WEKO3
アイテム
リハビリ用ロボットハンドシステムでの利用を想定した実時間処理可能な圧力センサ用自己校正アルゴリズムに関する研究
https://doi.org/10.18997/00007221
https://doi.org/10.18997/00007221cdb6db10-a3b5-410a-94d8-745b1b81ebde
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
sei_k_342.pdf (7.4 MB)
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| アイテムタイプ | 学位論文 = Thesis or Dissertation(1) | |||||||
|---|---|---|---|---|---|---|---|---|
| 公開日 | 2019-06-14 | |||||||
| 資源タイプ | ||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_db06 | |||||||
| 資源タイプ | doctoral thesis | |||||||
| タイトル | ||||||||
| タイトル | Real Time Self-Calibration Algorithm of Pressure Sensor for Robotic Hand Glove System | |||||||
| 言語 | en | |||||||
| タイトル | ||||||||
| タイトル | リハビリ用ロボットハンドシステムでの利用を想定した実時間処理可能な圧力センサ用自己校正アルゴリズムに関する研究 | |||||||
| 言語 | ja | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 著者 |
Ahmed M M Almassri
× Ahmed M M Almassri
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| 抄録 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | This study investigates the use of a novel Proposed Self-Calibration Algorithm （PSCA） of a multi pressure sensors in real time on robotic hand glove system. The PSCA should be able to fix major problems in the pressure sensor such as hysteresis, variation in gain and lack of linearity with high accuracy. The traditional calibration process for this kind of sensor is a time-consuming task because it is usually done through manual and repetitive identi?cation [1, 2]. Furthermore, a traditional computational method is inadequate for solving the problem since it is extremely difficult to resolve the mathematical formula among multiple confounding pressure variables [1, 3, 4]. Accordingly, this study proposed a new method to predicting self-calibration in a pressure sensor using Levenberg Marquardt Back Propagation Artificial Neural Network （LMBP-ANN） model. The proposed method was achieved using a collected data set of pressure sensor in real time. The collected measurements were shown to lead to lack of linearity and fluctuation in output pressure sensor over time which should be compensated. The proposed method was validated by comparing the output force of the trained network with the experimental target force （reference）. This work shows that the Proposed model exhibited a remarkable performance than traditional methods with a max MSE of 0.17325 and R value over 0.99 for the total response of training, testing and validation. The model was tested using an untrained input data set in order to verify the Proposed model’s capability for implementing a self-calibration algorithm. We find that, the Proposed LMBP-ANN model for self-calibration purposes is able to successfully predict the desired pressure over time, even the uncertain behaviour of the pressure sensors due to its material creep. Developing an intelligent wearable robotic hand glove system based on PSCA with self-calibration feature of grasping mechanism is implemented. Then grasping sampled objects （plastic bottle and sponge ball） with different weights based on the developed robotic hand glove system were successfully performed. The results proved that the PSCA has the ability to successfully and accurately estimate the desired grasping forces in real time even the decrease and fluctuation of the forces pattern in sensor response. Afterwards, the PSCA was implemented and tested in real time based on MCU and software （MATLAB）. For validity and performance purpose of PSCA, the MSE and MAPE are calculated of 0.30 and 1.21% base on MCU and 0.08 and 0.6 base on MATLAB respectively. Overall, the PSCA presented here ensure that the problems of hysteresis, variation in gain and lack of linearity over time have overcome. Furthermore, we have obtained accurate measurements of grasping mechanism. This provides a useful methodology for the user to evaluate the performance of any measurement system in a real-time environment. | |||||||
| 目次 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 1 Introduction|2 Literature Review|3 Research Methodology|4 Results and Discussion for Calibration and Data Collection|5 Results and Discussion for Proposed Self-Calibration Algorithm|6 Results and Discussion for Developing Robotic Hand Glove System|7 Conclusion and Recommendations | |||||||
| 備考 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 九州工業大学博士学位論文 学位記番号：生工博甲第342号 学位授与年月日：平成31年3月25日 | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | self-calibration algorithm | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | pressure sensors | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | real time measurement system | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | artificial neural network | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | robotic hand glove | |||||||
| キーワード | ||||||||
| 主題Scheme | Other | |||||||
| 主題 | rehabilitation application | |||||||
| アドバイザー | ||||||||
| 和田, 親宗 | ||||||||
| 学位授与番号 | ||||||||
| 学位授与番号 | 甲第342号 | |||||||
| 学位名 | ||||||||
| 学位名 | 博士（工学） | |||||||
| 学位授与年月日 | ||||||||
| 学位授与年月日 | 2019-03-25 | |||||||
| 学位授与機関 | ||||||||
| 学位授与機関識別子Scheme | kakenhi | |||||||
| 学位授与機関識別子 | 17104 | |||||||
| 学位授与機関名 | 九州工業大学 | |||||||
| 学位授与年度 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | 平成30年度 | |||||||
| 出版タイプ | ||||||||
| 出版タイプ | VoR | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||
| アクセス権 | ||||||||
| アクセス権 | open access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||
| ID登録 | ||||||||
| ID登録 | 10.18997/00007221 | |||||||
| ID登録タイプ | JaLC | |||||||
