人體動作預測及雙軸機械手臂軌跡追蹤｜國立清華大學博碩士論文庫

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研究生：	黃柏霖 Huang, Po-Lin
論文名稱：	人體動作預測及雙軸機械手臂軌跡追蹤 Prediction of Human Arm Motions and Path Tracking for a Dual-Axial Robotic Device
指導教授：	李昌駿 Lee, Chang-Chun 鄭宏銘 Cheng, Hung-Ming
口試委員:	林顯易陳正義
學位類別：	碩士 Master
系所名稱：	工學院 - 動力機械工程學系 Department of Power Mechanical Engineering
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	123
中文關鍵詞：	中風上肢輔具、卷積神經網路、動作識別、軌跡追蹤控制
外文關鍵詞：	Robotic Assistive Arm, Motion Recognition, Trajectory Tracking Control
相關次數：	點閱：2 下載：0
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中風在台灣一直是一個不可忽視的疾病，其發病以及致死率一直居高不下，中風病患需要接受適當的治療以及漫長的復健之路才能回復，然而，多數的病患因為肌肉的無力以及復健帶來的疼痛而中途放棄，現行的復健器材無法有效的輔助病患出力做出特定的復健動作，因此，復健之路通常以失敗告終。這篇論文意在研究、發展出一套低成本、高效率的機械系統能夠使病患更加輕易且省力的做出指定動作。如此，將能提升病患的復健意願。
人工智慧在醫療產業的應用日新月異，在此研究中，運用Kinect感測器來感測人體動作，再以卷積神經網路進行高準確率的人體動作識別並將結果輸出至一個雙自由度的機械手臂上，並使用線性二次調節控制器控制，使其能夠進行雙軸的軌跡追蹤以此達成研究目的。

Stroke is one of the most devastating medical emergencies in Taiwan, which can cause neurological disorder and permanent impairment. Stroke patients need to receive proper treatment in time and rehabilitate to recover from their illness. However, many patients give up their treatment due to the painful process, heavy financial burden, and countless visits to clinic or medical centers. The long-term goal of this study aims to design an upper limb assistive robotic system to assist stroke patients for their rehabilitation and daily activities. In this thesis, the motion prediction models using deep learning algorithm and controller synthesis of multi-axial robotic device are focused.
To develop the models of motion prediction, the algorithm of Convolutional Neural Networks (CNNs) has been adopted. The experimental results indicate that the type of a motion can be predicted after 33% of a motion cycle being completed. The trajectories of involved joints can then be synthesized as tracking references of assistive robotic devices. To emulate the derived trajectories on a physical robotic device, adequate controller needs to be designed. A Linear Quadratic Regulator (LQR) controller and a controller based on pole-placement were adopted on a two degrees-of-freedom (2-DOF’s) robotic arm in this research. As a result, CNN can provide an excellent accuracy of motion prediction. The developed controller can also precisely compensate for the tracking performance of the selected motions.

摘要    I
ABSTRACT    II
TABLE OF CONTENTS    IV
LIST OF TABLES    VI
LIST OF FIGURES    VII
INTRODUCTION    1
1    RESEARCH BACKGROUND    1
2    MOTIVATION    2
3    PROBLEM STATEMENT    5
4    RESEARCH OBJECTIVES AND ORGANIZATION OF THESIS    7
LITERATURE REVIEW    9
1 INTRODUCTION    9
2 ROBOTIC ASSISTANCE OF STROKE REHABILITATION    10
3 ACQUISITION OF HUMAN MOTIONS    14
3.1 Application of Human Motions    15
3.2 Recognition of Human Motions for Medical Purposes    16
4 APPLICATIONS OF ROBOTIC DEVICES    19
4.1 Medical Applications – Robotic Assistive Devices    21
4.2 Medical Applications – Wearable Assistive Devices    23
4.3 Other Applications of Wearable Robotic Devices    26
5 MEDICAL APPLICATIONS WITH ARTIFICIAL INTELLIGENCE    28
5.1 Artificial Intelligence in Healthcare    28
5.2 Machine Perception and Machine Learning    30
MOTION ACQUISITION OF ARM TRAJECTORIES    32
1 EXPERIMENTAL SETUP    32
1.1 Setup of Acquisition Environment    33
1.2 Data Acquisition with Depth Camera    34
1.3 Data Acquisition with Regular Camcorder    37
2 RECRUITMENT OF TESTING SUBJECTS    38
2.1 Adopted Motions    39
2.2 Consideration of Different Body Conditions    41
DERIVATION OF MOTION MODEL    43
1 TRAJECTORY TRACKING RESULTS OF MOTIONS    43
1.1 Preprocessing of Recorded Trajectories    44
1.2 Phase Portraits of Recorded Motions    46
2 CONVOLUTIONAL NEURAL NETWORK    48
2.1 CNN Structure and Adjusted Algorithms    49
2.2 RESULT OF CNN MODEL    56
3 DETECTION OF MOTION IN REAL TIME    62
3.1 Motion Identification with Partial Trajectories    62
MODELING AND CONTROLLER SYNTHESIS OF EXPERIMENTAL PLATFORM    64
1 MECHANICAL HARDWARE AND ELECTRICAL DEVICES    64
2 MODELING OF ROBOTIC DEVICE    68
3 CONTROLLER DESIGN    71
3.1 Ackermann’s Formula    71
3.2 Linear Quadratic Regulator (LQR)    72
SIMULATION AND EXPERIMENTAL RESULTS    75
1 TRACKING RESULTS OF DUAL AXIAL MOVEMENT WITH CONTINUOUS METHOD    75
1.1 Simulation of Controlled System with Continuous LQR Method and Ackermann’s Formula    75
1.2 Issues of Controller Implementation Using Microcontroller    82
1.3 Nonlinear Conditions of the Experimental Platform    84
2 EXPERIMENTAL RESULTS OF DUAL-AXIAL MOVEMENTS WITH DISCRETE METHODS    85
2.1 Simulation of Discrete LQR and Ackermann’s Control    85
2.2 Experimental Results of Discrete Time Controller Using Ackermann’s Formula    89
2.2 Experimental Results of Discrete Time LQR Controller    98
CONCLUSION    111
1 SUMMARY OF THE RESEARCH    111
2 FUTURE WORKS    114
REFERENCES    116


                                

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