Comparison of Inverse Kinematics Algorithms for Digital Twin Industry 4.0 Applications

This paper presents two Inverse Kinematics (IK) algorithms that are used for digital twin Augmented Reality (AR) applications. The first algorithm is a simple Inverse Kinematics (IK) Unity code that considers up to 9 points on the human body to model the motion. The second algorithm is the BIO IK that can consider up to 38 points. The performance of the algorithms is compared with data obtained by a Motion Capture (MoCap) measurement system. The metric of accuracy was used to quantify the performance evaluation and was modeled as the error of the position of the modeled joints of a human avatar with those measured by the MoCap system. It is observed that the obtained accuracy of the position increases with the number of points that is considered by the IK algorithm. For the purpose of this investigation, a MoCap system based on 13 cameras and 38 markers on the human body was used to measure the location of the joints of a human operator performing specific motions. The motion of lifting was the epicenter of the investigation that causes the larger amount of accidents in typical manufacturing facilities. The application of this research falls within the concepts of Digital Twin (DT) in Industry 4.0 scenarios.

[1]  Daria Battini,et al.  Innovative real-time system to integrate ergonomic evaluations into warehouse design and management , 2014, Comput. Ind. Eng..

[2]  Kristian Martinsen,et al.  Integration of digital learning in industry 4.0 , 2018 .

[3]  Zahra Sedighi Maman,et al.  A data-driven approach to modeling physical fatigue in the workplace using wearable sensors. , 2017, Applied ergonomics.

[4]  Damian Valles,et al.  Machine Learning Techniques for Motion Analysis of Fatigue from Manual Material Handling Operations Using 3D Motion Capture Data , 2020, 2020 10th Annual Computing and Communication Workshop and Conference (CCWC).

[5]  Sam Kash Kachigan Statistical Analysis: An Interdisciplinary Introduction to Univariate & Multivariate Methods , 1986 .

[6]  G Bright,et al.  Quasi-Serial Manipulator - Inverse Kinematics and Workspace Analysis for Industrial Automation , 2020, 2020 International SAUPEC/RobMech/PRASA Conference.

[7]  Franck Multon,et al.  Validation of an ergonomic assessment method using Kinect data in real workplace conditions. , 2017, Applied ergonomics.

[8]  Jianwei Zhang,et al.  An efficient hybridization of Genetic Algorithms and Particle Swarm Optimization for inverse kinematics , 2016, 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[9]  Fazel Naghdy,et al.  Human motion capture sensors and analysis in robotics , 2011, Ind. Robot.

[10]  S H Snook,et al.  The design of manual handling tasks: revised tables of maximum acceptable weights and forces. , 1991, Ergonomics.

[11]  He Zhang,et al.  Digital Twin in Industry: State-of-the-Art , 2019, IEEE Transactions on Industrial Informatics.

[12]  Beno Benhabib,et al.  A complete generalized solution to the inverse kinematics of robots , 1985, IEEE J. Robotics Autom..

[13]  Sebastian Starke,et al.  Memetic Evolution for Generic Full-Body Inverse Kinematics in Robotics and Animation , 2019, IEEE Transactions on Evolutionary Computation.

[14]  Sunwook Kim,et al.  An evaluation of classification algorithms for manual material handling tasks based on data obtained using wearable technologies , 2014, Ergonomics.

[15]  Gabriele Bleser,et al.  Innovative system for real-time ergonomic feedback in industrial manufacturing. , 2013, Applied ergonomics.