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An integral part of information-centric smart manufacturing is the adaptation of industrial robots to complement human workers in a collaborative manner. While advancement in sensing has enabled real-time monitoring of workspace, understanding the semantic information in the workspace, such as parts and tools, remains a challenge for seamless robot integration. The resulting lack of adaptivity to perform in a dynamic workspace have limited robots to tasks with pre-defined actions. In this paper, a machine learning-based robotic object detection and grasping method is developed to improve the adaptivity of robots. Specifically, object detection based on the concept of single-shot detection (SSD) and convolutional neural network (CNN) is investigated to recognize and localize objects in the workspace. Subsequently, the extracted information from object detection, such as the type, position, and orientation of the object, is fed into a multi-layer perceptron (MLP) to generate the desired joint angles of robotic arm for proper object grasping and handover to the human worker. Network training is guided by forward kinematics of the robotic arm in a self-supervised manner to mitigate issues such as singularity in computation. The effectiveness of the developed method is validated on an eDo robotic arm in a human-robot collaborative assembly casemore »study.« less

Abstract Today’s manufacturing systems are becoming increasingly complex, dynamic, and connected. The factory operations face challenges of highly nonlinear and stochastic activity due to the countless uncertainties and interdependencies that exist. Recent developments in artificial intelligence (AI), especially Machine Learning (ML) have shown great potential to transform the manufacturing domain through advanced analytics tools for processing the vast amounts of manufacturing data generated, known as Big Data. The focus of this paper is threefold: (1) review the state-of-the-art applications of AI to representative manufacturing problems, (2) provide a systematic view for analyzing data and process dependencies at multiple levels that AI must comprehend, and (3) identify challenges and opportunities to not only further leverage AI for manufacturing, but also influence the future development of AI to better meet the needs of manufacturing. To satisfy these objectives, the paper adopts the hierarchical organization widely practiced in manufacturing plants in examining the interdependencies from the overall system level to the more detailed granular level of incoming material process streams. In doing so, the paper considers a wide range of topics from throughput and quality, supervisory control in human–robotic collaboration, process monitoring, diagnosis, and prognosis, finally to advances in materials engineering to achievemore »desired material property in process modeling and control.« less

Human-Robot Collaboration (HRC), which enables a workspace where human and robot can dynamically and safely collaborate for improved operational efficiency, has been identified as a key element in smart manu­facturing. Human action recognition plays a key role in the realization ofHRC, as it helps identify current human action and provides the basis for future action prediction and robot planning. While Deep Learning (DL) has demonstrated great potential in advancing human action recognition, effectively leveraging the temporal in­formation of human motions to improve the accuracy and robustness of action recognition has remained as a challenge. Furthermore, it is often difficult to obtain a large volume of data for DL network training and opti­mization, due to operational constraints in a realistic manufacturing setting. This paper presents an integrated method to address these two challenges, based on the optical flow and convolutional neural network (CNN)­based transfer learning. Specifically, optical flow images, which encode the temporal information of human motion, are extracted and serve as the input to a two-stream CNN structure for simultaneous parsing of spatial­-temporal information of human motion. Subsequently, transfer learning is investigated to transfer the feature extraction capability of a pre-trained CNN to manufacturing scenarios. Evaluation using engine block assemblymore »confirmed the effectiveness of the developed method.« less

Human-Robot Collaboration (HRC), which envisions a workspace in which human and robot can dynamically collaborate, has been identified as a key element in smart manufacturing. Human action recognition plays a key role in the realization of HRC as it helps identify current human action and provides the basis for future action prediction and robot planning. Despite recent development of Deep Learning (DL) that has demonstrated great potential in advancing human action recognition, one of the key issues remains as how to effectively leverage the temporal information of human motion to improve the performance of action recognition. Furthermore, large volume of training data is often difficult to obtain due to manufacturing constraints, which poses challenge for the optimization of DL models. This paper presents an integrated method based on optical flow and convolutional neural network (CNN)-based transfer learning to tackle these two issues. First, optical flow images, which encode the temporal information of human motion, are extracted and serve as the input to a two-stream CNN structure for simultaneous parsing of spatial-temporal information of human motion. Then, transfer learning is investigated to transfer the feature extraction capability of a pretrained CNN to manufacturing scenarios. Evaluation using engine block assembly confirmed themore »effectiveness of the developed method.« less