Haptic Virtual Fixtures for Robot-Assisted Manipulation

Abbott, Jake J.; Marayong, Panadda; Okamura, Allison M.

doi:10.1007/978-3-540-48113-3_5

Cited by 237 publications

(161 citation statements)

References 33 publications

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“…Assisted teleoperation frameworks, in which the human inputs are 'mediated' by the (partial) autonomy of the controlled robotic systems, have thus been proposed for reducing the workload requirements of the human operator and improving her/his performance [2]- [4]. Examples in this sense range from virtual fixtures [5], [6], which are taskdependent and require continuous human input, to more complicated shared control schemes aiming at profiting from the human's supervisory capabilities in guiding an autonomous system [4], [7].…”

Section: Introductionmentioning

confidence: 99%

A learning-based shared control architecture for interactive task execution

Abi-Farraj

Osa

Peters

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Shared control is a key technology for various robotic applications in which a robotic system and a human operator are meant to collaborate efficiently. In order to achieve efficient task execution in shared control, it is essential to predict the desired behavior for a given situation or context to simplify the control task for the human operator. To do this prediction, we use Learning from Demonstration (LfD), which is a popular approach for transferring human skills to robots. We encode the demonstrated behavior as trajectory distributions and generalize the learned distributions to new situations. The goal of this paper is to present a shared control framework that uses learned expert distributions to gain more autonomy. Our approach controls the balance between the controller's autonomy and the human preference based on the distributions of the demonstrated trajectories. Moreover, the learned distributions are autonomously refined from collaborative task executions, resulting in a master-slave system with increasing autonomy that requires less user input with an increasing number of task executions. We experimentally validated that our shared control approach enables efficient task executions. Moreover, the conducted experiments demonstrated that the developed system improves its performances through interactive task executions with our shared control.

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Section: Introductionmentioning

confidence: 99%

A learning-based shared control architecture for interactive task execution

Abi-Farraj

Osa

Peters

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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“…Within this context, the pursuit of enhanced human-in-the-loop guidance in manual or robot-assisted MIP, has led to the introduction of the concept of forbidden-region virtual fixtures (FRVF) and forbidden region guided fixtures [5,6]. The benefits of FRVF have been demonstrated for various applications, including orthopedic procedures [7], needle insertion [8], medical diagnosis [9,10], and image analysis [11].…”

Section: Introductionmentioning

confidence: 99%

Implementation of a force-feedback interface for robotic assisted interventions with real-time MRI guidance

Sternberg

Kilicarslan

Navkar

et al. 2013

2013 IEEE International Conference on Robotics and Automation

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Abstract-Efficient and intuitive interfacing of the interventionalist to the information and tools available from image-guided robotic assisted surgeries is required to achieve the full benefit of these technologies. Ongoing research has been performed into the use of forbidden region guided fixtures (FRVF) for human-in-the-loop control of image-guided procedures via haptic force-feedback devices (FFD). Although commercially available FFD provide sufficient degrees-offreedom (DoF), collaborating clinicians, as well as the results of our previous work indicate that these systems are not completely intuitive for controlling fixed-point access interventional tool which have a remote center of motion. Within this context, we introduce a new FFD which is designed with the same DoF constraints as a fixed-point access interventional tool. The device is tested in a clinical simulation of a robot assisted trans-apical valve implantation under guidance from real-time magnetic resonance imaging. Preacquired real-time images are used in the clinical simulation to dynamically update the FRVF and therefore provide guiding forces to allow the operator to see the safe boundaries of operation via a visualization interface and physically feel them through the FFD. Inertial and gravity compensation and per DoF dynamic response of the physical prototype are validated and the frequency response of the system demonstrates it is adequate for tactile sensing. During clinical simulation the operator was successfully able to maneuver the tool within the safe path to the region of interest with the guidance of visual and force-feedback.

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“…Any augmentation of the force feedback to the user in order to improve his performance during the execution of a task, e.g. by incorporating virtual fixtures as described by Abbott, Marayong and Okamura (2007), needs to be separately implemented with additional measures to ensure stability . The PSPM approach can accommodate these features by means of the intermediate data processing capabilities, but requires an additional hardware-controller settings condition to be satisfied.…”

Section: Proposed Two-layer Frameworkmentioning

confidence: 99%

“…proximity or navigation cues. Virtual fixtures as described by Abbott, Marayong and Okamura (2007) and Marayong et al (2002) are also a type of SCS.…”

Section: Haptic Feedback Systems In Medical Applicationsmentioning

confidence: 99%

“…Intended applications are in training, e.g. surgical simulators (Basdogan et al, 2001), virtual fixtures in telemanipulation (Abbott, Marayong and Okamura, 2007), and various fabrication processes (Radi et al, 2010). In this paper we will focus on kinesthetic (force feedback) and in the remainder of the paper the term 'haptic' refers only to kinesthetic information.…”

Section: Introductionmentioning

confidence: 99%

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Control of haptic interaction : an energy-based approach

Franken¹

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Haptic Virtual Fixtures for Robot-Assisted Manipulation

Cited by 237 publications

References 33 publications

A learning-based shared control architecture for interactive task execution

A learning-based shared control architecture for interactive task execution

Implementation of a force-feedback interface for robotic assisted interventions with real-time MRI guidance

Control of haptic interaction : an energy-based approach

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