Da Vinci tool torque mapping over 50,000 grasps and its implications on grip force estimation accuracy

Kong, Nathan J.; Stephens, Trevor K.; Kowalewski, Timothy M.

doi:10.1109/ismr.2018.8333292

Cited by 12 publications

(6 citation statements)

References 10 publications

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“…Here, the torque sensors were essential for determining slip and adjusting the grasp [ 52 ]. The ability to measure torques at the object–gripper interface enables precise grip force adjustments and opens up new uses of tactile sensors, such as being used for improving the grasping abilities, for example, in surgical tools [ 53 , 54 ] and precise positioning and applying rotation forces in assembly tasks [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

Design, Fabrication, and Characterization of a Novel Optical Six-Axis Distributed Force and Displacement Tactile Sensor for Dexterous Robotic Manipulation

Leslie,

Córdova Bulens,

Redmond

2023

Sensors

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Real-time multi-axis distributed tactile sensing is a critical capability if robots are to perform stable gripping and dexterous manipulation, as it provides crucial information about the sensor–object interface. In this paper, we present an optical-based six-axis tactile sensor designed in a fingertip shape for robotic dexterous manipulation. The distributed sensor can precisely estimate the local XYZ force and displacement at ten distinct locations and provide the global XYZ force and torque measurements. Its compact size, comparable to that of a human thumb, and minimal thickness allow seamless integration onto existing robotic fingers, eliminating the need for complex modifications to the gripper. The proposed sensor design uses a simple, low-cost fabrication method. Moreover, the optical transduction approach uses light angle and intensity sensing to infer force and displacement from deformations of the individual sensing units that form the overall sensor, providing distributed six-axis sensing. The local force precision at each sensing unit in the X, Y, and Z axes is 20.89 mN, 19.19 mN, and 43.22 mN, respectively, over a local force range of approximately ±1.5 N in X and Y and 0 to −2 N in Z. The local displacement precision in the X, Y, and Z axes is 56.70 μm, 50.18 μm, and 13.83 μm, respectively, over a local displacement range of ±2 mm in the XY directions and 0 to −1.5 mm in Z (i.e., compression). Additionally, the sensor can measure global torques, Tx, Ty, and Tz, with a precision of of 1.90 N-mm, 1.54 N-mm, and 1.26 N-mm, respectively. The fabricated design is showcased by integrating it with an OnRobot RG2 gripper and illustrating real-time measurements during in simple demonstration task, which generated changing global forces and torques.

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Section: Discussionmentioning

confidence: 99%

Design, Fabrication, and Characterization of a Novel Optical Six-Axis Distributed Force and Displacement Tactile Sensor for Dexterous Robotic Manipulation

Leslie,

Córdova Bulens,

Redmond

2023

Sensors

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“…The training is based on a set of measurements at the beginning of the surgery that is used afterward for force estimation throughout the entire surgery. Proposed approaches that have an instrument's operational parameters as inputs, do not consider the variations between instruments and the change of instruments behavior throughout its use (Kong et al, 2018). Considering how the research direction has evolved over the past decade, experimentation with different model architectures, development of efficient training, and identification methods that can be automatically performed at the system start-up (Spiers et al, 2015), improving the computation time and incorporation of online adaptation techniques are attractive research areas to be further investigated.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, any model relies on a set of measurements (calibration or training set) that are usually taken at the beginning and used throughout the estimation. It is experimentally shown that the tool behavior changes with time which deteriorates the estimation accuracy (Hadi Hosseinabadi et al, 2019;Kong et al, 2018). The environmental parameters such as temperature and humidity can also affect the instrument characteristics The severity level of the sources that contribute to the measurement inaccuracy (scale of 1 to 3; 1 is minimum, 3 is maximum, and × is no effect), and the importance of design requirements (scale of 1 to 3; 1 is the least, 3 is the most, and × refers to not a requirement) are compared.…”

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confidence: 99%

Force sensing in robot-assisted keyhole endoscopy: A systematic survey

Hosseinabadi

Salcudean

2021

The International Journal of Robotics Research

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Instrument–tissue interaction forces in minimally invasive surgery (MIS) provide valuable information that can be used to provide haptic perception, monitor tissue trauma, develop training guidelines, and evaluate the skill level of novice and expert surgeons. Force and tactile sensing is lost in many robot-assisted surgery (RAS) systems. Therefore, many researchers have focused on recovering this information through sensing systems and estimation algorithms. This article provides a comprehensive systematic review of the current force sensing research aimed at RAS and, more generally, keyhole endoscopy, in which instruments enter the body through small incisions. Articles published between January 2011 and May 2020 are considered, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The literature search resulted in 110 papers on different force estimation algorithms and sensing technologies, sensor design specifications, and fabrication techniques.

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“…Position and torque data were time-synchronously collected on both ends. Details of the hardware setup including the sensors used on the proximal and distal ends are explained in [4] and [5], respectively. For this dataset, the tested range of orientations included the following: pitch angles (-60 to +60°), roll angles (-90 to +90°) and yaw angles (-90 to +30°).…”

Section: Methodsmentioning

confidence: 99%

“…The estimation technique utilized in this work is an artificial neural network similar to the one explained in [4]. The neural network architecture consisted of 60 nodes in a single hidden layer, with input features of position, velocity, torque, pitch spindle, and roll spindle all measured on the proximal end.…”

Section: Methodsmentioning

confidence: 99%

Impact of Jaw Orientation on Grip Force Estimation for a da Vinci EndoWrist Surgical Tool

Stephens¹,

O’Neill²,

Kong³

et al. 2018

The Hamlyn Symposium on Medical Robotics

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Da Vinci tool torque mapping over 50,000 grasps and its implications on grip force estimation accuracy

Cited by 12 publications

References 10 publications

Design, Fabrication, and Characterization of a Novel Optical Six-Axis Distributed Force and Displacement Tactile Sensor for Dexterous Robotic Manipulation

Design, Fabrication, and Characterization of a Novel Optical Six-Axis Distributed Force and Displacement Tactile Sensor for Dexterous Robotic Manipulation

Force sensing in robot-assisted keyhole endoscopy: A systematic survey

Impact of Jaw Orientation on Grip Force Estimation for a da Vinci EndoWrist Surgical Tool

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