Robotics and Computer-integrated Manufacturing最新文献

Robotic machining could provide a solution for removing supports from metal additive manufactured workpieces, replacing labor-intensive work. However, the robot’s intrinsic weaknesses of low positioning accuracy and structural rigidity primarily restrict its applications. Improving the accuracy of robotic machining remains an unresolved issue. A mixed solution is proposed, in which a portable CNC machine with the capability of visual feature recognition is equipped with a universal industrial robot. The robot implements positioning motions in a large space, while the portable CNC fulfills accurate machining motions on a local feature of the workpiece. A sizeable weight of the portable CNC exerts a moderate load on the industrial robot’s joints, increasing joint stiffness. The mixed machining system exhibits high accuracy and stiffness when milling a steel/titanium alloy workpiece, achieving tolerances up to ±0.04 mm on a 60×80 mm U-shaped path without exciting any structural vibration modes. When the dimension of the workpiece exceeds the machining range of the portable CNC, a combined algorithm of coarse-fine registration based visual identification and robot error compensation is designed to align the spatial coordinates of the machining motion with that of the positioning motion, thereby extending the machining range with high accuracy. Through the proposed mixed robot machining method, experiments of doubling the machining range have been done to verify that the mixed machining robotic system is able to slot a 550 mm-long path with accuracy of ±0.1 mm. Furthermore, the mixed robotic machining system is applied to recognize and remove multiple supports of lattices, grids and ribs from a titanium-alloy additive manufactured thin-wall workpiece with high accuracy and high efficiency.

Visual recognition of weld beads is essential for post-weld robotic grinding. The recognition of thin-walled weld bead boundary, especially the backside boundary, remains challenging due to the diverse features such as debris, misalignment, and deformation. Based on point cloud from a laser scanner, we present a robust and accurate backside boundary recognition method for girth weld beads of thin-walled pipes. A boundary point extraction method is designed based on an adaptive sliding window model. Without prior morphology features, the influence of misalignment and deformation on the accuracy of boundary point recognition is greatly reduced by the local model matching strategy. Leveraging the correlation among overall weld bead features, an anomalous boundary point recognition and correction method based on DBSCAN clustering is proposed to further enhance robustness. A series of validation experiments were conducted by the obtained backside point cloud data inside a girth weld pipe, and our proposed method showed a high accuracy and a high robustness to misalignment, deformation and debris features.

The process design intent is the concentration of the technologists’ design cognitive process which contains the experiential knowledge and skills. It can reproduce technologists’ design thinking process in process design and provides guidance and interpretability for the generation of process results. The machining process route, as a core component of a part's entire manufacturing process, contains substantial process design intent. If the process design intent embedded in the existing process route can be explicitly identified, subsequent technologists will be able to learn and understand the original designers’ thinking, methodologies, and intents. This understanding enables effective reuse of design thinking and logic in the process design of new parts, rather than merely reusing data. It can also promote the propagation of the expertise and skills inherent in the process design intent. However, existing research on process design intent lacks a detailed explanation of its formation and specific structure from the design cognition perspective, making it challenging to effectively predict the process design intent containing interpretable empirical knowledge in the process route. To address this issue, this paper provides a method for predicting process design intent in the process route using heterogeneous graph convolutional networks. First, the heterogeneous graph is used to represent the parts and their associated process routes in the dataset. The nodes in the graph are then labeled based on accumulated and summarized process design intent. The prediction of process design intent in the process route is then converted into a node classification issue with heterogeneous graphs. A node classification network model is constructed using a heterogeneous graph convolutional network where the input is the created heterogeneous graph, and the output is the design reason contained in the machining feature and the intent cognition embedded in the working step, both of which are part of the process design intent. After training, the proposed model accurately predicted design reasons for machining features and intent cognitions for working steps (95.13 % and 96.85 %, respectively). Finally, examples of actual process routes are analyzed to verify the method's feasibility and reliability. The method given in this article can help technologists gain a deeper understanding of process route generation, hence improving their process design capabilities.

Human–robot collaborative polishing can integrate the capabilities of humans and automation to deal with complex polishing tasks. Traditional impedance-control-based human–robot collaboration (HRC) requires operators to physically interact with robots for a good polishing performance, which brings unsafety to operators. To address this issue, a corrective shared control architecture using haptic feedback is proposed in this paper, where the direct force-reflection is used to guarantee the exact human-intention intervention. The proposed control architecture is designed with two layers: (i) the transparency layer in which the direct force-reflection and the human–robot collaborative polishing strategy are implemented; (ii) the passivity layer in which two energy tanks are designed and endowed with master and slave sides and a coupling energy scaling policy is employed to guarantee the passivity of the whole system. Under the proposed architecture, the constant force is adopted to polish normal areas of workpieces, and corrective force based on human intention is applied to deal with unexpected issues. Finally, two groups of experiments are conducted to evaluate the proposed architecture from two aspects: polishing effect and user experience.

Generating model-free grasps in complex scattered scenes remains a challenging task. Most current methods adopt PointNet++ as the backbone to extract structural features, while the relative associations of geometry are underexplored, leading to non-optimal grasp prediction results. In this work, a parallelized graph-based pipeline is developed to solve the 7-DoF grasp pose generation problem with point cloud as input. Using the non-textured information of the grasping scene, the proposed pipeline simultaneously performs feature embedding and grasping location focusing in two branches, avoiding the mutual influence of the two learning processes. In the feature learning branch, the geometric features of the whole scene will be fully learned. In the location focusing branch, the high-value grasping locations on the surface of objects will be strategically selected. Using the learned graph features at these locations, the pipeline will eventually output refined grasping directions and widths in conjunction with local spatial features. To strengthen the positional features in the grasping problem, a graph convolution operator based on the positional attention mechanism is designed, and a graph residual network based on this operator is applied in two branches. The above pipeline abstracts the grasping location selection task from the main process of grasp generation, which lowers the learning difficulty while avoiding the performance degradation problem of deep graph networks. The established pipeline is evaluated on the GraspNet-1Billion dataset, demonstrating much better performance and stronger generalization capabilities than the benchmark approach. In robotic bin-picking experiments, the proposed method can effectively understand scattered grasping scenarios and grasp multiple types of unknown objects with a high success rate.

Automating fabric manipulation in garment manufacturing remains a challenging task due to the characteristics of limp sheet materials and the diversity of fabrics used. This paper introduces an adaptive and optimized robotic fabric handling system, designed to address these challenges. The system comprises an industrial robot, four needle grippers, and a novel adaptive gripper jig system capable of adjusting the positions of the grippers adaptively to accommodate the shape and material properties of the garment fabric parts. To do this, an in-depth analysis of fabric gripping characteristics—accounting for material properties, gripping position, and fabric deformation—is conducted. A two-stage machine learning model predicting fabric deflection and folding is established from the analyzed data. This model is then incorporated into a vision-guided algorithm that determines the optimal gripping points on garment parts using corresponding CAD data. In addition, the exact position of the target fabric part is swiftly recognized via an algorithm that maps the real-time captured images to the CAD-based shape information. The decision-making information—namely optimal gripping points and garment part position—are subsequently transmitted to the robotic system for automated fabric handling process. The performance of the developed algorithms was quantitatively evaluated, and the integrated robotic system verified to be capable of completing garment manufacturing automation by connecting the processes of automatic fabric cutting and sewing.

Owing to the advantages of good flexibility and low cost, robots are gradually replacing manual labor as an effective carrier for the grinding and polishing of aeroengine blades. However, the geometric features of blades are complex and diverse, and the contour accuracy and surface quality requirements are high, making the robotic grinding and polishing of blades still a challenging task. For this reason, this article first designs a new device by integrating different tools, which can achieve full-feature grinding and polishing of blades. Then, in order to improve the accuracy and stability of force tracking during the robotic grinding and polishing processes, a variable impedance control approach with simultaneous changes in stiffness and damping and parameter boundaries is proposed. Finally, the superiority of the proposed variable impedance control method is verified by comparative experiments on surface tracking. In addition, by combining the device with the variable impedance control method in the robotic grinding and polishing experiments of an aeroengine blade, their effectiveness in practical situations is confirmed.

This article focuses on improving the compliance, efficiency, and robustness of batch precision assembly of small-scale pegs flexibly absorbed by a suction cup. The main contribution is that a force–vision fusion fuzzy control method (FVFFC) is proposed to achieve precision assembly with unknown clearance or interference fit. Both visual and force features are designed to describe the state of the peg and hole with the deformation of the suction cup. Then, a force–vision fusion control framework is proposed, where the visual features dynamically modify the reference position of admittance control and guide compliant adjustment of the peg angles. Furthermore, based on theoretical analysis, two fuzzy logic inference modules are developed to estimate the contact state as well as either the clearance or interference amount between the peg and hole in order to adaptively tune the control parameters. Finally, sufficient experiments are conducted to demonstrate the superiority and robustness of the FVFFC method.

This study presents the Reconfigurable and Responsive Robot Manufacturing (R3M) architecture, a novel framework engineered to autonomously adapt to fluctuating product variants and demands within manufacturing environments. At the heart of R3M lies an integrated architecture that ensures a seamless data flow between critical modules, facilitated by an advanced communication platform. These modules are central to delivering a range of services crucial for operational efficiency. Key to the architecture is the incorporation of Automated Risk Assessment aligned with ISO-12100 standards, utilizing ROS2 Gazebo for the dynamic modification of robot skills in a plug-and-produce manner. The architecture's unique approach to requirements definition employs AutomationML (AML), enabling effective system integration and the consolidation of varied information sources. This is achieved through the innovative use of skill-based concepts and AML Class Libraries, enhancing the system's adaptability and integration within manufacturing settings. The narrative delves into the intricate descriptions of products, equipment, and processes within the AML framework, highlighting the strategic consideration of profitability in the product domain and distinguishing between atomic and composite skills in equipment characterization. The process domain serves as an invaluable knowledge repository, bridging the gap between high-level product demands and specific equipment capabilities via process patterns. The culmination of these elements within the R3M framework provides a versatile and scalable solution poised to revolutionize manufacturing processes. Empirical results underscore the architecture's robust perception abilities, with a particular focus on a real-world application in robotic lamination stacking, elucidating both the inherent challenges and the tangible outcomes of the R3M deployment.

In recent years, with the deepening integration of digital twins (DT) and the Industrial Internet of Things (IIoT), solutions based on digital twins have been widely applied in IIoT scenarios. However, most existing solutions tend to overlook the latency issue during the interaction between mobile devices, such as industrial mobile robots (IMR), and their DTs while in motion. Excessive interaction latency can directly impair the real-time response capability and decision accuracy of industrial mobile robots, and in severe cases, it may lead to the failure of intricate industrial tasks. In order to solve the above problems, we propose a digital twin dynamic migration method for industrial mobile robots. Firstly, we design and implement a STGCN-Transformer-based movement trajectory prediction method for IMR to predict the future movement trajectory of IMR and pre-migrate the DT of IMR to all intelligent gateways (IG) within the prediction range. Then, we design and implement a Proximal Policy Optimization-based DT migration time determination method for IMR and obtain the migration timing of DT under the premise of balancing the DT migration overhead, the load of the IG where the DT is deployed, the load of the IG where the DT is connected, and the communication delay between the IMR and the IG where the DT is deployed. Next, the DT of the IMR is migrated based on the IMR’s anticipated trajectory and optimal times for migration, with the objective of minimizing the interaction latency between the IMR and its DT. Finally, we conduct simulation experiments on the proposed method. Through theoretical and simulation experiments, it has been proven that the proposed method can effectively ensure the dynamic interaction delay between the IMR and its DT during the moving process, thereby enhancing the real-time responsiveness and decision precision of the IMR.