垃圾貯坑內之垃圾高度圖建置及其模擬環境建構

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：97

、訪客IP：18.116.62.106

姓名

莊晨馨(Chen-Hsin Chuang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

垃圾貯坑內之垃圾高度圖建置及其模擬環境建構

相關論文

★ 直接甲醇燃料電池混合供電系統之控制研究	★ 利用折射率檢測法在水耕植物之水質檢測研究
★ DSP主控之模型車自動導控系統	★ 旋轉式倒單擺動作控制之再設計
★ 高速公路上下匝道燈號之模糊控制決策	★ 模糊集合之模糊度探討
★ 雙質量彈簧連結系統運動控制性能之再改良	★ 桌上曲棍球之影像視覺系統
★ 桌上曲棍球之機器人攻防控制	★ 模型直昇機姿態控制
★ 模糊控制系統的穩定性分析及設計	★ 門禁監控即時辨識系統
★ 桌上曲棍球：人與機械手對打	★ 麻將牌辨識系統
★ 相關誤差神經網路之應用於輻射量測植被和土壤含水量	★ 三節式機器人之站立控制

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

我們每個人每天或多或少會製造出一些垃圾，而在台灣，垃圾通常透過垃圾車運送至垃圾焚化廠進行處理。垃圾在焚化廠內處理的第一步為將垃圾貯坑內的垃圾，經由天車控制人員操控吊車，夾取貯坑內的垃圾至垃圾進料斗來進行後續的垃圾處理。本論文為減輕天車操控人員的負擔，開發出一套垃圾貯坑的處理系統，使用二維光達掃描貯坑內垃圾並輸出三維點雲，再根據垃圾的三維點雲決定垃圾夾取的位置與順序，夾取後再重新掃描夾取周圍的垃圾深度，建立新垃圾的三維點雲，再重複夾取過程，一直循環下去，直到垃圾量夠少才停止。以上用以大量取代操控人員的工作，使得垃圾處理更即時、更自動、更有效率。另外，還可透過垃圾的三維點雲建模，將垃圾山模型重新置入模擬環境當中，作為天車操控人員垃圾輪廓的參考。
本論文的研究分為四大部分，第一部分為使用Blender軟體製作垃圾模型放入Gazebo模擬環境，並在Gazebo模擬環境進行天車的模擬，將模擬之二維光達安裝在模擬環境中天車的側面，透過程式控制天車的移動，來對整個垃圾貯坑進行垃圾深度的掃描，並在最後進行整個垃圾貯坑的深度圖呈現。第二部分會針對掃描完整個貯坑後的點雲進行後處理，過濾掉不需要的深度資料點，對剩餘的深度資料點進行接續的夾取點判斷，並規畫出一套夾取順序的流程。當垃圾經由多次被夾取後，進行被夾取範圍內深度資料的更新，最終目的為將原先放置於Gazebo模擬環境內貯坑的垃圾，盡可能的去除。第三部份針對點雲建模的部分，將光達掃描的原始掃描資料點，進行補中值點的動作以增加點雲稠密性，接著將填補完中間值的點雲，透過CloudCompare軟體來建模出一個三維立體模型。第四部分為設計一個真實的小型垃圾貯坑場域，安裝二維光達在由馬達控制的移動平台上，透過實際的模擬，確認演算法是否得以正常的運行並呈現出正確的深度資料投影點。如此的模擬，為希望當二維光達被安裝於真正的垃圾天車上時，設計出來的演算法能夠發揮其功能與效率。

摘要(英)

Each of us generates some amount of waste every day. In Taiwan, garbage is usually transported by garbage trucks to incineration plants for processing. The first step in the incineration process involves using a crane controlled by operators to grab the garbage from the refuse bunker and transfer it to the refuse charging hopper for further processing. In this thesis, a refuse bunker processing system is developed to alleviate the burden on the crane operators. It utilizes a 2D lidar to scan the garbage in the refuse bunker and export a 3D point cloud. Based on the 3D point cloud, the system determines the position and order of the garbage-grabbing points. After grabbing, the system rescans the depth of the surrounding grabbing points to create a renewed 3D point cloud. This process is repeated until the garbage is reduced to a low level. The system aims to replace manual labor with automation, making garbage processing more real-time and efficient. By modeling the garbage from the 3D point cloud, the model will be put into the simulation environment, serving as a reference for crane operators in determining the contour of the overall garbage.
The research in this thesis consists of four main parts. The first part involves creating garbage models using Blender and placing them into the Gazebo simulation environment. The simulation of the crane is conducted, and a 2D lidar is installed on the side of the crane. The crane′s movement is controlled by the program to scan the depth of the entire refuse bunker and a depth map of the entire refuse bunker is generated. In the second part, post-processing is performed on the scanned point cloud of the entire refuse bunker. Unnecessary depth data points are filtered, and the remaining depth data points are used to determine the grabbing points. As the garbage is grabbed multiple times, the depth data within the grabbing range will then be updated. The ultimate goal is to remove as much garbage as possible from the refuse bunker. The third part focuses on point cloud reconstruction. The original scanned point cloud is processed to fill in missing values and increase the density of the point cloud. The filled-in point cloud is then used to create a 3D model using CloudCompare. The fourth part involves designing a real small-scale refuse bunker. A 2D lidar is mounted on a mobile platform controlled by the motor. The algorithm′s functionality is verified through actual simulations in projecting correct depth points. These simulations aim to ensure that the designed algorithm can perform effectively and efficiently when the 2D lidar is installed on an actual garbage crane.

關鍵字(中)

★ Blender
★ 二維光達
★ Gazebo模擬環境
★ 點雲處理
★ 點雲稠密性
★ 馬達控制

關鍵字(英)

★ Blender
★ 2D-LiDAR
★ Gazebo simulation environment
★ Point cloud processing
★ Point cloud density
★ Motor control

論文目次

摘要 i
ABSTRACT ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 ix
第一章緒論 1
1.1 研究動機與背景 1
1.2 文獻回顧 1
1.3 論文目標 4
1.4 論文架構 4
第二章系統架構及軟硬體介紹 5
2.1 系統流程圖 5
2.2 硬體介紹 6
2.2.1 筆記型電腦規格介紹 6
2.2.2 光達規格介紹 6
2.2.3 步進馬達與驅動板規格介紹 7
2.2.4 直流馬達規格介紹 8
2.2.5 直流馬達的訊號轉接器[31]、電源集線板[32]與電源線[31]規格介紹 9
2.3 軟體介紹 10
2.3.1 虛擬平台介紹 10
2.3.2 光達軟體介紹 11
第三章模擬環境Gazebo 12
3.1 模擬環境Gazebo 12
3.1.1 Rostopic [34] 12
3.1.2 光達的規格設定 13
3.1.3 光達掃描完的深度點之投影位置校正 17
3.2 垃圾模型 18
3.2.1 垃圾模型的製作 18
3.2.2 垃圾模型放入貯坑 20
3.3 首次的垃圾高度掃描 21
3.3.1 大車的控制 21
3.4 垃圾夾取點的選擇 22
3.4.1 聚類分群法 23
3.4.2 尋找最高點法 27
3.4.3 影像輪廓法 27
3.5 被夾取區域附近的深度更新 28
3.6 點雲的中間值填補 30
3.6.1 兩台光達 30
3.6.2 X方向上的補值 31
3.6.3 Y方向上的補值 34
3.7 點雲建模 39
3.7.1 Alpha shape [18] 39
3.7.2 Ball pivot [21] 41
3.7.3 CloudCompare點雲建模 43
3.8 結合影像的深度圖 45
第四章小型場域設計 48
4.1 小場域的設計 48
4.1.1 小場域的尺度規格設計 48
4.1.2 結構材質的選擇 49
4.1.3 小場域馬達，外牆與移動平台結構的安裝 49
4.1.4 LiDAR網路連接與線路架設 50
第五章小場域環境的實驗結果 51
5.1 實驗誤差 51
5.1.1 光達誤差 51
5.1.2 馬達設定與誤差 51
5.2 垃圾物件 54
5.2.1 透明或半透明物件的掃描 54
5.2.2 其他物件 55
5.3 整個小場域的深度掃描投影與夾取流程實驗 57
第六章結論與未來展望 67
6.1 結論 67
6.2 未來展望 67
參考文獻 69

參考文獻

[1] "中華民國內政部戶政司 - 人口統計資料," [Online]. Available: https://www.ris.gov.tw/app/portal/346. [Accessed: June, 2023].
[2] "行政院環保署署 - 全國一般廢棄物產生量," [Online]. Available: https://data.epa.gov.tw/dataset/detail/STAT_P_126. [Accessed: June, 2023].
[3] "橋式起重機 - 維基百科," [Online]. Available: https://reurl.cc/8joZDR. [Accessed: June, 2023].
[4] "臺灣垃圾焚化廠列表 - 維基百科," [Online]. Available: https://reurl.cc/o71XND. [Accessed: June, 2023].
[5] L. He, C. Chen, T. Zhang, H. Zhu and S. Wan, "Wearable depth camera: Monocular depth estimation via sparse optimization under weak supervision," IEEE Access, vol. 6, pp. 41337-41345, Jul. 2018.
[6] J.-H. Lee, M. Heo, K.-R. Kim and C.-S. Kim, "Single-image depth estimation based on Fourier domain analysis," IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA , Jun. 2018, pp. 330-339.
[7] L. Ding and G. Sharma, "Fusing structure from motion and lidar for dense accurate depth map estimation," IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP), New Orleans, LA, USA, Mar. 2017, pp. 1283-1287.
[8] J. Lambert, A. Carballo, A. M. Cano, P. Narksri, D. Wong, E. Takeuchi and K. Takeda "Performance analysis of 10 models of 3D LiDARs for automated driving," IEEE Access, vol. 8, pp. 131699-131722, Jul. 2020.
[9] J. P. Queralta, F. Yuhong, L. Salomaa, L. Qingqing, T. N. Gia, Z. Zou, H. Tenhunen and T. Westerlund, "FPGA-based architecture for a low-cost 3D LiDAR design and implementation from multiple rotating 2D lidars with ROS," IEEE SENSORS, Montreal, QC, Canada, Oct. 2019, pp. 1-4.
[10] R. Yagfarov, M. Ivanou and I. Afanasyev, "Map comparison of lidar-based 2D SLAM algorithms using precise ground truth," The 15th International Conference on Control, Automation, Robotics and Vision(ICARCV), Singapore, Nov. 2018, pp. 1979-1983.
[11] B.-h. Kang and S.-i. Choi, "Pothole detection system using 2D lidar and camera," The Ninth International Conference on Ubiquitous and Future Networks(ICUFN), Milan, Italy, Jul. 2017, pp. 744-746.
[12] M. Ester, H.-P. Kriegel, J. Sander and X. Xu, "A density based algorithm for discovering clusters in large spatial databases with noise", KDD-96 Proceedings, pp. 226-231, 1996.
[13] K. A. Nazeer and M. Sebastian, "Improving the accuracy and efficiency of the k-means clustering algorithm", Proceedings of the World Congress on Engineering, vol. 1, pp. 1-3, Jul. 2009.
[14] X. Lan, Q. Li and Y. Zheng, "Density k-means: A new algorithm for centers initialization for k-means," 6th IEEE International Conference on Software Engineering and Service Science(ICSESS), Beijing, China, Sep. 2015, pp. 958-961.
[15] L. He, Y. Chen and J. Zhao, "Automatic docking recognition and location algorithm of port oil loading arm based on 3D laser point cloud," IEEE International Conference on Mechatronics and Automation(ICMA), Beijing, China, 2020, pp. 615-620.
[16] N. Charron, S. Phillips and S. L. Waslander, "De-noising of lidar point clouds corrupted by snowfall," 15th Conference on Computer and Robot Vision(CRV), Toronto, ON, Canada, May 2018, pp. 254-261.
[17] I. Ashraf, S. Hur and Y. Park, "An investigation of interpolation techniques to generate 2D intensity image from lidar data," IEEE Access, vol. 5, pp. 8250-8260, Apr. 2017.
[18] H. Edelsbrunner, D. Kirkpatrick and R. Seidel, "On the shape of a set of points in the plane," IEEE Transactions on Information Theory, vol. 29, no. 4, pp. 551-559, Jul. 1983.
[19] R. C. dos Santos, M. Galo and A. C. Carrilho, "Extraction of building roof boundaries from lidar data using an adaptive alpha-shape algorithm," IEEE Geoscience and Remote Sensing Letters, vol. 16, no. 8, pp. 1289-1293, Aug. 2019.
[20] Z. Liu, H. Liu, Z. Lu and Q. Zeng, "A dynamic fusion pathfinding algorithm using Delaunay triangulation and improved A-star for mobile robots," IEEE Access, vol. 9, pp. 20602-20621, Jan. 2021.
[21] F. Bernardini, J. Mittleman, H. Rushmeier, C. Silva and G. Taubin, "The ball-pivoting algorithm for surface reconstruction," IEEE Transactions on Visualization and Computer Graphics, vol. 5, no. 4, pp. 349-359, Oct.-Dec. 1999.
[22] S. -Y. Chen, S. -F. Chang and C. -W. Yang, "Generate 3d triangular meshes from spliced point clouds with Cloudcompare," IEEE 3rd Eurasia Conference on IOT, Communication and Engineering(ECICE), Yunlin, Taiwan, Oct. 2021, pp. 72-76.
[23] Z. Yang and D. Li, "WasNet: A neural network-based garbage collection management system," IEEE Access, vol. 8, pp. 103984-103993, Jun. 2020.
[24] C. Shi, R. Xia and L. Wang, "A novel multi-branch channel expansion network for garbage image classification," IEEE Access, vol. 8, pp. 154436-154452, Aug. 2020.
[25] "Asus TUF Dash F15 筆記型電腦," [Online]. Available: https://s.yimg.com/zp/MerchandiseImages/C7FA101421-Gd-9911054.jpg. [Accessed: June, 2023].
[26] "SICK 2D-LiDAR感測器 - TiM571-2050101," [Online]. Available: https://www.sick.com/tw/zf/lidar/2d-lidar/tim/tim571-2050101/p/p412444. [Accessed: June, 2023].
[27] "Arduino Mega 2560 Rev3微控制器開發板," [Online]. Available: https://reurl.cc/lDoW6v. [Accessed: June, 2023].
[28] "L298N模組," [Online]. Available: https://reurl.cc/M80QrK. [Accessed: June, 2023].
[29] "FLASHFORGE Stepper Motor 17HD4063-05N," [Online]. Available: https://reurl.cc/GAx6ly. [Accessed: June, 2023].
[30] "ROBOTIS e-Manual RX-64馬達," [Online]. Available: https://emanual.robotis.com/docs/en/dxl/rx/rx-64/. [Accessed: June, 2023].
[31] "Dynamixel Starter Set馬達入門套件包," [Online]. Available: https://reurl.cc/Eor5G1. [Accessed: June, 2023].
[32] "U2D2 Power Hub - ROBOTIS e-Manual," [Online]. Available: https://emanual.robotis.com/docs/en/parts/interface/u2d2_power_hub/. [Accessed: June, 2023].
[33] "SOPAS Engineering Tool," [Online]. Available: https://www.sick.com/tw/zf/sopas-engineering-tool/p/p367244. [Accessed: June, 2023].
[34] "Rostopic - ROS Wiki," [Online]. Available: http://wiki.ros.org/rostopic. [Accessed: June, 2023].
[35] "SICK 2D-LiDAR感測器 - LMS143-15100 Security Prime," [Online]. Available: https://www.sick.com/hk/zf/lidar/2d-lidar/lms1xx/lms143-15100-security-prime/p/p412845. [Accessed: June, 2023].
[36] T. Zhang, R. Ramakrishnan and M. Livny, "BIRCH: An efficient data clustering method for very large databases," ACM SIGMOD Record, vol. 25, pp. 103-114, Jun. 1996.
[37] "CloudCompare," [Online]. Available: https://www.cloudcompare.org/main.html. [Accessed: June, 2023].

指導教授

王文俊(Wen-June Wang)

審核日期

2023-7-26

推文