Development of an Optical Tracking Based Teleoperation System with Virtual Reality

Su, Yunpeng; Ahmadi, Mohammad; Bartneck, Christoph; Steinicke, Frank; Chen, Xiaoqi

doi:10.1109/iciea.2019.8833835

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…A similar approach has Fig. 1 Preliminary test with sensor placement on a robot TCP visualization with raw and filtered sensor data been presented in [30,33] in which human motion tracking is implemented for running in Unity3D and robot operating system (ROS). In [36], a hidden Markov model to compensate latency of human motion has been presented.…”

Section: Related Workmentioning

confidence: 99%

Human motion quality and accuracy measuring method for human–robot physical interactions

Tuli

Manns

Zeller

2022

Intel Serv Robotics

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In human–robot collaboration (HRC), human motion capture can be considered an enabler for switching autonomy between humans and robots to create efficient and safe operations. For this purpose, wearable motion tracking systems such as IMU and lighthouse-based systems have been used to transfer human joint motions into robot controller models. Due to reasons such as global positioning, drift, and occlusion, in some situations, e.g., HRC, both systems have been combined. However, it is still not clear if the motion quality (e.g., smoothness, naturalness, and spatial accuracy) is sufficient when the human operator is in the loop. This article presents a novel approach for measuring human motion quality and accuracy in HRC. The human motion capture has been implemented in a laboratory environment with a repetition of forty-cycle operations. Human motion, specifically of the wrist, is guided by the robot tool center point (TCP), which is predefined for generating circular and square motions. Compared to the robot TCP motion considered baseline, the hand wrist motion deviates up to 3 cm. The approach is valuable for understanding the quality of human motion behaviors and can be scaled up for various applications involving human and robot shared workplaces.

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Section: Related Workmentioning

confidence: 99%

Human motion quality and accuracy measuring method for human–robot physical interactions

Tuli

Manns

Zeller

2022

Intel Serv Robotics

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“…A second press returns the end-effector to the toolbox and the arm to the HOME position. In order to communicate information between the UI and the arm, a method to translate the imitation tool's potentiometers data and key functions was developed that uses Rosbridge Websocket to send /sensor msgs/joy message data types to the arm [8]. Note that ROS is run on Raspberry Pi 4 at both sides.…”

Section: User Interfacementioning

confidence: 99%

Multifunctional Arm for Telerobotic Wind Turbine Blade Repair

Sadeghian

Sareh

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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“…While traditional approaches are practical for industrial tasks, their third-dimensional responsiveness limitation may make them less intuitive for new users and harder to pilot in situations where users do not have a direct line of sight to the robot as in remote control [21] [22]. VR controllers allow users to direct the robot end-effector with hand gestures, a control method that is intuitive and natural to the user, enabling flawless human-robot interactions [23]. This strategy creates the impression that the robot is directly responsive to human hand movements.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of IoT and VR for Real-Time Teleoperation of Industrial Robots

Luu,

Nguyen,

Tran

et al. 2024

Preprint

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During the early stages of the Industry 4.0 era, teleoperation with human involvement in manufacturing processes and robot operation remained important. The existing teleoperation technologies have limitations in providing operator with spatial awareness and initiative control mechanism, making their applications difficult in tasks with high complexity and precision. This study implements the advancements from the Internet of Things (IoT) and Virtual Reality (VR) technology to improve the real-time teleoperation for industrial robots. The proposed design utilizes the benefits of the Message Queuing Telemetry Transport (MQTT) protocol in conjunction with a cloud broker, which has not been addressed significantly in previous studies for teleoperation. This setup enables the system to efficiently handle substantial amounts of data in short periods of time, allowing for an uninterrupted user experience while maintaining a consistent connection regardless of distance. Real-time robot control is accomplished by tracking the user's hand movements with a VR controller while providing the user with visual feedback from the VR environment. The incorporation of VR technology intends to improve the intuitiveness of controlling robot operations, provide more responsive feedback, and improve the quality of work performed by operators. Practical experiments are conducted with an industrial robot to observe the performance against the system under different internet conditions and different QoS setups. The evaluation criteria primarily concern latency and accuracy. The experiment aims to evaluate the effectiveness and scalability of the suggested system and to offer significant insight into its advantages and limitations relative to existing control system through systematic investigation.

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Development of an Optical Tracking Based Teleoperation System with Virtual Reality

Cited by 8 publications

References 8 publications

Human motion quality and accuracy measuring method for human–robot physical interactions

Human motion quality and accuracy measuring method for human–robot physical interactions

Multifunctional Arm for Telerobotic Wind Turbine Blade Repair

Effectiveness of IoT and VR for Real-Time Teleoperation of Industrial Robots

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