357 research outputs found

    Development of a Novel Impedance-Controlled Quasi-Direct-Drive Robot Hand

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    Most robotic hands and grippers rely on actuators with large gearboxes and force sensors for controlling gripping force. However, this might not be ideal for tasks which require the robot to interact with an unstructured and/or unknown environment. We propose a novel quasi-direct-drive two-fingered robotic hand with variable impedance control in the joint space and Cartesian space. The hand has a total of four degrees of freedom, a backdrivable gear train, and four brushless direct current (BLDC) motors. Field-Oriented Control (FOC) with current sensing is used to control motor torques. Variable impedance control allows the hand to perform dexterous manipulation tasks while being safe during human-robot interaction. The quasi-direct-drive actuators enable the fingers to handle contact with the environment without the need for complicated tactile or force sensors. A majority 3D printed assembly makes this a low-cost research platform built with affordable off-the-shelf components. The hand demonstrates grasping with force-closure and form-closure, stable grasps in response to disturbances, tasks exploiting contact with the environment, simple in-hand manipulation, and a light touch for handling fragile objects.Comment: 75 pages, A Thesis in Partial Fulfillment of the Requirements for the Degree of Master of Science in Mechanical Engineering at Stony Brook Universit

    Ground Robotic Hand Applications for the Space Program study (GRASP)

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    This document reports on a NASA-STDP effort to address research interests of the NASA Kennedy Space Center (KSC) through a study entitled, Ground Robotic-Hand Applications for the Space Program (GRASP). The primary objective of the GRASP study was to identify beneficial applications of specialized end-effectors and robotic hand devices for automating any ground operations which are performed at the Kennedy Space Center. Thus, operations for expendable vehicles, the Space Shuttle and its components, and all payloads were included in the study. Typical benefits of automating operations, or augmenting human operators performing physical tasks, include: reduced costs; enhanced safety and reliability; and reduced processing turnaround time

    νŽ™μΈν™€ μž‘μ—…μ„ μœ„ν•œ λ‹€μžμœ λ„ 그리퍼 및 각도 μ—λŸ¬ μΈ‘μ • μ‹œμŠ€ν…œμ˜ 섀계

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› κ³΅κ³ΌλŒ€ν•™ 기계항곡곡학뢀, 2017. 8. 김쒅원.νŽ™μΈν™€(Peg-In-Hole) μž‘μ—…μ€ λ‘œλ΄‡μ„ ν™œμš©ν•œ μ‘°λ¦½μž‘μ—… 쀑 κ°€μž₯ 기초적인 μž‘μ—…μ΄λΌκ³  ν•  수 μžˆλ‹€. μ‘°κ·Έλ§ˆν•œ μœ„μΉ˜ μ—λŸ¬μ—λ„ λΌμž„ ν˜„μƒ(Jamming λ˜λŠ” Wedging)이 λ°œμƒν•˜κ³  μ΄λŠ” λΆ€ν’ˆ μ‚½μž… 쀑에 νŒŒμ†μ„ μœ λ°œν•  수 있기 λ•Œλ¬Έμ—, 쑰립 λŒ€μƒλ¬Όκ°„μ˜ μœ„μΉ˜ 및 λ°©ν–₯에 λŒ€ν•œ 정렬이 성곡적인 νŽ™μΈν™€ μž‘μ—…μ„ μœ„ν•΄μ„œλŠ” 무엇보닀 μ€‘μš”ν•˜λ‹€. μ΄λŸ¬ν•œ νŽ™μΈν™€ μž‘μ—…μ„ μœ„ν•΄μ„œλŠ” μ§€κΈˆκΉŒμ§€ λ§Žμ€ 연ꡬ가 μ§„ν–‰λ˜μ–΄ μ™”μœΌλ©°, λŒ€μƒλ¬Όκ°„μ˜ μ •λ ¬ 방식에 λ”°λΌμ„œ μˆ˜λ™μ  λ˜λŠ” λŠ₯동적 λ°©λ²•μœΌλ‘œ κ΅¬λΆ„λœλ‹€. RCC(Remote Center Compliance)둜 λŒ€ν‘œλ˜λŠ” μˆ˜λ™μ μΈ 정렬방법은 μ»΄ν”ŒλΌμ΄μ–ΈμŠ€μ™€ λŒ€μƒ λΆ€ν’ˆμ˜ νŠΉμ • λͺ¨μ–‘을 μ΄μš©ν•˜λŠ” λ°˜λ©΄μ—, λŠ₯동적인 정렬방법은 λΉ„μ „μ΄λ‚˜ 쑰립 μ‹œ λ°œμƒν•˜λŠ” 반λ ₯ 정보λ₯Ό μ΄μš©ν•˜μ—¬ λŒ€μƒλ¬Όκ°„μ˜ 정렬을 μˆ˜ν–‰ν•œλ‹€. μˆ˜λ™μ  μ •λ ¬ 방법은 νŠΉλ³„ν•œ μΈ‘μ •μ΄λ‚˜ λ…Έλ ₯ 없이 μ‚¬μš©λ  수 μžˆλ‹€λŠ” μž₯점을 가지고 μžˆμ§€λ§Œ, λΆ€ν’ˆμ˜ 챔버(Chamfer) μ‚¬μ΄μ¦ˆλ‚˜ νŽ™μ˜ 길이 등에 λ”°λΌμ„œ μ‚¬μš© κ°€λŠ₯ μ—¬λΆ€κ°€ κ²°μ •λ˜μ–΄ 적용이 μ œν•œμ μ΄λ‹€. λΉ„μ „μ˜ ν™œμš©μ„ ν†΅ν•œ 정렬도 λ˜ν•œ 적용이 μ œν•œμ μΈλ°, κ·Έ μ΄μœ λŠ” μΉ΄λ©”λΌμ˜ μ„€μΉ˜ μœ„μΉ˜ 및 μ£Όλ³€ ν™˜κ²½μ— λ”°λ₯Έ μΈ‘μ • μ •ν™•λ„μ˜ 민감성 λ•Œλ¬Έμ΄λ‹€. λ³Έ ν•™μœ„ λ…Όλ¬Έμ—μ„œλŠ” 효과적인 νŽ™μΈν™€ μž‘μ—…μ„ μˆ˜ν–‰ν•˜κΈ° μœ„ν•˜μ—¬ λ‹€μžμœ λ„μ˜ 그리퍼, 각도 μ—λŸ¬ μΈ‘μ •κΈ° 및 μΈ‘μ •λœ 힘 정보λ₯Ό κ΅°μ§‘ν™”ν•˜μ—¬ λŒ€μƒλ¬Όκ°„μ˜ μœ„μΉ˜ μ—λŸ¬λ₯Ό μΈ‘μ •ν•  수 μžˆλŠ” μ•Œκ³ λ¦¬μ¦˜μ΄ μ œμ•ˆλ˜μ—ˆλ‹€. 이λ₯Ό μœ„ν•˜μ—¬ ν•˜λ‹¨μ˜ μ£Όμš” 세가지 핡심 κΈ°λŠ₯이 μ‹œμŠ€ν…œ 섀계에 κ΅¬ν˜„λ˜μ—ˆμœΌλ©°, 사각 ν˜•μƒμ˜ νŽ™μΈν™€ μž‘μ—…μ„ 톡해 증λͺ…λ˜μ—ˆλ‹€. μœ„μΉ˜ μ—λŸ¬ 보정 μž‘μ—… μ‹œ λ―Έμ„Έ μ‘°μ • μž‘μ—…μ„ μœ„ν•˜μ—¬, 4 μžμœ λ„λ₯Ό μ§€λ‹Œ 두 개의 μ†κ°€λ½μœΌλ‘œ κ΅¬μ„±λœ 그리퍼가 μ„€κ³„λ˜μ—ˆμœΌλ©°, 손가락 끝 λ‹¨μ—λŠ” 6μΆ• 힘 μ„Όμ„œκ°€ λ‚΄μž¬λ˜μ–΄ 반λ ₯ 츑정을 κ°€λŠ₯ν•˜κ²Œ ν•˜μ˜€λ‹€. λ‘œλ΄‡μ˜ 손λͺ©μ— μ„€μΉ˜λœ 힘 μ„Όμ„œμ™€ λ‘œλ΄‡ νŒ”μ˜ μžμœ λ„λ₯Ό μ‚¬μš©ν•˜μ—¬ μž‘μ—…μ„ μˆ˜ν–‰ν•˜λŠ” 일반적인 λ°©λ²•κ³ΌλŠ” 달리, μ„€κ³„λœ λ‹€μžμœ λ„ 그리퍼λ₯Ό ν™œμš©ν•˜μ—¬ νŽ™μ„ μ‘°μž‘ κ°€λŠ₯ν•˜κ²Œ ν•˜μ˜€λ‹€. λ˜ν•œ, νŽ™μ˜ μ–‘ μΈ‘λ©΄μ—μ„œ λ°œμƒλœ 반λ ₯ 정보듀을 νŽ™μ˜ μœ„μΉ˜ 정보와 ν•¨κ»˜ μ €μž₯ν•˜μ—¬ μœ„μΉ˜μ—λŸ¬ λ„μΆœμ— ν™œμš© κ°€λŠ₯ν•˜λ„λ‘ ν•˜μ˜€λ‹€. 2 μžμœ λ„μ˜ 직ꡐ λ‘œλ΄‡κ³Ό λ ˆμ΄μ € 거리 μ„Όμ„œλ‘œ κ΅¬μ„±λœ κ²¬μ‹€ν•œ 각도 μΈ‘μ •κΈ°(Scanner)κ°€ νŽ™κ³Ό 홀 μ‚¬μ΄κ°„μ˜ 각도 μ—λŸ¬ 보정을 μœ„ν•˜μ—¬ 섀계 및 κ΅¬ν˜„λ˜μ—ˆλ‹€. νŽ™κ³Ό 홀 μ‚¬μ΄κ°„μ˜ 접촉 쑰건에 λ”°λΌμ„œ λͺ¨λ©˜νŠΈ 반λ ₯의 λ°œμƒ μœ λ¬΄κ°€ κ²°μ •λ˜λŠ”λ°, 힘 정보λ₯Ό λ°”νƒ•μœΌλ‘œ ν•œ λΉ λ₯΄κ³  μ‹ λ’°μ„± μžˆλŠ” μ—λŸ¬ 좔정을 μœ„ν•΄μ„œλŠ” 각도 μ—λŸ¬ 츑정을 ν†΅ν•œ 보정을 ν•„μš”λ‘œ ν•œλ‹€. μ‚¬κ°ν˜•μƒμ˜ νŽ™ 인 홀 μž‘μ—…μ˜ κ²½μš°μ—λŠ”, νŽ™κ³Ό 홀 μ‚¬μ΄κ°„μ˜ 엣지 및 지지 면의 μˆ˜μ— λ”°λΌμ„œ 총 5κ°€μ§€μ˜ 경우둜 접촉 쑰건이 λΆ„λ₯˜κ°€ λ˜λŠ”λ°, λͺ¨λ©˜νŠΈλŠ” κ·Έ μ€‘μ—μ„œ ν•œκ°€μ§€μ˜ κ²½μš°μ—λ§Œ λ°œμƒν•˜κ²Œ λœλ‹€. 각도 μ—λŸ¬ 보정을 ν†΅ν•˜μ—¬, 접촉 쑰건은 2κ°€μ§€λ‘œ μ€„μ–΄λ“€κ²Œ 되며, 이λ₯Ό ν†΅ν•˜μ—¬ μ—λŸ¬ 보정 μ‹œκ°„μ„ μ€„μ΄λŠ” 것이 κ°€λŠ₯ν•˜λ‹€. νŽ™κ³Ό 홀 μ‚¬μ΄κ°„μ˜ μœ„μΉ˜ μ—λŸ¬λ₯Ό μΆ”μΆœν•˜κΈ° μœ„ν•˜μ—¬, λͺ¨λ©˜νŠΈ 반λ ₯ 정보와 νŽ™μ˜ μœ„μΉ˜ μ •λ³΄λ‘œ κ΅¬μ„±λœ 데이터 μ„ΈνŠΈμ— ꡰ집화 μ•Œκ³ λ¦¬μ¦˜μ„ μ μš©ν•˜μ˜€λ‹€. 각도 μ—λŸ¬ 보정 후에도, λͺ¨λ©˜νŠΈκ°€ λ°œμƒν•˜μ§€ μ•ŠλŠ” κ²½μš°κ°€ λ‚¨κ²Œ 되며 μ΄λŸ¬ν•œ ν˜Όν•©λœ 데이터 μ„ΈνŠΈμ—μ„œλ„ μœ„μΉ˜ μ—λŸ¬λ₯Ό μΆ”μΆœν•  수 μžˆλŠ” 인곡지λŠ₯을 ν•„μš”λ‘œ ν•œλ‹€. 이λ₯Ό μœ„ν•˜μ—¬, 기계 ν•™μŠ΅μ—μ„œ μ‚¬μš©λ˜λŠ” 두 κ°€μ§€μ˜ λŒ€ν‘œμ μΈ μ•Œκ³ λ¦¬μ¦˜, K 평균 μ•Œκ³ λ¦¬μ¦˜κ³Ό κ°€μš°μ‹œμ•ˆ ν˜Όν•© λͺ¨λΈ μ•Œκ³ λ¦¬μ¦˜μ„ λ‹€μ–‘ν•œ μΈ‘μ • 데이터 μ„ΈνŠΈλ“€μ— μ μš©ν•˜μ˜€λ‹€. μ—λŸ¬ μΆ”μΆœ μ‹œ μ•Œκ³ λ¦¬μ¦˜μ˜ 정확도와 견싀함을 확인 ν•˜κΈ° μœ„ν•˜μ—¬ 같은 μ‘°κ±΄μ—μ„œ μΈ‘μ •λ˜κ±°λ‚˜ λ‹€λ₯Έ μ†λ„μ—μ„œ μΈ‘μ •λœ μ„Έ 개의 데이터 μ„ΈνŠΈκ°€ μœ„μΉ˜ μ—λŸ¬ μΆ”μΆœμ„ μœ„ν•˜μ—¬ μ‚¬μš©λ˜μ—ˆλ‹€. K 평균 μ•Œκ³ λ¦¬μ¦˜μ˜ 경우, μΆ”μΆœλœ μœ„μΉ˜ μ—λŸ¬μ˜ 정확도와 각각의 데이터 μ„ΈνŠΈμ—μ„œ μΆ”μΆœλœ μœ„μΉ˜ μ—λŸ¬ κ°’λ“€μ˜ νŽΈμ°¨λŠ” 각각 0.29mm, 0.14mm μ΄λ‚΄μ΄μ§€λ§Œ, κ°€μš°μ‹œμ•ˆ ν˜Όν•© λͺ¨λΈ μ•Œκ³ λ¦¬μ¦˜μ˜ κ²½μš°μ—λŠ” 각각 0.44mm, 0.43mmλ₯Ό 보이고 μžˆλ‹€. K 평균 μ•Œκ³ λ¦¬μ¦˜μ€ μœ„μΉ˜ μ—λŸ¬ μΆ”μΆœμ—μ„œ μ•ˆμ •μ μΈ 정확도와 견싀함을 가지며, κ°€μš°μ‹œμ•ˆ ν˜Όν•© λͺ¨λΈ μ•Œκ³ λ¦¬μ¦˜μ€ μœ„ν•˜μ—¬ μ œν•œμ‘°κ±΄μ„ μ§€λ‹Œ νŒŒλΌλ―Έν„° μ‚¬μš©μ„ ν•„μš”λ‘œ ν•˜λŠ” 것을 확인할 수 μžˆλ‹€. μ„Όμ„œλ‘œλΆ€ν„°μ˜ 정보에 μ˜μ§€ν•˜μ§€ μ•Šκ³ , κΈ΄ λ‚˜μ„ ν˜• κΆ€μ λ§Œμ„ μ΄μš©ν•˜μ—¬ μ—λŸ¬ 보정을 μˆ˜ν–‰ν•˜λŠ” λΈ”λΌμΈλ“œ μ„œμΉ˜(Blind Search)와 비ꡐ할 λ•Œ, μ œμ•ˆλœ 츑정기와 μœ„μΉ˜ μΆ”μΆœ μ•Œκ³ λ¦¬μ¦˜μ€ 짧고 νŽΈμ°¨κ°€ μ—†λŠ” μ—λŸ¬ 보정 μ‹œκ°„μ˜ μž₯점을 가지고 μžˆλ‹€. 주어진 검색 μ˜μ—­μ„ 수직 μˆ˜ν‰μœΌλ‘œ μ›€μ§μ΄λŠ” 짧은 XY ꢀ적을 μ‚¬μš©ν•˜μ—¬ μ—λŸ¬ 보정 μ‹œκ°„μ„ 단좕 κ°€λŠ₯ν•˜κ²Œ ν•˜κ³ , 각도 μ—λŸ¬ 보정을 ν†΅ν•˜μ—¬ 접촉 쑰건 경우의 수λ₯Ό μ€„μ΄λ©΄μ„œ μ—λŸ¬ 보정을 μœ„ν•œ μ‹œκ°„μ— νŽΈμ°¨κ°€ 없도둝 ν•˜μ˜€λ‹€.Peg-In-Hole is the one of basic tasks for robotic assembly. For successful Peg-In-Hole, the position and orientation alignment between mating parts is very important because small error can induce jamming and wedging which generates excessive force leading to damages on mating parts during insertion. A lot of researches for Peg-In-Hole task have been underway and it can be categorized into passive and active approaches. The passive approach represented by Remote Center Compliance uses the compliance and shape of mating parts for alignment, whereas the active approach uses measurement from vision, force or both of them. Passive approach has strength in which alignment can be done passively without any other measurements but applications are limited because it depends on the shape of mating parts like chamfer size and length of peg. Utilization of vision is also limited because of sensitivity in accuracy which is affected significantly by camera location and surrounding environment. In this dissertation, a dexterous gripper with an angular error measuring instrument and reliable position error estimation algorithm by clustering the force dataset is proposed for Peg-In-Hole task. Three main key features stated below are implemented in the system design and tested with square Peg-In-Hole experiments. The dexterous gripper which consists of 4 DOF(Degree Of Freedom) two fingers embedded with 6 axis force sensors at the fingertip is designed for micro manipulation during error recovery. Unlike the usual method in which force sensor is mounted on the robot wrist and peg is manipulated by robot arm, the designed dexterous gripper is used for both of grasping and manipulating peg. Reaction force generated on both side of peg is also measured at fingertip and recorded with peg position for error estimation. Robust angle measuring instrument, Scanner, consisted of 2DOF manipulator and laser distance sensor is also designed and implemented for detecting the angular error between peg and hole. Depending on the contact condition, its decided whether moment is generated or not, thus angular error compensation is necessary for fast and reliable error estimation based on the force data. In case of square Peg-In-Hole, the contact condition can be classified into 5 cases depending on the number of edge and supporting area between peg and hole and moment is generated in only one case. With the angular error compensation, the number of contact condition can be diminished to 2 cases thus shortened recovery time can be accomplished. To extract the position error between peg and hole, error estimation with clustering algorithm is applied to the measured dataset of moment and peg position. Even after angular error compensation, there still exists the condition which generates no reaction moment, thus artificial intelligence which can extract the position error among mixed dataset is required. Two representative algorithms, K means algorithms and Gaussian Mixture Model algorithm, commonly used in machine learning for clustering dataset are applied to various datasets constructed with position and moment for estimating position error. Two datasets, one constructed with the three datasets measured at same condition and the other constructed with three datasets measured with different velocity are used to check accuracy and robustness in error estimation from both of algorithm. The accuracy of estimated position error and deviation among estimated error in each dataset from K means algorithm is within 0.29mm and 0.14mm whereas both of that from Gaussian Mixture Model algorithm is within 0.44mm and 0.43mm. K means algorithm shows stable accuracy and robustness on position error estimation whereas the Gaussian Mixture Model algorithm needs to use constrained parameter for both of them. Comparing with blind search which uses no information from sensors and long spiral trajectory for error recovery, the proposed measurement system and algorithms have advantages in terms of recovery time and no variation of it. Short XY trajectory which moves horizontally and vertically in given search area can be used and error recovery time have no variation regardless of position error by diminishing the number of contact conditions through angular error compensation.Chapter 1. Introduction 1 1.1. Robotic Assembly and Peg-In-Hole Task 1 1.2. Previous Research Works 2 1.2.1. Passive approaches 3 1.2.2. Active approaches 5 1.3. Purpose and Contribution of Research 9 Chapter 2. Contact Condition Analysis 12 2.1. Classification of Contact Condition 12 2.1.1. Connected Component Labeling 12 2.1.2. Binary image generation procedure 13 2.1.3. Analysis results for contact condition 14 2.2. Force and Moment depending on Contact Condition 17 Chapter 3. Design Synthesis of Gripper and Scanner 21 3.1. Overall Design Overview 21 3.2. Design and Mechanism of Finger 23 3.2.1. Advantages of parallel mechanism 23 3.2.2. Mechanism description of finger 28 3.2.3. Kinematics of finger 31 3.3 Design and Mechanism of Scanner 33 3.3.1. Mechanism description 33 3.3.2. FEM analysis for deflection compensation 34 Chapter 4. Error Recovery Algorithms 40 4.1. Clustering for Error Estimation 40 4.1.1. K means algorithm 41 4.1.2. Gaussian Mixture Model algorithm 42 4.2. Procedure for Error Recovery 44 4.3. Comparison of Error Recovery Algorithms 45 4.3.1. Comparison of trajectory in blind and XY search 45 4.3.2. Comparison of trajectory for position error recovery 46 4.3.3. Comparison of trajectory for angular error recovery 49 4.3.4. Comparison of variation in recovery time 50 Chapter 5. Experimental Results 52 5.1. Angular Error Measurement of Scanner 52 5.1.1. Verification of scanner accuracy and repeatability 52 5.1.2. Measurement and alignment of angular error 56 5.2. Reaction Moment Measurement at Fingertip 58 5.2.1. Measurement of moment data 58 5.2.2. Description of measurement condition 59 5.2.3. Clustering results from K means algorithm 61 5.2.4. Clustering results from Gaussian Mixture Model Algorithm 64 5.2.5 Comparison of clustering result 69 Chapter 6. Conclusion 71 Bibliography 74 Abstract in Korean 78Docto

    Haptic Exploration of Unknown Objects for Robust in-hand Manipulation.

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    Human-like robot hands provide the flexibility to manipulate a variety of objects that are found in unstructured environments. Knowledge of object properties and motion trajectory is required, but often not available in real-world manipulation tasks. Although it is possible to grasp and manipulate unknown objects, an uninformed grasp leads to inferior stability, accuracy, and repeatability of the manipulation. Therefore, a central challenge of in-hand manipulation in unstructured environments is to acquire this information safely and efficiently. We propose an in-hand manipulation framework that does not assume any prior information about the object and the motion, but instead extracts the object properties through a novel haptic exploration procedure and learns the motion from demonstration using dynamical movement primitives. We evaluate our approach by unknown object manipulation experiments using a human-like robot hand. The results show that haptic exploration improves the manipulation robustness and accuracy significantly, compared to the virtual spring framework baseline method that is widely used for grasping unknown objects

    Adaptive and reconfigurable robotic gripper hands with a meso-scale gripping range

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    Grippers and robotic hands are essential and important end-effectors of robotic manipulators. Developing a gripper hand that can grasp a large variety of objects precisely and stably is still an aspiration even though research in this area has been carried out for several decades. This thesis provides a development approach and a series of gripper hands which can bridge the gap between micro-gripper and macro-gripper by extending the gripping range to the mesoscopic scale (meso-scale). Reconfigurable topology and variable mobility of the design offer versatility and adaptability for the changing environment and demands. By investigating human grasping behaviours and the unique structures of human hand, a CFB-based finger joint for anthropomorphic finger is developed to mimic a human finger with a large grasping range. The centrodes of CFB mechanism are explored and a contact-aided CFB mechanism is developed to increase stiffness of finger joints. An integrated gripper structure comprising cross four-bar (CFB) and remote-centre-of-motion (RCM) mechanisms is developed to mimic key functionalities of human hand. Kinematics and kinetostatic analyses of the CFB mechanism for multimode gripping are conducted to achieve passive-adjusting motion. A novel RCM-based finger with angular, parallel and underactuated motion is invented. Kinematics and stable gripping analyses of the RCM-based multi-motion finger are also investigated. The integrated design with CFB and RCM mechanisms provides a novel concept of a multi-mode gripper that aims to tackle the challenge of changing over for various sizes of objects gripping in mesoscopic scale range. Based on the novel designed mechanisms and design philosophy, a class of gripper hands in terms of adaptive meso-grippers, power-precision grippers and reconfigurable hands are developed. The novel features of the gripper hands are one degree of freedom (DoF), self-adaptive, reconfigurable and multi-mode. Prototypes are manufactured by 3D printing and the grasping abilities are tested to verify the design approach.EPSR

    On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

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    Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas
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