Robust rigid point cloud registration is effective for accurate positioning and measurement of complex components. The existing registration algorithms, however, fail to overcome the matching distortion caused by structural deviation, unknown abnormal allowance, and various measurement inherent defects. Although the recently proposed VMM and WPMAVM algorithms can inhibit the matching distortion to some extent, they still fail in the presence of numerous abnormal points. In this study, we present a progressive and adaptive variance minimization (PAVM) algorithm to address these issues. A progressive de-pseudo weight is established to ensure the involvement of all point pairs in optimization at the initial registration stage. Then, an approximately truncated weight function is employed to mitigate the influence of abnormal points on registration results. Furthermore, a novel adaptive coordination distance function is established by improving the symmetric point-to-plane distance metric and combining the first-order approximate point-to-point distance metric, which enhances the algorithm speed and stability. The analysis investigates the anti-abnormal interference ability and quadratic convergence, validating the feasibility of the PAVM algorithm. Experiments are undertaken to illustrate the notable benefits of our algorithm in convergence stability, matching speed, and universality. These attributes render the algorithm well-suited for registration tasks involving diverse complex components.

Robotic grinding is utilized for machining large complex components due to its exceptional flexibility and expansive workspace. However, the existing research primarily focuses on the global machining and lacks solutions for repair and remanufacturing of components with local defects. In order to perform the local repair efficiently, this paper proposes a 3D vision-guided robotic grinding framework for repairing random defects, by taking the removal of welding slags on the automotive body as an example. Based on the measured point cloud, a robust function weighted variance minimization (RFWVM) registration algorithm is utilized to position the automotive body with high precision. Meanwhile, the datum plane is reconstructed to extract the defect point clouds. On this basis, the welding slags are divided into regions in accordance with their height characteristics. For these regions, a robotic grinding depth prediction model is then constructed to determine the mapping between welding slag height and process parameters, and the genetic algorithm is further improved to solve the shortest path decision-making problem in robotic grinding. The experimental results conducted in a typical region of an automotive body confirm the practicality and effectiveness of the proposed framework. This study provides a valuable reference for local defects repair of complex components.

This work develops a dynamic compliant force control (DCFC) strategy for the robotic belt grinding system to suppress the vibrations and over-grinding phenomenon. First, the vibration mechanism is investigated, and the corresponding vibration models before and after contact are constructed, both of which can decompose the vibrations into three components: free, accompanying and forced vibrations. Next, the extra compliant hardware is equipped to the grinder to realize the dynamic adjustment of equivalent damping. The DCFC strategy considering mechanical compliance accompanied by the dynamic closed-loop control of the grinder damping is presented based on the empirical wavelet transform and multi-scale permutation entropy. Moreover, the cutting fluctuation ratio index is proposed to evaluate the severity of the over-grinding together with over-grinding time. Experiments demonstrate that compared with the general force control, the DCFC strategy can reduce the vibration amplitude from 3.01 mm/s$^2$to 0.97 mm/s$^2$, and the over-grinding time/cutting fluctuation ratio from 1.05 s/24.71% to 0.54 s/13.18%, consequently enhancing the grinding stability and quality.Note to Practitioners—This work is motivated by the need to maintain the grinding stability and suppress the over-grinding phenomenon for the robotic belt grinding system. The vibrations and over-grinding phenomenon caused by the weak rigidity and poor precision of the robot always result in incomplete grinding of the workpiece that needs partially manual regrinding. The proposed DCFC strategy enables a dynamic compliant contact force between the grinding tool and the robot, thus effectively suppressing the over-grinding phenomenon and enhancing the consistency of grinding quality, which is particularly suitable for considering material removal consistency in cut-in and cut-out areas. This method can be implemented by the user as described or be acquired as a standalone device, but some extra hardware needs to be equipped with the grinding tool to use this method.

Industrial robots are characterized by good flexibility and a large working space, and offer a new approach for the machining of large and complex parts with small machining allowances (extra material allowed for subsequent machining). Parts of this type (such as aircraft skin parts, wind turbine blades, etc.) are easily deformed due to their large scale and low stiffness. Therefore, these parts cannot be directly machined according to the designed model. A feasible method is to plan a robotic machining path by using the point clouds of parts after clamping from onsite measurement which contains inherent defects of measurement such as noise points and abrupt points. In this article, a novel method is proposed to plan and optimize a robotic machining path that meets the requirements of smoothness, dexterity, and stiffness based on the point cloud from onsite measurement. The dual nonuniform rational B-spline curves of the machining path points and tool axis points are generated at first. Next, an objective function of smoothness optimization is established to filter out the local mutation of the path by considering the constraints of both the deformation energy and the deviation. Then, the objective function of robot postures optimization is established to optimize dexterity and Cartesian stiffness of a robot during the machining process. To show the feasibility of the proposed method, simulation and experiments are carried out. It is proved that the proposed method can generate a smooth machining trajectory. The stability of joint rotation and the rigidity and dexterity of the robot are improved during the machining process.

Multirobot systems have shown great potential in dealing with complicated tasks that are impossible for a single robot to achieve. One essential problem encountered in cooperatively working of the multirobot systems is the unknown initial transformation relationships from hand to eye, base to base, and flange to tool. In this article, the problem of multicoordinates calibration for a dual-robot system is formulated to a matrix equation AXB = YCZ. A novel approach for simultaneously solving the unknowns in equation AXB = YCZ is proposed, which is composed of a closed form method based on the Kronecker product and an iterative method which converts the calculation of a nonlinear problem to an optimization problem of a strictly convex function. The closed form method is used to quickly obtain an initial estimation for the iterative method to improve the efficiency and accuracy of iteration. In addition, a series of conditions on the solvability of the problem are proposed to guide the operators to select appropriate robot attitudes during the calibration process. To show the feasibility and superiority of the proposed iterative method, two other calibration methods are chosen to be compared to the proposed method through simulation and practical experiments. The comparison results verify the superiority of the proposed method in accuracy, efficiency, and stability.