基於多通道 RSS 的可見光定位系統之設計與其訊號處理方法

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姓名

洪子翔(Tzu-Hsiang Hung) 查詢紙本館藏

畢業系所

機械工程學系

論文名稱

基於多通道 RSS 的可見光定位系統之設計與其訊號處理方法
(Design of Visible Light Positioning System Based on Multi-Channel RSS and its Signal Processing Approach)

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至系統瀏覽論文 (2024-12-31以後開放)

摘要(中)

隨著傳統製造業面臨產品多樣化和生產需求迅速變化的挑戰，生產線的靈活性需求日益增加。全自動化設備雖能提升效率，但在少量生產情況下，其高昂成本使投資回報難以令人滿意。同時，人力成本逐年上升，高度適應性的人力難以應對產線需求。因此，企業轉而部署具適應性且能執行複雜裝配任務的機器手臂。然而，頻繁的產線更換要求機器手臂每次更動後需重新校正，這過程既耗時又繁瑣。使用教導器進行再定位也是一個耗時的過程，且校正點位越多，所需時間越長。此外，這一過程需要經驗豐富且技術熟練的人力配合，否則難以保證校正精度，從而影響產品品質。
本研究提出一種新的方法，結合可見光定位和工業機器手臂，通過利用可見光的特性及接收訊號強度(Received Signal Strength, RSS)環境指紋識別技術，搭配神經網路模型來實現對機器手臂的再定位。該方法基於多輸入多輸出系統(Multiple Input Multiple Output, MIMO)，並透過RSS環境指紋識別技術獲取精確的位置信息。在環境指紋識別技術中，應用了相應的訊號處理和資料處理方法，以準備模型訓練所需的數據。這些經過處理後的數據輸入神經網路、柯爾莫哥洛夫-阿諾爾德網路與隨機森林回歸等多種模型進行訓練和評估，以分析這些模型在不同條件下的特性和表現，從而提高定位的準確性和穩定性。研究結果表明，本系統的平均精度誤差低於5 mm。
在數據處理過程中，以濾波器預先濾除環境光與其他雜訊，並對接收的訊號強度進行分析，使用訊號雜訊比(Signal-to-noise ratio, SNR)進行特徵篩選，盡可能減少在資料特徵方面產生的SNR誤差，提高模型的擬合度和數據的預測能力。透過本研究的SNR特徵篩選方法，整體精度提升了55.65%。而這些技術的結合宗旨在減少機器手臂校正所需的時間和人力，提高生產線的靈活性和效率。

摘要(英)

As traditional manufacturing industries face the challenges of product diversification and rapidly changing production demands, the need for flexibility in production lines is increasingly growing. Although fully automated equipment can enhance efficiency, its high costs make the return on investment unsatisfactory in low-volume production scenarios. Additionally, with rising labor costs, highly adaptable human labor is becoming insufficient to meet the demands of production lines. Consequently, companies are turning to deploy adaptable robotic arms capable of performing complex assembly tasks. However, frequent production line changes require the robotic arms to be recalibrated each time they are repositioned, a process that is both time-consuming and tedious. Using a teaching pendant for repositioning is also a time-intensive process, and the more calibration points required, the longer the time needed. Moreover, this process requires experienced and skilled personnel to ensure calibration accuracy; otherwise, product quality may be compromised.
This study proposes a novel method that combines visible light positioning with industrial robotic arms. By utilizing the characteristics of visible light and the Received Signal Strength (RSS) fingerprinting technique, along with neural network models, this method aims to achieve the repositioning of robotic arms. The method is based on a Multiple Input Multiple Output (MIMO) system and obtains precise location information through the RSS fingerprinting technique. In the fingerprinting process, appropriate signal processing and data processing methods are applied to prepare the data needed for model training. The processed data is then input into various models, including Neural Networks, Kolmogorov-Arnold Networks, and Random Forest Regressor, for training and evaluation, analyzing their characteristics and performance under different conditions to improve positioning accuracy and stability. The research results show that the system achieves an average accuracy error of less than 5 mm.
During the data processing, filters are used to preemptively eliminate ambient light and other noise, and the received signal strength is analyzed using the Signal-to-Noise Ratio (SNR) for feature selection, minimizing SNR errors in the data features and improving the model′s fit and predictive capabilities. Through the SNR feature selection method proposed in this study, overall accuracy was improved by 55.65%. The integration of these techniques aims to reduce the time and manpower required for robotic arm calibration, thereby enhancing the flexibility and efficiency of production lines.

關鍵字(中)

★ 可見光定位
★ 接收訊號強度指紋識別
★ 機器學習
★ 機器手臂校正
★ 訊號處理

關鍵字(英)

★ Visible Light Positioning
★ Received Signal Strength Fingerprints
★ Machine Learning
★ Robotic Arm Calibration
★ Signal Processing

論文目次

AI 工具應用聲明 i
摘要 ii
Abstract iii
致謝 v
目錄 vi
圖目錄 ix
表目錄 xi
第一章緒論 1
1-1研究背景 1
1-2研究動機 1
1-3文獻探討 2
1-3-1再定位技術之應用現況 2
1-3-2可見光定位技術 2
1-4研究目的 4
1-5論文架構 4
第二章相關技術 6
2-1再定位技術 6
2-1-1 Wi-Fi室內定位 6
2-1-2視覺定位 7
2-1-3光定位技術 7
2-1-4基於多輸入多輸出光定位技術 10
2-2環境指紋抽樣方法 11
2-2-1網格採樣法 12
2-2-2拉丁超立方抽樣法 12
2-2-3均勻設計 13
2-3機器學習技術 13
2-3-1集成式學習-隨機森林 14
2-3-2多層感知器 14
2-3-3柯爾莫哥洛夫-阿諾爾德網路 15
第三章研究方法與實驗設計 17
3-1研究架構 17
3-2架構說明 18
3-3成效評估 19
第四章實驗設計 21
4-1環境配置 21
4-1-1實驗設備 22
4-1-2接收端-治具設計 23
4-1-3多輸入多輸出系統 24
4-1-4發射頻率選用與設計 25
4-1-5視場角分布分析 27
4-2環境指紋收集 29
4-2-1採樣空間定義 29
4-2-2環境指紋採樣方法 30
4-2-3原始資料格式 33
4-3資料前處理 34
4-3-1高通濾波參數配置 35
4-3-2快速傅立葉轉換 36
4-3-3特徵篩選評估 39
4-4模型評估 45
第五章結果與討論 46
5-1基於單通道接收訊號在不同高度下分布 46
5-2基於SNR特徵篩選的特結果比較 52
5-3數據點數量於Z軸分析 56
5-4使用多種採樣法於不同模型之分析結果 59
第六章結論與未來展望 66
6-1結論與貢獻 66
6-2限制與範圍 67
6-3未來與展望 68
參考文獻 69

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指導教授

林錦德(Chin-Te Lin)

審核日期

2024-8-20

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