7 research outputs found

    A Study on the Development of Surgical-Operation-By-Wire (SOBW) for Advanced Surgical Robot System

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต, 2015. 8. Sungwan Kim.๊ธฐ์กด์˜ ๊ฐœ๋ณต ์ˆ˜์ˆ ์— ๋น„ํ•ด ์ตœ์†Œ ์นจ์Šต ์ˆ˜์ˆ ์€ ๋งŽ์€ ์žฅ์ ์ด ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์กด ๋ณต๊ฐ•๊ฒฝ ๋„๊ตฌ๋ฅผ ์ด์šฉํ•œ ์ตœ์†Œ ์นจ์Šต ์ˆ˜์ˆ ์˜ ํ•œ๊ณ„๋ฅผ ๊ทน๋ณตํ•˜๊ณ ์ž ๋กœ๋ด‡์„ ์ด์šฉํ•œ ๋ณต๊ฐ•๊ฒฝ ์ˆ˜์ˆ ์ด ๋„๋ฆฌ ์‹œํ–‰๋˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๋Œ€ํ‘œ์ ์ธ ๋ณต๊ฐ•๊ฒฝ ์ˆ˜์ˆ  ๋กœ๋ด‡์ธ ๋‹ค๋นˆ์น˜ ๋กœ๋ด‡์˜ ๊ฒฝ์šฐ ์—”๋“œ์ดํŽ™ํ„ฐ์˜ ์ง‘๊ฒŒ๊ฐ€ ๋‹ค์–‘ํ•œ ์ž์„ธ์—์„œ ๊ท ์ผํ•œ ํž˜์„ ๋‚ด์ง€ ๋ชปํ•˜๋Š” ๊ฒƒ์ด ๋‹ค๋ฅธ ์—ฐ๊ตฌ์ง„์— ์˜ํ•ด ๋ฐํ˜€์กŒ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋ฅผ ๊ฐ€์„ค๋กœ ๋‘๊ณ  ์ด๋ฅผ ๊ตฌ์ฒด์ ์ธ ์‹คํ—˜์œผ๋กœ ๊ทœ๋ช…ํ•˜์˜€์œผ๋ฉฐ, ๋ฌธ์ œ์˜ ์›์ธ์ด ๊ธˆ์† ์ค„๋กœ ์ œ์–ด๋˜๋Š” ์—”๋“œ์ดํŽ™ํ„ฐ ๋•Œ๋ฌธ์ž„์„ ์ฆ๋ช…ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์—”๋“œ์ดํŽ™ํ„ฐ์˜ ์ง‘๋Š” ํž˜, ์ž์„ธ์— ๋”ฐ๋ฅธ ์ปค๋„ฅํ„ฐ ๊ฐ๋„, ์ „๋‹ฌ ํ† ํฌ๋ฅผ ์ƒˆ๋กœ์ด ๊ณ ์•ˆํ•œ ํ† ํฌ์ „๋‹ฌ์‹œ์Šคํ…œ์œผ๋กœ ์ธก์ •ํ•˜์˜€๋‹ค. ์ธก์ • ๊ฒฐ๊ณผ ์˜์‚ฌ์˜ ๊ท ์ผํ•œ ์˜๋„์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  27๊ฐ€์ง€ ์ž์„ธ์—์„œ ์„ธ๊ฐ€์ง€ ์—”๋“œ์ดํŽ™ํ„ฐ๊ฐ€ ๋ชจ๋‘ ๋‹ค๋ฅธ ํž˜์„ ๋‚ด์—ˆ์œผ๋ฉฐ ์ตœ์†Œ 1.84๋ฐฐ์—์„œ ์ตœ๋Œ€ 3.37๋ฐฐ์˜ ์ฐจ์ด๊ฐ€ ๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋‹จ์ ์„ ๊ทน๋ณตํ•˜๊ณ ์ž ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ๊ฐ€์ง€ ์ธก๋ฉด์—์„œ ํ•ด๊ฒฐ์ฑ…์„ ์ œ์‹œํ•˜์˜€๋‹ค. ์ฒซ์งธ๋กœ, ๋‹ค๋นˆ์น˜์˜ ์—”๋“œ์ดํŽ™ํ„ฐ ๋‚ด๋ถ€ ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ๋ถ„์„ํ•˜์—ฌ ์—”๋“œ์ดํŽ™ํ„ฐ์˜ ๋‹ค์–‘ํ•œ ์ž์„ธ์—์„œ ๊ท ์ผํ•œ ํž˜์„ ๋‚ด๊ธฐ ์œ„ํ•œ ๋ณด์ƒ ํž˜์„ ์ œ์‹œํ•˜๋Š” ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋ชจ๋ธ์—์„œ ๊ณ„์‚ฐ๋˜๋Š” ๊ฐ’๊ณผ ์‹ค์ œ ๊ฐ’์„ ๋น„๊ตํ•˜์—ฌ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ํ† ํฌ์ „๋‹ฌ์‹œ์Šคํ…œ์„ ํ†ตํ•ด ์–ป์€ ํŒŒ๋ผ๋ฏธํ„ฐ๋กœ๋ถ€ํ„ฐ 10.69-16.25%์˜ ์˜ค์ฐจ ๋ฒ”์œ„ ๋‚ด์—์„œ ์˜ˆ์ธก ์ง‘๋Š” ํž˜์„ ๊ณ„์‚ฐํ•˜๋Š” ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•˜์˜€๋‹ค. ๋ณธ ๋ชจ๋ธ์„ ์ด์šฉํ•˜๋ฉด ๊ธฐ์กด ๋‹ค๋นˆ์น˜ ์‹œ์Šคํ…œ์˜ ๊ตฌ์กฐ์  ๋ฌธ์ œ๋ฅผ ์†Œํ”„ํŠธ์›จ์–ด์ ์œผ๋กœ ๊ทน๋ณตํ•˜๋Š”๋ฐ ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ์˜์‚ฌ๋Š” ์—”๋“œ์ดํŽ™ํ„ฐ ์ง‘๊ฒŒ์— ์ž‘์šฉํ•˜๋Š” ์‹ค์ œ ํž˜์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์–ป์„ ์ˆ˜ ์žˆ์œผ๋ฉฐ, ๋งˆ์Šคํ„ฐ ์ธํ„ฐํŽ˜์ด์Šค์— ์••๋ ฅ ์„ผ์„œ ๋“ฑ์ด ๊ตฌ๋น„๋˜๋ฉด ์ง‘๋Š” ํž˜์„ ์›ํ•˜๋Š” ๋Œ€๋กœ ์กฐ์ •ํ•  ์ˆ˜๋„ ์žˆ์–ด์„œ ์ˆ˜์ˆ  ๋„์ค‘ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ์‚ฌ๊ณ ๋ฅผ ๋ฏธ์—ฐ์— ๋ฐฉ์ง€ ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋‘˜์งธ๋กœ, ๋‹ค๋นˆ์น˜ ์‹œ์Šคํ…œ์˜ ๊ตฌ์กฐ์  ๋ฌธ์ œ๋ฅผ ๊ทผ๋ณธ์ ์œผ๋กœ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ƒˆ๋กœ์šด ์ˆ˜์ˆ  ๋กœ๋ด‡ ์—”๋“œ์ดํŽ™ํ„ฐ ์‹œ์Šคํ…œ, Surgical-Operation-By-Wire (SOBW)๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. 6์ถ• ๋กœ๋ด‡ํŒ”์„ ์‚ฌ์šฉํ•˜์—ฌ ์ƒˆ๋กœ์šด ์—”๋“œ์ดํŽ™ํ„ฐ์™€ ํ•จ๊ป˜ ์ˆ˜์ˆ ์— ์“ฐ์ผ ์ˆ˜ ์žˆ๋Š” ์ถ”๊ฐ€์˜ ์ž์œ ๋„๋ฅผ ๊ฐ–์ถ”์—ˆ๋‹ค. ์ œ์•ˆ๋œ ์ˆ˜์ˆ  ๋กœ๋ด‡ ์‹œ์Šคํ…œ์€ ํ•ญ๊ณต์šฐ์ฃผ๊ณตํ•™๊ธฐ์ˆ ์— ๋„๋ฆฌ ์“ฐ์ด๋Š” Hands-On-Throttle-And-Stick (HOTAS)์„ ํ™œ์šฉํ•˜์—ฌ 6์ถ• ํž˜/ํ† ํฌ ์„ผ์„œ๊ฐ€ ์ถ”๊ฐ€๋œ iHOTAS ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ํ†ตํ•ด ์ œ์–ด๋œ๋‹ค. ์ง‘๊ฒŒ์˜ ๋ฐ˜์‘์‹œ๊ฐ„์ด 0.2์ดˆ๋กœ ๊ณ„์‚ฐ๋˜์—ˆ๊ณ , ๋ณธ ์‹œ์Šคํ…œ์„ ์ฒ˜์Œ ์ ‘ํ•˜๋Š” ์ฐธ๊ฐ€์ž๊ฐ€ ์ˆ ๊ธฐ ํ…Œ์ŠคํŠธ์—์„œ ํ‰๊ท  176์ดˆ์•ˆ์— ์ˆ˜ํ–‰ํ•˜์—ฌ 300์ดˆ ์ปท์˜คํ”„ ํƒ€์ž„์•ˆ์— ์ˆ˜ํ–‰ํ•  ์ˆ˜ ์žˆ๊ฒŒ ์‹œ์Šคํ…œ์ด ์ž˜ ๊ตฌ์„ฑ๋˜์—ˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์‹œ์Šคํ…œ์˜ ๋™์ž‘ ๋ฒ”์œ„๋Š” 11,157.0 cm3์œผ๋กœ ๊ณ„์‚ฐ๋˜์—ˆ๋‹ค. ๋‹ค์–‘ํ•œ ๊ฒ€์ฆ์„ ํ†ตํ•ด ์ œ์•ˆ๋œ ์ˆ˜์ˆ  ๋กœ๋ด‡ ์‹œ์Šคํ…œ์ด ์‹ค์ œ ์ˆ˜์ˆ ์— ์ถฉ๋ถ„ํžˆ ์“ฐ์ผ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.Abstract i List of Tables iv List of Figures vi Contents x 1. Introduction 1 1.1. Robotic Laparoscopic Surgery 1 1.2. End-effectors and Master Interfaces in Robotic Laparoscopic Surgery 8 1.3. Objectives and Scope 12 1.3.1. Gripping Force Measurement for Various Postures and Mathematical Compensation Model 17 1.3.1.1. Torque Transfer System (TTS) 18 1.3.1.2. Calibration of the Sensors 21 1.3.1.3. Force Measurement with Respect to the EndoWrists Posture 23 1.3.2. Novel End-effector and Mater Interface 31 ? 2. Materials and Methods 34 2.1. EndoWrist Inner Mechanism Model 34 2.2. Development of the Laparoscopic Robot 37 2.2.1. Overview 40 2.2.2. External Arm 40 2.2.3. End-effector (KS-4) 42 2.2.3.1. Pneumatic Gripper System 48 2.2.4. Forward Kinematics of the System 53 3. Results 58 3.1.Prediction of the Compensation Force for EndoWrists 58 3.1.1. EndoWrists Gripping Force 58 3.1.2. Prediction Results and Validation 60 3.2. Pneumatic Type of End-effector (KS-4) and Novel Master Interfaces 63 3.2.1. End-effectors Gripping Force 63? 3.2.1.1. Gripping Force System Setup 63 3.2.1.2. Relationship between Compressors Pressure and Gripping Force 66 3.2.1.3. Reaction Time 68 3.2.1.4. Durability Test 71 3.2.2. Simple Peg Task 72 3.2.3. Workspace 76 3.2.4. System Specification 77 4. Discussion 80 5. Conclusion 89 References 90 Abstract in Korean 100Docto

    Force Sensing Surgical Scissor Blades using Fibre Bragg Grating Sensors

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    This thesis considers the development and analysis of unique sensorised surgical scissor blades for application in minimally invasive robotic surgery (MIRS). The lack of haptic (force and tactile) feedback to the user is currently an unresolved issue with modern MIRS systems. This thesis presents details on smart sensing scissor blades which enable the measurement of instrument-tissue interaction forces for the purpose of force reflection and tissue property identification. A review of current literature established that there exists a need for small compact, biocompatible, sterilisable and robust sensors which meet the demands of current MIRS instruments. Therefore, the sensorised blades exploit the strain sensing capabilities of a single fibre Bragg grating (FBG) sensor bonded to their surface. The nature and magnitude of the strain likely to be experienced by the blades, and consequently the FBG sensor, while cutting soft tissue samples were characterised through the use of an application specific test-bed. Using the sensorised blades to estimate fracture properties is proposed, hence two methods of extracting fracture toughness information from the test samples are assessed and compared. Investigations were carried out on the factors affecting the transfer of strain from the blade material to the core of the FBG sensor for surface mounted or partially embedded arrangements. Results show that adhesive bond length, thickness and stiffness need to be carefully specified when bonding FBG sensors to ensure effective strain transfer. Calibration and dynamic cutting experiments were carried out using the characterisation test-bed. The complex nature of the blade interaction forces were modelled, primarily for the purpose of decoupling the direct, lateral, friction and fracture strains experienced by the bonded FBG sensor during cutting. The modelled and experimental results show that the approach taken in sensorising the blade enables detailed cutting force data to be obtained and consequently leads to a unique method in estimating the kinetic friction coefficient for the blades. The forces measured using the FBG are validated against a commercial load cell used in the test-bed. This research work demonstrates that this unique approach of placing a single optical fibre onto the scissor blades can, in an unobtrusive manner, measure interblade friction forces and material fracture properties occurring at the blade-tissue interface

    Characterization of soft tissue cutting for haptic display: experiments and computational models

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    Real-time medical simulation for robotic surgery planning and surgery training requires realistic yet computationally fast models of the mechanical behavior of soft tissue. This work presents a study to develop such a model to enable fast haptics display in simulation of softtissue cutting. An apparatus was developed and experiments were conducted to generate force-displacement data for cutting of soft tissue such as pig liver. The force-displacement curve of cutting pig liver revealed a characteristic pattern: the overall curve is formed by repeating units consisting of a local deformation segment followed by a local crack-growth segment. The modeling effort reported here focused on characterizing the tissue in the local deformation segment for fast haptic display. The deformation resistance of the tissue was quantified in terms of the local effective modulus (LEM) consistent with experimental forcedisplacement data. An algorithm was developed to determine LEM by solving an inverse problem with iterative finite element models. To enable faster simulation of cutting of a threedimensional (3D) liver specimen of naturally varying thickness, three levels of model order reduction were studied. Additionally, the variation of the LEM with cutting speed was determined. The values of LEM decreased as the cutting speed increased. This thesis also includes the characteristic response of soft tissue to the growth of a cut (cracking) with a scalpel blade. The experimentally measured cut-force versus cut-length data was used to determine the soft tissueโ€™s resistance to fracture (resistance to crack extension) in scalpel cutting. The resistance to fracture of the soft tissue is defined as the amount of mechanical work needed to cause a cut (crack) to extend for a unit length in a soft-tissue sample of unit thickness. The equipment, method, and model are applicable for all soft tissue.Finally, the method of determining the property of the pig liver tissue during cutting was verified. Dual C-arm fluoroscopes were used to obtain the motion of the beads embedded inside the specimen during cutting. The experimentally measured displacement field was compared to the displacement field obtained through finite element model based on the LEM values at each localized area.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 200

    Variational methods for modeling and simulation of tool-tissue interaction

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    Ph.DDOCTOR OF PHILOSOPH

    Perceptual Similarities Amongst Novel, 3D Objects

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    Measurement, Analysis and Display of Haptic Signals during Surgical Cutting

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    The forces experienced while surgically cutting anatomical tissues from a sheep and two rats were investigated for three scissor types. Data were collected in situ using instrumented Mayo, Metzenbaum, and Iris scissors immediately after death to minimize post-mortem e#ects. The force-position relationship, the frequency components present in the signal, the significance of the cutting rate, as well as other invariant properties, were investigated after segmentation of the data into distinct task phases
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