeting the machining precision requirements for composite components in the aerospace industry.

Industrial robots often face challenges in high-precision tasks due to low absolute positioning accuracy. While model-based parameter identification is commonly used for calibration due to its simplicity and cost-effectiveness, it lacks a clear basis for quantifying multifactor influences and conducting sequential error identification. This article proposes a novel stepwise calibration method that leverages sensitivity analysis to address multisource errors. The method identifies primary factors affecting accuracy, evaluates correlated parameters, and conducts sequential identification of errors. Experimental validation on the CR 20 and other robots demonstrates the method’s superior performance in both accuracy and robustness, highlighting its universality and suitability for heavy-load applications. Across all experiments, this method reduces the average error by over 86%, significantly outperforming conventional calibration techniques.

Benchmark feature detection is critical in mobile robot automatic drilling systems for compensating robot accuracy and assembly errors in aerospace manufacturing. System accuracy is influenced by reference feature recognition, which is often hindered by material interference and background noise. To address these issues, this paper proposes a method that uses a 2D industrial camera for image capture, applies deep learning for initial target recognition and positioning, and then determines the feature extraction location based on the initial recognition. The extracted benchmark positions are accurately fitted using an improved Huber algorithm. Experimental results demonstrate that this approach improves the benchmark feature detection recognition rate by 43.8%, center recognition accuracy by 78.26%, and overall hole processing accuracy by 54.69%.

Carbon fiber reinforced plastics (CFRP) are widely used in aerospace components, and trimming is an essential process in their manufacturing. In the trimming process, robots are increasingly employed due to their flexibility, cost-effectiveness and extensive processing range. A critical factor that directly affects the geometric accuracy in CFRP parts trimming is the contour error. However, the multisource errors of robots lead to processing contour accuracy defects, especially for free-form nonparametric splines and continuous paths. This article proposes a switching geometrical relations real-time contour error estimation technique. A projection ratio is introduced to improve the solution accuracy of contour error. Subsequently, a double-loop cascade contour error control method based on singular perturbation techniques is proposed. The inner loop utilizes a secondary encoder to compensate for joint flexibility by tracking control, while the outer loop is based on contour error estimation to implement pose correction control in Cartesian space for the desired contour. The estimation and control methods were validated by continuous pentagonal and space S curve paths. The results reveal that the discrepancies of contour error estimation are small within 0.03 mu m and calculation time is less than 0.1 ms. The contour error is less than +/- 0.17 mm, representing a 90% improvement.

To improve the efficiency of multi-material additive manufacturing and enhance the safety of multi-robot cooperative printing with uncertain execution delays, a dual-robot cooperative path planning method is proposed for layer-by-layer printing. In the proposed algorithm, the description of the printing region is reconstructed by simply using the rectangular envelope region and a two-dimensional directed line segment. The adjacency list of the printing region is established to guide the optimization direction. Therefore, redundant information about the printing region is effectively removed, which is conducive to the optimization of the problem. A multi-round cooperation strategy with multiple synchronous starting points is proposed to accommodate uncertain execution delays by separating the space of the dual-robot printing area, so as to avoid the potential collision risk of dual-robot. To further optimize the printing efficiency, local strategies are used to reduce the makespan. Hence, a better printing order can be obtained, and states of cooperative and non-cooperative printing can be unified. In addition, the corresponding NC control strategy is designed for the industrial application of the cooperative strategy. The simulation result shows that the method proposed in this paper can effectively reduce the makespan of dual-robot cooperative additive manufacturing, and accommodate the uncertain execution delays of the dual-robot.

In this article, an adaptive iterative learning control (AILC) scheme has been proposed to enhance the accuracy of the dynamic path tracking of 6-degrees of freedom industrial robots. Based on the memorized data and current feedback from a three-dimensional visual measurement instrument, an adaptive algorithm is developed to update the time-varying control parameters of the AILC scheme iteratively. A new compensation signal is calculated to adjust the control inputs produced by the dynamic path tracking control module at each time interval. Through the adaptation algorithm, the identical initial conditions can be relaxed to some extent with the AILC scheme. Moreover, the stability analysis of the proposed AILC scheme is presented. Experimental results on FANUC M20iA, using C-Track 780 as a photogrammetry sensor, demonstrate the superior performance of the developed AILC scheme in terms of pose accuracy, disturbance rejection ability, and control performance.

Vision-based robotic trajectory tracking control is considered a promising technology. However, the slow sampling rate and latency of the vision sensor enormously limit the tracking performance. To conquer the issues, this paper proposes a dual-space error-state Kalman filter (DS-ESKF). By combining the encoder measurement with the vision measurement, the end-effector's pose between adjacent vision measurements is restored, and the pose estimation cycle is synchronized with the control cycle. The critical distinguishing of DS-ESKF is that the encoder-driven error-state kinematics for the industrial robot is reconstructed. The experimental results on the Staubli TX60 industrial robot show that compared with the existing dual-rate Kalman filters (DR-KF), DS-ESKF can reduce estimation errors by about 50 % and have robust estimation performance. By applying the trajectory tracking control scheme combining DS-ESKF with a simple PID controller to Staubli TX60, the tracking accuracy is significantly improved (+/- 0.11 mm for position and +/- 0.05 degrees for orientation).

Flexible and efficient multi-robot systems are being increasingly used in the field of aerospace manufacturing. The task planning considers solely the objective of productivity has been well studied in previous works, however, when dealing with high-quality machining of complex products, combining the objective of productivity and machining performance in planning is an issue that requires further discussion. This paper proposes a two-objective optimization technique for the MRS that undertakes the machining mission of large spacecraft components, where the robot base positions and task assignment are taken as optimization variables, and a multi-objective algorithm based on NSGA-II is introduced, efficient execution of the algorithm is guaranteed by specially designed crossover and correction operators. A test case of spacecraft structure machining mission shows that the algorithms find both productivity-optimal and machining performance-optimal solutions, and an appropriate trade-off between productivity and machining performance should be more desirable in real-world applications.

In the process of satellite assembly, there are errors in the installation and positioning of the cabin board and the frame, which leads to the deviation between the position of the screw screwed by the teaching robot and the actual position of the screw hole. In order to solve this problem, a method of robot screw position and orientation correction based on monocular vision is proposed. The Eye-in-Hand structure was used to capture images online by monocular camera. After filtering and denoising the images, the Canny operator was used to extract the feature contour of nail holes. In view of the fracture of the nail hole feature contour, a fracture contour similarity splicing algorithm was proposed, so as to retain the original information of the nail hole more comprehensively. The image was upgraded to the sub-pixel level, and the position of nail holes in the image was extracted with high precision through sub-pixel edge detection. The homologous relation between the actual image and the desired image is calculated to estimate the pose of the robot when taking the desired image. According to the prior relationship between the desired pose when the robot takes a picture and the desired pose when the robot tightens the screws, the actual robot's tightening screw position was corrected. The deviation threshold of the image and the error threshold of the robot kinematics were set, and then the correction times of the position of the screw tightened by the robot were constrained. Finally, the experimental results show that the proposed method can meet the accuracy requirements of robot screw nails in the satellite assembly process.

Compared with a single manipulator manufacturing cell, a dual manipulator cooperative system has more advantages in reconfigurability and flexibility. However, there are calibration errors and multi-source disturbances in the collaborative process, which lead to the processing trajectory accuracy defects of large-scale associated machining features. To solve the above problems, a practical path tracking synchronous control algorithm based on position-based visual servoing (PBVS) is proposed in this paper for the dual manipulator cooperative system, the proposed dynamic path tracking cross-coupled sliding mode controller (PTCSMC) scheme can realize dynamic paths correction while executing the pre-planned paths. In addition, for the cross-coupled technology to be applied into the proposed control algorithm for dynamic path tracking based on the real-time feedback of the highly repeatable 3D visual measurement instrument (VMI), the tracking and synchronous errors of the dual manipulators converge synchronously to zero. Finally, the stability of proposed control algorithm is proven by the Lyapunov method. In the end, the real-time line and circle path tracking experimental results using two industrial manipulators demonstrate the effectiveness of the proposed algorithm.