Human Motion Position Prediction for Human-Robot Collaboration in Manufacturing Considering Point Cloud Completion Of Human Joint

2021 ◽  
Author(s):  
Yang Hu ◽  
Zitong Liu ◽  
Feng Xu ◽  
Jiayi Liu ◽  
Wenjun Xu ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Intelligent Manufacturing ◽  
Human Motion ◽  
Accurate Estimation ◽  
Natural Interaction ◽  
Position Prediction ◽  
Traditional Manufacturing ◽  
Manufacturing Mode ◽  
Human Robot Collaboration ◽  
Human Joint

Abstract The research of human-robot collaboration for intelligent manufacturing is being paid gradually increasing attention due to high flexibility and high manufacturing efficiency. Comparing with the traditional manufacturing with low flexibility, human-robot collaboration in manufacturing system provides more personalized and flexible way to cover the shortages of traditional manufacturing mode. In human-robot collaboration system, human motion position prediction in the collaborative space is an essential prerequisite for ensuring the safety of workers. In this paper, 3D sensor Kinect is utilized to directly obtain human joint information. A partial circle delimitation method is used to solve the offset phenomenon of human joint obtained by Kinect, so as to achieve accurate estimation of human joint points. On this basis, an algorithm combing multilayer perceptron and long short-term memory network is explored to predict human motion position accurately. It not only helps to avoid complex feature extraction due to its end-to-end characteristic, but also provide natural interaction manner between human and robot without wearable devices or tags that may become a burden for the former. After that, the experimental results demonstrate that the proposed method makes predicting results accurate, and provides the reliable basis for human position prediction in the human-robot collaboration. This research could be applied to the human motion position prediction in human-robot collaboration process.

Knowledge Recommendation System for Human-Robot Collaborative Disassembly Using Knowledge Graph

2021 ◽  
Author(s):  
Yang Hu ◽  
Yiwen Ding ◽  
Feng Xu ◽  
Jiayi Liu ◽  
Wenjun Xu ◽  
...  
Keyword(s):  
Recommendation System ◽  
Industrial Robots ◽  
Knowledge Graph ◽  
Disassembly Process ◽  
Knowledge Based ◽  
Manufacturing Efficiency ◽  
Human Operators ◽  
Traditional Manufacturing ◽  
Human Robot Collaboration

Abstract In recent years, more and more attention has been paid to Human-Robot Collaborative Disassembly (HRCD) in the field of industrial remanufacturing. Compared with the traditional manufacturing, HRCD helps to improve the manufacturing flexibility with considering the manufacturing efficiency. In HRCD, knowledge could be obtained from the disassembly process and then provides useful information for the operator and robots to execute their disassembly tasks. Afterwards, a crucial point is to establish a knowledge-based system to facilitate the interaction between human operators and industrial robots. In this context, a knowledge recommendation system based on knowledge graph is proposed to effectively support Human-Robot Collaboration (HRC) in disassembly. A disassembly knowledge graph is constructed to organize and manage the knowledge in the process of HRCD. After that, based on this, a knowledge recommendation procedure is proposed to recommend disassembly knowledge for the operator. Finally, the case study demonstrates that the developed system can effectively acquire, manage and visualize the related knowledge of HRCD, and then assist the human operator to complete the disassembly task by knowledge recommendation, thus improving the efficiency of collaborative disassembly. This system could be used in the human-robot collaboration disassembly process for the operators to provide convenient knowledge recommendation service.

Conceptual Model and Operational Framework of Service-Oriented Manufacturing

2011 ◽  
Vol 179-180 ◽  
pp. 1177-1182 ◽  
Author(s):  
Xiao Qing Wu ◽  
Ming Shun Yang ◽  
Xin Qin Gao ◽  
Li Ba
Keyword(s):  
Conceptual Model ◽  
Manufacturing Industry ◽  
Producer Services ◽  
Operating Characteristics ◽  
Product Service System ◽  
Manufacturing Enterprises ◽  
Product Service ◽  
Service Oriented ◽  
Traditional Manufacturing ◽  
Manufacturing Mode

With the fiercer competition and more complex environment of manufacturing industry, the service-oriented manufacturing mode integrating manufacture and service has become an inevitable trend. Together with the producer services and product-service system, the development process of service-oriented manufacturing mode was summarized. Compared with the traditional manufacturing mode, a conceptual model of service-oriented manufacturing was proposed. Furthermore, the operational framework of service-oriented manufacturing mode was established based on its operating characteristics. The research paper could provide the manufacturing enterprises with some foundation for implementing the service-oriented manufacturing mode.

scholarly journals A deep learning hybrid predictive modeling (HPM) approach for estimating evapotranspiration and ecosystem respiration

2021 ◽  
Vol 25 (11) ◽  
pp. 6041-6066
Author(s):  
Jiancong Chen ◽  
Baptiste Dafflon ◽  
Anh Phuong Tran ◽  
Nicola Falco ◽  
Susan S. Hubbard
Keyword(s):  
Deep Learning ◽  
Predictive Modeling ◽  
Vegetation Index ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Model Simulation ◽  
Ecosystem Respiration ◽  
Ecosystem Dynamics ◽  
Accurate Estimation ◽  
East River

Abstract. Climate change is reshaping vulnerable ecosystems, leading to uncertain effects on ecosystem dynamics, including evapotranspiration (ET) and ecosystem respiration (Reco). However, accurate estimation of ET and Reco still remains challenging at sparsely monitored watersheds, where data and field instrumentation are limited. In this study, we developed a hybrid predictive modeling approach (HPM) that integrates eddy covariance measurements, physically based model simulation results, meteorological forcings, and remote-sensing datasets to estimate ET and Reco in high space–time resolution. HPM relies on a deep learning algorithm and long short-term memory (LSTM) and requires only air temperature, precipitation, radiation, normalized difference vegetation index (NDVI), and soil temperature (when available) as input variables. We tested and validated HPM estimation results in different climate regions and developed four use cases to demonstrate the applicability and variability of HPM at various FLUXNET sites and Rocky Mountain SNOTEL sites in Western North America. To test the limitations and performance of the HPM approach in mountainous watersheds, an expanded use case focused on the East River Watershed, Colorado, USA. The results indicate HPM is capable of identifying complicated interactions among meteorological forcings, ET, and Reco variables, as well as providing reliable estimation of ET and Reco across relevant spatiotemporal scales, even in challenging mountainous systems. The study documents that HPM increases our capability to estimate ET and Reco and enhances process understanding at sparsely monitored watersheds.

Augmented Technology for Safety and Maintenance in Industry 4.0

2021 ◽  
pp. 495-502
Author(s):  
Vikas Kukshal ◽  
Amar Patnaik ◽  
Sarbjeet Singh
Keyword(s):  
Industry 4.0 ◽  
Manufacturing System ◽  
Industrial Revolution ◽  
Intelligent Manufacturing ◽  
Skilled Workers ◽  
New Era ◽  
Intelligent Manufacturing System ◽  
Traditional Manufacturing ◽  
Area Of Concern ◽  
Rapid Transformation

The traditional manufacturing system is going through a rapid transformation and has brought a revolution in the industries. Industry 4.0 is considered to be a new era of the industrial revolution in which all the processes are integrated with a product to achieve higher efficiency. Digitization and automation have changed the nature of work resulting in an intelligent manufacturing system. The benefits of Industry 4.0 include higher productivity and increased flexibility. However, the implementation of the new processes and methods comes along with a lot of challenges. Industry 4.0. requires more skilled workers to handle the operations of the digitalized manufacturing system. The fourth industrial revolution or Industry 4.0 has become the absolute reality and will undoubtedly have an impact on safety and maintenance. Hence, to tackle the issues arising due to digitization is an area of concern and has to be dealt with using the innovative technologies in the manufacturing industries.

scholarly journals Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1801 ◽  
Author(s):  
Haitao Guo ◽  
Yunsick Sung
Keyword(s):  
Motion Capture ◽  
Short Term Memory ◽  
Arm Movements ◽  
Ground Truth ◽  
Human Movement ◽  
Human Motion ◽  
Soft Sensors ◽  
Upper Arm ◽  
Human Motion Capture ◽  
Gesture Control

The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM.

scholarly journals Understanding human motion and gestures for underwater human-robot collaboration

2018 ◽  
Vol 36 (5) ◽  
pp. 851-873 ◽  
Author(s):  
Md Jahidul Islam ◽  
Marc Ho ◽  
Junaed Sattar
Keyword(s):  
Human Motion ◽  
Human Robot Collaboration

scholarly journals An Augmented Discrete-Time Approach for Human-Robot Collaboration

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Peidong Liang ◽  
Lianzheng Ge ◽  
Yihuan Liu ◽  
Lijun Zhao ◽  
Ruifeng Li ◽  
...  
Keyword(s):  
Discrete Time ◽  
Human Motion ◽  
Stiffness Index ◽  
Robot Dynamics ◽  
Robot Arm ◽  
Human Arm ◽  
Human Kinematics ◽  
Human Motor ◽  
New Generation ◽  
Human Robot Collaboration

Human-robot collaboration (HRC) is a key feature to distinguish the new generation of robots from conventional robots. Relevant HRC topics have been extensively investigated recently in academic institutes and companies to improve human and robot interactive performance. Generally, human motor control regulates human motion adaptively to the external environment with safety, compliance, stability, and efficiency. Inspired by this, we propose an augmented approach to make a robot understand human motion behaviors based on human kinematics and human postural impedance adaptation. Human kinematics is identified by geometry kinematics approach to map human arm configuration as well as stiffness index controlled by hand gesture to anthropomorphic arm. While human arm postural stiffness is estimated and calibrated within robot empirical stability region, human motion is captured by employing a geometry vector approach based on Kinect. A biomimetic controller in discrete-time is employed to make Baxter robot arm imitate human arm behaviors based on Baxter robot dynamics. An object moving task is implemented to validate the performance of proposed methods based on Baxter robot simulator. Results show that the proposed approach to HRC is intuitive, stable, efficient, and compliant, which may have various applications in human-robot collaboration scenarios.

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