Robotics and Computer-integrated Manufacturing最新文献

With the rapid advancements in three-dimensional (3D) printing, researchers have shifted their focus towards the mechanical systems and methods used in this field. While Fused Deposition Modelling (FDM) remains the dominant method, alternative printing methods such as Spatial 3DP (S-3DP) have emerged. However, the majority of existing research on 3D printing technology has been emphasizing offline control, which lacks the capability to dynamically adjust the printing path in real time. Such an limitation has resulted in a decrease in printing efficiency. Therefore, this paper proposes a human-robot interaction (HRI) method based on real-time gesture control for Robotic Spatial 3DP (RS-3DP). This method incorporates utilization of YOLOv5 and Mediapipe algorithms to recognize gestures and convert the gesture information into real-time robot operations. Results show that this approach offers a feasible solution to address the issue of discontinuous S-3DP nodes because it achieves a gesture-controlled robot movement accuracy of 91 % and an average system response time of approximately 0.54 s. The proposed HRI method represents a pioneering advancement in real-time control for RS-3DP, thereby paving the way for further exploration and development in this field.

Industry 5.0 paradigm has renewed interest in the human sphere, emphasizing the importance of workers’ well-being in manufacturing activities. In such context, collaborative robotics originated as a technology to support humans in tiring and repetitive tasks. This study investigates the effects of assembly complexity in Human-Robot collaboration using physiological indicators of cognitive effort. In a series of experiments, participants performed assembly processes of different products with varying complexity, in two modalities: manually and with cobot assistance. Physiological measures, including skin conductance, heart rate variability and eye-tracking metrics were collected. The analysis of physiological signals showed trends suggesting the impact of assembly complexity and cobot support. One key finding of the study is that a single physiological signal usually may not provide a complete understanding of cognitive load. Therefore, a holistic approach should be followed. This approach highlighted the importance of considering multiple measures simultaneously to accurately assess workers’ well-being in industrial environments.

With the recent vision of Industry 5.0, the cognitive capability of robots plays a crucial role in advancing proactive human–robot collaborative assembly. As a basis of the mutual empathy, the understanding of a human operator’s intention has been primarily studied through the technique of human action recognition. Existing deep learning-based methods demonstrate remarkable efficacy in handling information-rich data such as physiological measurements and videos, where the latter category represents a more natural perception input. However, deploying these methods in new unseen assembly scenarios requires first collecting abundant case-specific data. This leads to significant manual effort and poor flexibility. To deal with the issue, this paper proposes a novel cross-domain few-shot learning method for data-efficient multimodal human action recognition. A hierarchical data fusion mechanism is designed to jointly leverage the skeletons, RGB images and depth maps with complementary information. Then a temporal CrossTransformer is developed to enable the action recognition with very limited amount of data. Lightweight domain adapters are integrated to further improve the generalization with fast finetuning. Extensive experiments on a real car engine assembly case show the superior performance of proposed method over state-of-the-art regarding both accuracy and finetuning efficiency. Real-time demonstrations and ablation study further indicate the potential of early recognition, which is beneficial for the robot procedures generation in practical applications. In summary, this paper contributes to the rarely explored realm of data-efficient human action recognition for proactive human–robot collaboration.

Robotic additive manufacturing using a cold spray deposition head attached to a robotic arm can deposit material in a solid state with deposition rates in kilogrammes per hour. Under such a high deposition rate, the complicated interplay between the robot’s motion, gun standoff distance, spray angle, overlapping, and the interaction of supersonic powder particles with a growing structure could cause overabundance or deficiency of material build-up. Over time, the accumulation of these discrepancies can negatively affect the overall shape and size of the final manufactured object. In-process spatio-temporal 3D reconstruction, also known as 4D reconstruction, could allow for early detection of deviations from the design, thus providing the opportunity to rectify at an early stage, making the process more robust, efficient and productive. However, in-process model reconstruction is challenging due to the dynamic nature of the scene (e.g. sensor and object relative movements), the three-dimensional growth of a time-varying build object, the textureless nature of build surfaces, and its computational complexity. We propose a real-time, in-process 4D reconstruction framework for free-form additive manufacturing processes, such as cold spray that deals with a real-time dynamic and evolving scene built by incremental deposition of materials. In our approach, temporal point clouds from three cameras are acquired and segmented to extract the region of interest (build object). The subsequent multi-temporal and multi-camera registration of the segmented 3D data is addressed by combining geometrically constrained Fiducial marker tracking and plane-based registration without drift accumulation. Finally, the registered point clouds are fused via voxel fusion of growing parts to reconstruct the 3D model of the object with smoothened surfaces. The proposed solution is deployed and verified in a robotic cold spray cell with different test scenarios and shape complexities.

As advancements in transportation equipment intelligence continue, the job shop scheduling problem integrating finite transportation resources (JSPIFTR) has attracted considerable attention. Within the domain of shop scheduling, the neighborhood structure serves as a cornerstone for enabling intelligent optimization algorithms to effectively navigate and discover optimal solutions. However, current algorithms for JSPIFTR rely on generalized neighborhood structures, which incorporate operators like insertion and swap. While these structures are tailored to the encoding vectors, their utilization often leads to suboptimal optimization efficacy. To address this limitation, this paper introduces novel neighborhood structures specifically designed to the distinctive properties of JSPIFTR. These innovative structures leverage the intrinsic structural information in integrated scheduling, thereby enhancing the optimization effectiveness of the algorithm. Firstly, two theorems are presented to demonstrate the feasibility of the neighborhood solution. Secondly, different neighborhood structures for critical transportation and processing tasks are subsequently designed based on the analysis of the problem properties and constraints. Thirdly, an efficient fast evaluation method is developed to expediently calculate the objective value of the neighborhood solution. Finally, the novel neighborhood structures are combined with the tabu search (TS_NNS) and compared with other state-of-the-art methods on EX and NEX benchmarks. The comparative results demonstrate the remarkable performance of the neighborhood structure, with the TS_NNS enhancing the best solutions across 23 instances.

Industrial robots are promising and competitive alternatives for performing machining operations due to their advantages of good mobility, high flexibility and low cost. However, the application of industrial robots in the field of high-precision machining such as grinding is hugely limited by the characteristic of weak stiffness. Aiming at this problem, a whole-path posture optimization method of robotic grinding based on multi-performance evaluation indices is proposed in this paper. Firstly, a kinematic performance evaluation index is utilized to directly refine the regions of the robot workspace. Secondly, a stiffness performance evaluation index comprehensively considering the characteristics of grinding process is put forward. Simultaneously, a space conversion method is proposed to convert the stiffness index from the robot end to the tool end, and then a task-oriented flexibility ellipsoid on the tool-workpiece contact point is established. Furtherly, on these bases, aiming for the motion smoothness and the overall maximum stiffness of the robot in the whole grinding path, and taking the performance of the robot body as the constraint synergistically, an optimization model is established to optimize the posture of the robot. Finally, three groups of comparative grinding experiments are carried out on a KUKA kr210–2 robotic grinding platform. The results demonstrate that by using the posture optimization algorithm proposed in this paper, a better comprehensive performance including stiffness and motion smoothness in the whole grinding path can be achieved, and the workpiece after grinding has a higher removal depth and a better consistency, whose roughness has also been enhanced. These phenomenons indicate that the proposed method can significantly improve the accuracy and stability of grinding, thereby the effectiveness of this method is verified.

Industry 5.0 aims to prioritize human operators, focusing on their well-being and capabilities, while promoting collaboration between humans and robots to enhance efficiency and productivity. The integration of collaborative robots must ensure the health and well-being of human operators. Indeed, this paper addresses the need for a human-centered framework proposing a preference-based optimization algorithm in a human–robot collaboration (HRC) scenario with an ergonomics assessment to improve working conditions. The HRC application consists of optimizing a collaborative robot end-effector pose during an object-handling task. The following approach (AmPL-RULA) utilizes an Active multi-Preference Learning (AmPL) algorithm, a preference-based optimization method, where the user is requested to iteratively provide qualitative feedback by expressing pairwise preferences between a couple of candidates. To address physical well-being, an ergonomic performance index, Rapid Upper Limb Assessment (RULA), is combined with the user’s pairwise preferences, so that the optimal setting can be computed. Experimental tests have been conducted to validate the method, involving collaborative assembly during the object handling performed by the robot. Results illustrate that the proposed method can improve the physical workload of the operator while easing the collaborative task.

The authors provide a corrigendum for two equations of their published article [1]. These corrections do not impact the general kinetostatic model, elastic calibration algorithms, case studies’ results and conclusions presented in the article.

The digital twin is an emerging technology that enhances industrial digitalization, as it establishes a dynamic virtual model that emulates a specific phenomenon of the corresponding physical system, thus imparting added value in many manufacturing activities. Production scheduling is one of the manufacturing activities that can fulfill step-improvements from the digital twin. However, modest endeavors and discussions have been achieved in the application of the digital twin in production scheduling. To alleviate the scarcity of discussions on this topic, this paper provides a systematic review of the integration of the digital twin and dynamic production scheduling. First, this paper presents a summary of works related to digital twin-driven production scheduling. Subsequently, the paper investigates how to leverage the digital twin into production scheduling to improve its real-time capability, performance, and robustness within smart manufacturing systems such as sustainable manufacturing, zero-defect manufacturing, and human-centric manufacturing paradigms. Emphasis will then be placed on identifying research opportunities that need further investigation. Additionally, the paper discusses some manufacturing technologies that can be used in tandem to establish a shop floor digital twin encompassing both manufacturing assets and human resources. Finally, a conceptual digital twin framework is proposed to underpin future research.