Real-Time Motion Tracking for Humans and Robots in a Collaborative Assembly Task

Author:

Tuli Tadele BelayORCID,Manns Martin

Abstract

Human-robot collaboration combines the extended capabilities of humans and robots to create a more inclusive and human-centered production system in the future. However, human safety is the primary concern for manufacturing industries. Therefore, real-time motion tracking is necessary to identify if the human worker body parts enter the restricted working space solely dedicated to the robot. Tracking these motions using decentralized and different tracking systems requires a generic model controller and consistent motion exchanging formats. In this work, our task is to investigate a concept for a unified real-time motion tracking for human-robot collaboration. In this regard, a low cost and game-based motion tracking system, e.g., HTC Vive, is utilized to capture human motion by mapping into a digital human model in the Unity3D environment. In this context, the human model is described using a biomechanical model that comprises joint segments defined by position and orientation. Concerning robot motion tracking, a unified robot description format is used to describe the kinematic trees. Finally, a concept of assembly operation that involves snap joining is simulated to analyze the performance of the system in real-time capability. The distribution of joint variables in spatial-space and time-space is analyzed. The results suggest that real-time tracking in human-robot collaborative assembly environments can be considered to maximize the safety of the human worker. However, the accuracy and reliability of the system regarding system disturbances need to be justified.

Publisher

MDPI AG

Subject

General Medicine

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2. Human motion quality and accuracy measuring method for human–robot physical interactions;Intelligent Service Robotics;2022-07-02

3. Comparison of AI-based Task Planning Approaches for Simulating Human-Robot Collaboration;Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems;2021-11-01

4. Knowledge-Based Digital Twin for Predicting Interactions in Human-Robot Collaboration;2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA );2021-09-07

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