A Tele-Operated Display With a Predictive Display Algorithm

Jung, Yeongtae; Han, Kyu-Taek; Bae, Joonbum

doi:10.1109/access.2019.2948879

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…The proposed system, as will be discussed in Section 5.3, attains a high level of usability and is easy to be trained. In comparison with previous works which adopted purely direct control approaches, such as Orosco et al (2021), Madder et al (2020) and Jung et al (2019), we find that these approaches still require highly trained operators and cannot present solutions to avoid high concentration levels and cognitive stresses. It requires the operator’s undivided attention throughout the task working time which increases stress, especially when working in unstructured environments.…”

Section: Related Workmentioning

confidence: 59%

“…They found that, in comparison to previous approaches that included sparse multivariate linear regression, remote system control is improved when the time delay is known ahead of time. Jung et al (2019) used a kinematic model of the human head and neck, as well as camera parameters, to create a predictive display. The authors used a simple linear model of motion to predict future images based on current images using the relationship between camera orientation and the positional changes of the human controllers.…”

Section: Related Workmentioning

confidence: 99%

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Augmented reality-assisted gesture-based teleoperated system for robot motion planning

Salman

Roman

2023

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Purpose The study proposed a human–robot interaction (HRI) framework to enable operators to communicate remotely with robots in a simple and intuitive way. The study focused on the situation when operators with no programming skills have to accomplish teleoperated tasks dealing with randomly localized different-sized objects in an unstructured environment. The purpose of this study is to reduce stress on operators, increase accuracy and reduce the time of task accomplishment. The special application of the proposed system is in the radioactive isotope production factories. The following approach combined the reactivity of the operator’s direct control with the powerful tools of vision-based object classification and localization. Design/methodology/approach Perceptive real-time gesture control predicated on a Kinect sensor is formulated by information fusion between human intuitiveness and an augmented reality-based vision algorithm. Objects are localized using a developed feature-based vision algorithm, where the homography is estimated and Perspective-n-Point problem is solved. The 3D object position and orientation are stored in the robot end-effector memory for the last mission adjusting and waiting for a gesture control signal to autonomously pick/place an object. Object classification process is done using a one-shot Siamese neural network (NN) to train a proposed deep NN; other well-known models are also used in a comparison. The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved. Findings The system was contextualized in one of the nuclear industry applications: radioactive isotope production and its validation were performed through a user study where 10 participants of different backgrounds are involved. The results revealed the effectiveness of the proposed teleoperation system and demonstrate its potential for use by robotics non-experienced users to effectively accomplish remote robot tasks. Social implications The proposed system reduces risk and increases level of safety when applied in hazardous environment such as the nuclear one. Originality/value The contribution and uniqueness of the presented study are represented in the development of a well-integrated HRI system that can tackle the four aforementioned circumstances in an effective and user-friendly way. High operator–robot reactivity is kept by using the direct control method, while a lot of cognitive stress is removed using elective/flapped autonomous mode to manipulate randomly localized different configuration objects. This necessitates building an effective deep learning algorithm (in comparison to well-known methods) to recognize objects in different conditions: illumination levels, shadows and different postures.

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Section: Related Workmentioning

confidence: 59%

Section: Related Workmentioning

confidence: 99%

Augmented reality-assisted gesture-based teleoperated system for robot motion planning

Salman

Roman

2023

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show abstract

“…They use a first-order gaze movement model with a time delay to predict specific target points and use these as points of movement during the time delay. Jung et al ( 2019 ) utilize a predictive display using a kinematic model of the human head and neck along with camera parameters. This is an interesting approach where they use the relationship between camera orientation and the human controllers' positional changes to predict future images based on current images using a simple linear model of motion.…”

Section: Time Series Prediction Methods For Mitigating Time Delay mentioning

confidence: 99%

A Brief Survey of Telerobotic Time Delay Mitigation

2020

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There is a substantial number of telerobotics and teleoperation applications ranging from space operations, ground/aerial robotics, drive-by-wire systems to medical interventions. Major obstacles for such applications include latency, channel corruptions, and bandwidth which limit teleoperation efficacy. This survey reviews the time delay problem in teleoperation systems. We briefly review different solutions from early approaches which consist of control-theory-based models and user interface designs and focus on newer approaches developed since 2014. Future solutions to the time delay problem will likely be hybrid solutions which include modeling of user intent, prediction of robot movements, and time delay prediction all potentially using time series prediction methods. Hence, we examine methods that are primarily based on time series prediction. Recent prediction approaches take advantage of advances in nonlinear statistical models as well as machine learning and neural network techniques. We review Recurrent Neural Networks, Long Short-Term Memory, Sequence to Sequence, and Generative Adversarial Network models and examine each of these approaches for addressing time delay. As time delay is still an unsolved problem, we suggest some possible future research directions from information-theory-based modeling, which may lead to promising new approaches to advancing the field.

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Predicted Trajectory Guidance Control Framework of Teleoperated Ground Vehicles Compensating for Delays

Zhang

Wang

et al. 2023

IEEE Trans. Veh. Technol.

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A Tele-Operated Display With a Predictive Display Algorithm

Cited by 4 publications

References 27 publications

Augmented reality-assisted gesture-based teleoperated system for robot motion planning

Augmented reality-assisted gesture-based teleoperated system for robot motion planning

A Brief Survey of Telerobotic Time Delay Mitigation

Predicted Trajectory Guidance Control Framework of Teleoperated Ground Vehicles Compensating for Delays

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