An estimation method of grasping force for laparoscope surgical robot based on the model of a cable-pulley system

Xue, Renfeng; Du, Zhijiang; Yan, Zhiyuan; Ren, Bo

doi:10.1016/j.mechmachtheory.2018.12.032

Cited by 21 publications

(10 citation statements)

References 32 publications

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“…Sensors placed at the gripper jaw provide the most accurate readings and have the most stringent design constraints. They are difficult to fabricate, package, mount (He et al, 2014;Kim et al, 2018b), and shield (Seok et al, 2019;Wang et al, 2014) and have limited adaptability which makes them cost-prohibitive for disposable instruments (Xue et al, 2019). In addition, the electronics are usually placed away from the transducer which deteriorates the signal-to-noise ratio (SNR) (Suzuki et al, 2018).…”

Section: Sensor Locationmentioning

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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Section: Sensor Locationmentioning

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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“…While the size, sterilizability, biocompatibility, and insulation requirements are more relaxed for the sensors placed outside Sensors placed at the gripper jaw provide the most accurate readings and have the most stringent design constraints. They are difficult to fabricate, package, mount Kim et al (2018b); He et al (2014), and shield Seok et al (2019); Wang et al (2014) and have limited adaptability which makes them cost-prohibitive for disposable instruments Xue et al (2019). Additionally, the electronics are usually placed away from the transducer which deteriorates the Signal to Noise Ratio (SNR) Suzuki et al (2018).…”

Section: Locationmentioning

confidence: 99%

Force Sensing in Robot-assisted Keyhole Endoscopy: A Systematic Survey

Hosseinabadi¹,

Salcudean²

2021

Preprint

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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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“…The perception of force information is conducive to improving the robustness of the hexarotor and fully movable robot control. Six-axis force/torque sensors are widely used in industrial robot collision detection and feedback control [6][7][8]. In some smarter robot systems, six-axis force/torque sensors are used to implement functions such as tactile sensing, safety control, and collision detection [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Research on Collision Point Identification Based on Six-Axis Force/Torque Sensor

et al. 2020

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The collision detection algorithm of the robot body previously needed to rely on the surface geometry information of the colliding object and no deformation was allowed during the collision process. To solve this problem, a new robot body collision detection algorithm that uses the force information of the six-axis force/torque sensor at the base to self-constrain is proposed which does not rely on the geometric information of the colliding object surface, and the deformation also allows deformation during the collision. In terms of sensor data preprocessing, a gravity and dynamic force compensation algorithm for the six-axis force/torque sensor at the base is proposed to ensure that the reading of the six-axis force/torque sensor at the base always maintains the value of 0 when the robot is working. Then, the robot is considered to have collided with the outside world when the sensor reading exceeds the set threshold. And a precision factor is proposed to analyze the influence of force and collision distance on the accuracy of the algorithm. Finally, the new algorithm proposed in this paper is compared with the traditional algorithm that relies on the geometric information of the colliding body surface. The experimental results indicate that the accuracy of the collision point detection algorithm proposed in this paper is close to that of the traditional method, but it does not need to rely on the geometric information of the collision body surface, and there is no requirement for whether there is deformation during the contact process. It can be concluded that the collision distance is the most important factor affecting the accuracy of the algorithm, followed by the conclusion of the magnitude of the collision force through the calculation of the precision factor. The results show that this method can effectively detect the collision point of the machine body, and the maximum error at the farthest point of the robot is 8.712%, which lays a certain foundation for the subsequent research on human-machine collaboration in small collaborative robots.

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An estimation method of grasping force for laparoscope surgical robot based on the model of a cable-pulley system

Cited by 21 publications

References 32 publications

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

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

Force Sensing in Robot-assisted Keyhole Endoscopy: A Systematic Survey

Research on Collision Point Identification Based on Six-Axis Force/Torque Sensor

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