3 research outputs found
Design and Development of Instrumented Remote Centre Compliance
In the field of robotics and automatic assembly tooling, it is often necessary to provide some compliance when fitting two parts together or when engaging a tool with a complementarily shaped aperture. This need arises because of the tolerances in gripping and positioning capability of a robot arm and the dimensional tolerances of the members being positioned. The use of excessive force to engage two imperfectly aligned members can lead to damage to the members or assembly tooling. A remote centre compliance (RCC) is a device that can provide a compliance center projected outward from the device. Remote compliance centers decouple lateral and angular motion. A RCC device can be used in assembly to ease the insertion force. When a project compliance center is near the insertion point of a peg-in-hole type assembly, the peg translates into the hole when it strikes the outside lead-in chamfer without rotating. This translation without rotation prevents the jamming and galling seen from compliance devices that have a compliance center far away from the insertion point. The proposed work aims at designing and developing an intelligent RCC device which helps the parts assemble even if there are misalignments of known limits and is capable of capturing useful information for the assembly process
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μΌμλ‘λΆν°μ μ 보μ μμ§νμ§ μκ³ , κΈ΄ λμ ν κΆ€μ λ§μ μ΄μ©νμ¬ μλ¬ λ³΄μ μ μννλ λΈλΌμΈλ μμΉ(Blind Search)μ λΉκ΅ν λ, μ μλ μΈ‘μ κΈ°μ μμΉ μΆμΆ μκ³ λ¦¬μ¦μ μ§§κ³ νΈμ°¨κ° μλ μλ¬ λ³΄μ μκ°μ μ₯μ μ κ°μ§κ³ μλ€. μ£Όμ΄μ§ κ²μ μμμ μμ§ μνμΌλ‘ μμ§μ΄λ 짧μ XY κΆ€μ μ μ¬μ©νμ¬ μλ¬ λ³΄μ μκ°μ λ¨μΆ κ°λ₯νκ² νκ³ , κ°λ μλ¬ λ³΄μ μ ν΅νμ¬ μ μ΄ μ‘°κ±΄ κ²½μ°μ μλ₯Ό μ€μ΄λ©΄μ μλ¬ λ³΄μ μ μν μκ°μ νΈμ°¨κ° μλλ‘ νμλ€.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