The Effects of Simulated Inertia and Force Prediction on Delayed Telepresence

Clarke, Stella; Schillhuber, G.; Zaeh, Michael F.; Ulbrich, H.

doi:10.1162/pres.16.5.543

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…Using the prediction-based particle filter method, the predicted force is calculated by approximating the posterior density function by using (5)- (8).…”

Section: Force Predictionmentioning

confidence: 99%

“…In the area of Internet transport protocols, several proposals have been suggested based on modifications to transport control protocol (TCP) and user datagram protocol (UDP) to enable faster transmissions of data packets [5], [6]. In the area of signal processing, prediction-based methods that perform motion and force predictions have been proposed [7], [8]. The Kalman filter method, which provides a recursive solution to the linear prediction and estimation, was proposed as a prediction-based approach [9].…”

Section: Introductionmentioning

confidence: 99%

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The Internet-Based Teleoperation: Motion and Force Predictions Using the Particle Filter Method

Lee

Payandeh

Trajković

2010

Volume 8: Dynamic Systems and Control, Parts a and B

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In this paper, we present motion and force predictions in Internet-based teleoperation systems using the particle filter method. The particle filter, also known as the sequential Monte Carlo (SMC) method, is a probabilistic prediction or estimation technique within a sequential Bayesian framework: Data at a current time step are predicted or estimated by recursively generating probability distribution based on previous observations and input states. In this paper, we first formulate the particle filter method using a prediction-based approach. Motion and force data flows, which may be impaired by the Internet delay, are formulated within a sequential Bayesian framework. The true motion and force data are then predicted by employing the prediction-based particle filter method using the impaired observations and previous input states. We performed experiments using a haptic device that interacts with a mechanics-based virtual 3D graphical environment. The haptic device is used as a master controller that provides positioning inputs to a 4-degree of freedom (4-DoF) virtual robotic manipulator while receiving feedback force through interactions with the virtual environment. We simulate the Internet delay with variations typically observed in a user datagram protocol (UDP) transmission between the master controller and the virtual teleoperated robot. In this experimental scenario, the particle filter method is implemented for both motion and force data that experience the Internet delay. The proposed method is compared with the conventional Kalman filter. Experimental results indicate that in nonlinear and non-Gaussian environments the prediction-based particle filter has distinct advantage over other methods. INTRODUCTIONAn Internet-based teleoperation system is an interactive application where though a master device, a human operator transmits motion data while simultaneously receiving reflecting force data from a slave robot controller. Unlike other Internet applications that mainly focus on the reliable data transmission, interactive applications are highly delay-sensitive. The Internet delay, which is unknown and varies over time according to network conditions, may cause instability of an overall teleoperation system. Furthermore, the transmitted motion and force data are often impaired by significant delay and delay jitter during the Internet transmission [1].Various approaches have been suggested in order to solve the time delay issue of Internet-based teleoperation systems. In the area of control systems, the wave variables transformation and its extensions have focused on the stability of overall teleoperation systems in the presence of constant delay [2], [3],

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“…Using the prediction-based particle filter method, the predicted force is calculated by approximating the posterior density function by using (5)- (8).…”

Section: Force Predictionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Internet-Based Teleoperation: Motion and Force Predictions Using the Particle Filter Method

Lee

Payandeh

Trajković

2010

Volume 8: Dynamic Systems and Control, Parts a and B

View full text Add to dashboard Cite

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“…In previous studies in the fields of virtual reality [10] and telepresence [17], the use of double exponential smoothing (DESP) as a prediction algorithm has been shown to produce optimal results in terms of high prediction accuracy and fast learning time. The next section is dedicated to explaining DESP.…”

Section: Movement Predictionmentioning

confidence: 99%

“…Also, execution of the algorithm should be fast, since any time taken to generate the prediction is essentially time added to the delay. In [17], it was shown that for data extracted from the PHANToM Desktop haptic device [18], DESP was the optimal algorithm when compared against four other common prediction algorithms.…”

Section: Double Exponential Smoothingmentioning

confidence: 99%

Prediction-based methods for teleoperation across delayed networks

et al. 2007

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The remote nature of telepresence scenarios can be seen as a strongpoint and also as a weakness. Although it enables the remote control of robots in dangerous or inaccessible environments, it necessarily involves some kind of communication mechanism for the transmission of control signals. This communication mechanism necessarily involves adverse network effects such as delay. Three mechanisms aimed at improving the effects of network delay are presented in this paper: (1) Motion prediction to partially compensate for network delays, (2) force prediction to learn a local force model, thereby reducing dependency on delayed force signals, and (3) haptic data compression to reduce the required bandwidth of high frequency data. The utilized motion prediction scheme was shown to improve operator performance, but had no influence on operator immersion. The force prediction provided haptic feedback through synchronous forces from the local model, thereby stabilizing the control loop. The developed haptic data compression scheme reduced the number of packets sent across the network by 90%, while improving the quality of the haptic feedback.

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“…Therefore, we interest in enabling a stable and realistic perception rendering to the operators when data communication is imperfect or delayed. The synchronization between hand movements of the operator and the force perception is then lost if a large time-delay is present [6]. In terms of control theory, this matter can be solved using a state-estimator in order to predict the behavior of the process with less actual feedback signals from physical sensors [28].…”

Section: Introductionmentioning

confidence: 99%

Remote Minimally Invasive Surgery – Haptic Feedback and Selective Automation in Medical Robotics

Staub

Ono

Mayer

et al. 2011

Applied Bionics and Biomechanics

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The automation of recurrent tasks and force feedback are complex problems in medical robotics. We present a novel approach that extends human-machine skill-transfer by a scaffolding framework. It assumes a consolidated working environment for both, the trainee and the trainer. The trainer provides hints and cues in a basic structure which is already understood by the learner. In this work, the scaffolding is constituted by abstract patterns, which facilitate the structuring and segmentation of information during “Learning by Demonstration” (LbD). With this concept, the concrete example of knot-tying for suturing is exemplified and evaluated. During the evaluation, most problems and failures arose due to intrinsic system imprecisions of the medical robot system. These inaccuracies were then improved by the visual guidance of the surgical instruments. While the benefits of force feedback in telesurgery has already been demonstrated and measured forces are also used during task learning, the transmission of signals between the operator console and the robot system over long-distances or across-network remote connections is still a challenge due to time-delay. Especially during incision processes with a scalpel into tissue, a delayed force feedback yields to an unpredictable force perception at the operator-side and can harm the tissue which the robot is interacting with. We propose a XFEM-based incision force prediction algorithm that simulates the incision contact-forces in real-time and compensates the delayed force sensor readings. A realistic 4-arm system for minimally invasive robotic heart surgery is used as a platform for the research.

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The Effects of Simulated Inertia and Force Prediction on Delayed Telepresence

Cited by 6 publications

References 21 publications

The Internet-Based Teleoperation: Motion and Force Predictions Using the Particle Filter Method

The Internet-Based Teleoperation: Motion and Force Predictions Using the Particle Filter Method

Prediction-based methods for teleoperation across delayed networks

Remote Minimally Invasive Surgery – Haptic Feedback and Selective Automation in Medical Robotics

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