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研究生:彭迦儒
研究生(外文):Chia-Ju Peng
論文名稱(外文):Sensing and actuation with interpenetrated polymers, application in microrobotics
指導教授:陳世叡Luc ChassagneBarthélemy Cagneau
指導教授(外文):Shih-Jui ChenLuc ChassagneBarthélemy Cagneau
學位類別:博士
校院名稱:國立中央大學
系所名稱:機械工程學系
學門:工程學門
學類:機械工程學類
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:220
中文關鍵詞:軟機械人導電聚合物致動器感測器
外文關鍵詞:Soft roboticsConducting polymersActuatorsSensors
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本論文針對導電互穿聚合物(C-IPN)換能器之特性進行量測與分析,藉由了解C-IPN在致動或感測過程中之機械與電壓響應,包括輸出電壓、形變量、出力等參數關係,來研究該材料作為致動器、感測器在微機器人領域之應用可行性,且實施在C-IPN控制系統中。導電聚合物的優點是驅動電壓低、變形能力大、易於製造以及可以與微機電製程相整合。C-IPN為一類以導電聚合物PEDOT為主所構成之三層互穿結構(導電聚合物/固態電解質/導電聚合物)智能材料,由於可逆的電化學能量轉換過程,導電聚合物具有作為致動器與力學感測器的發展潛力。
為了快速了解C-IPN的特性與降低實驗操作中精密量測的困難度,本研究採取先製作大尺寸樣本在巨觀的尺度下進行量測與探討,並將結果驗證與應用於小尺度樣本上。研究中探討了該導電聚合物致動與感測之性能。在機械響應方面,其力學模型可模擬與估算該細長形彎曲C-IPN換能器之自由位移和輸出力等機械產出,通過實驗和驗證,結果顯示了尖端位移、受力、與驅動電壓的關係。在電壓響應方面,實驗中測量了在不同彎曲條件下C-IPN的輸出訊號,並導出電響應的等效電路模型參數,以便識別C-IPN在受壓情形下輸出電壓隨時間變化的關係。此外,本研究針對隨時間下降之感測電壓與電量,提出了一種補償方法來平衡導電聚合物感測器的電壓損失。
為了同時展現同一材料感測和致動的功能,本文以微型鑷子為例,展示了由一C-IPN致動臂與C-IPN感測臂組成之夾取裝置,最終目標為設計與發展高自由度微型鑷子或其他軟機械人及微型機器人應用。C-IPN鑷子夾取裝置可透過控制系統設置驅動電壓以操作致動臂之抓握力,夾持一物體並利用感測臂之輸出訊號來進行抓取狀態之監測。夾持與釋放物體過程中之各階段皆成功以實驗線性模型進行預測與識別,量測結果可用於建立回授控制,並充分展示了C-IPN換能器之應用潛力。
This thesis deals with the measurements, modeling, and the demonstration of a conducting polymer transducer for robotic applications. As a subfamily of electroactive polymers, conducting polymer has advantages of low operating voltage, large deformation capabilities, ease of manufacturing, and integration within the micro-electro-mechanical system (MEMS). Because of a reversible electrochemical process, conducting polymers can be used as actuators or mechanical sensors. In this work, we investigate a PEDOT-based conducting polymer with the architecture of interpenetrating polymer networks (IPN) from a robotic perspective. Thanks to the pseudo-trilayer compact structure, this conducting polymer is promising for applications of great interest in soft sensors and soft actuators at the macro- or microscale.
The behaviors and performances of the conducting polymer transducer as a bending actuator and as a bending sensor are investigated, respectively. Two working modes, namely actuation and sensing, are identified through modeling and experimental validations. With the experiments and validations, the performances of the slender-shape bending polymer actuator, including the free displacement and the output force, are characterized based on the mechanical model. Mathematical derivation and measurements are employed to identify the parameters. The results show the tip displacements and the forces of the bending actuators versus time, position, and given voltage. The electrical responses of the bending polymer sensors in different bending conditions are measured. Analytical functions of electrical responses are derived in order to identify the sensing outputs and the parameters of the model. The capability of the electrical model to predict the output voltage of the bending sensor versus time in a good agreement with experiments is shown in the study. Besides, the sensing signal drops over time, especially in quasi-static mechanical deformation. It is characterized based on modeling, and a compensation method is proposed to balance the decreasing voltage of the polymer sensor.
To demonstrate both the functions to sense and actuate achieved within the same material, a soft gripper made of two polymer fingers (one active and one passive) is presented in this thesis. This is a promising first step towards more complex 3D structures. The gripping force of the active finger can be set with the driving input and estimated by the proposed mechanical model. The passive finger of the gripper outputs a sensing signal when grasping an object, which is useful to monitor the contact with the object and to control the gripper in closed-loop. A sphere was successfully lifted in the experiment, and we were able to detect the gripping phase and the time when the contact was broken. The gripping force was monitored with the corresponding sensing output by the linear model of the polymer sensor.
摘要 I
Abstract II
Acknowledgements IV
Table of contents VI
Table of figures IX
Chapter 1. Introduction and overview 1
1.1. Overview 1
1.2. Applications in micro-world 2
1.3. Motivation and problem statement 11
1.4. Thesis outline 13
Chapter 2. Physico-chemical material background about C-IPN 15
2.1. Introduction 15
2.2. Conducting polymers for robotics 16
2.2.1. Active polymers as intelligent materials 16
2.2.2. Conducting interpenetrated polymer network 31
2.3. Fabrication process 32
2.4. Actuation and sensing behaviors of the trilayer transducer 34
2.5. Summary 35
Chapter 3. Electromechanical characterization of polymer actuators and sensors 36
3.1. Introduction 36
3.2. Mechanical properties and modeling 37
3.2.1. Modeling 37
3.2.2. Experimental test 47
3.3. Electrical properties and modeling 49
3.3.1. Equivalent circuit of polymer 49
3.3.2. Impedance model of the actuator 50
3.3.3. Electrical response of the sensor 52
3.3.4. Leakage identification and compensation 57
3.4. Summary 61
Chapter 4. Measurements and validations of actuation and sensing 62
4.1. Introduction 62
4.2. Development of electronics for experiments 62
4.3. Video tracking for deflection of polymers 69
4.3.1. Measuring setup 69
4.3.2. Image processing method 71
4.4. Performance measurements of C-IPN actuators 74
4.4.1. Experimental setup 74
4.4.2. Tip displacement 75
4.4.3. Force measurement 83
4.5. Performance measurements of C-IPN sensors 88
4.5.1. Experimental setup 88
4.5.2. Response to an impulse disturbance 91
4.5.3. Response to a step disturbance 101
4.5.4. Response to a periodic disturbance 107
4.6. Case study: a soft gripper system 113
4.6.1. Grasping experiment 113
4.6.2. Model coupling and gripping force detection 119
4.7. Summary 129
Chapter 5. Perspectives in micro robotics 131
5.1. Introduction 131
5.2. Conducting polymer in microfabrication 132
5.3. Measurement of performance in micropolymers 136
5.3.1. Tip displacement and the force of the microactuator 136
5.3.2. Output signal of the microsensor 140
5.4. Demonstrations of the microgripper using the brain-computer interface 146
5.4.1. Experimental setup 146
5.4.2. Brainwave processing method 149
5.4.3. Grasping experiments 152
5.5. Summary 157
Chapter 6. Conclusion and outlook 158
Publications 164
Appendix A. Analytical expression of elastic cantilevers for mechanical modeling 165
Appendix B. Recognition of attentiveness level using Hilbert-Huang transform and support machine learning 169
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