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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 林達德(Ta-Te Lin) | |
| dc.contributor.author | Chang-Chih Liu | en |
| dc.contributor.author | 劉昶志 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:42:05Z | - |
| dc.date.available | 2011-08-22 | |
| dc.date.copyright | 2011-08-22 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-15 | |
| dc.identifier.citation | 1. 李治緯。2009。適用於田間機器人之改良式同步定位與地圖建構演算法。碩士論文。臺北：國立臺灣大學生物產業機電工程學研究所。
2. 曾清涼、儲慶美。1999。GPS衛星量測原理與應用。二版。台南：國立成功大學衛星資訊研究中心。 3. Ahamed, T., T. Takigawa, M. Koike, T. Honma, A. Yoda, H. Hasegawa, P. Junyusen and Q. Zhang. 2004. Characterization of laser range finder for in-field navigation of autonomous tractor. Proceedings of Conference on Automation Technology for Off-Road Equipment. 120-130. 4. Arras, K. and S. Vestli. 1998. Hybrid, high-precision localisation for the mail distributing mobile robot system MOPS. Proceedings of the International Conference on Robotics and Automation. 3129-3143. 5. Bak, T. and H.Jakobsen. 2004. Agricultural robotic platform with four wheel steering for weed detection. Biosystems Engineering. 87(2): 125-136. 6. Barawid, J. O. C., A. Mizushima, K. Ishii and N. Noguchi. 2007. Development of an autonomous navigation system using a two-dimensional laser scanner in an orchard application. Journal of Biosystems Engineering. 96(2): 139-149. 7. Belforte, G., P. Gay, and D. Aimonino. 2007 robotics for improving quality, safety and productivity in intensive agriculture: challenges and opportunities. Industrial Robotics: Programming, Simulation and Applications. I-Tech. 677- 690. 8. Besl, P. and N. McKay. 1992. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence. 14(2): 239-256. 9. Billingsley, J., A. Visala and M. Dunn. 2007. Robotics in agriculture and forestry. Springer Handbooks of Robotics. 1065-1077. 10. Blasco, J., N. Aleixos, J. M. Roger, G. Rabatal, and E. Molto. 2002. Robotic weed control using machine vision. Journal of Biosystems Engineering. 83(2): 381-390. 11. Ceres, R., J. L. Pons, A. R. Jimenez, J. M. Martin and L. Calderon. 1998. Design and implementation of an aided fruit-harvesting robot (Agribot). Journal of Industrial Robot. 25(5): 337-346. 12. Chong, V. K., N. Kondo, K. Ninomiya, T. Nishi, M. Monta, K. Namba, and Q. Zhang. 2008. Feature extraction for eggplant fruit grading system using machine vision. Journal of Applied Engineering in Agriculture. 24(5): 675-684. 13. Cole, D. M. and P. M. Newman. 2006. Using laser range data for 3D SLAM in outdoor environments. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). 1556-1563. 14. Dunn, M., J. Billingsley and N. Finch. 2003. Machine vision classification of animals. Mechatronics and Machine Vision 2003: Future trends. 15. Dijkstra, E. W. 1959. A note on two problems in connection with graphs. Numerische Mathematik 1: 269–271. 16. Edan, Y. and G. Miles. 1994. Systems engineering of agricultural robot design Systems. IEEE Transactions on Man and Cybernetics. 24(8): 1259-1265. 17. Ferguson, D. and A. Stentz. 2006. Using interpolation to improve path planning: the field D* algorithm. Journal of Field Robotics. 79-101. 18. Fox, D.,W. Burgard and S. Thrun. 1998. Active markov localization for mobile robots. Journal of Robotics and Autonomous Systems. 25:195-207. 19. Fox, D., J. Hightower, L. Lin, D. Schulz and G. Boriello. 2003. Bayesian filters for location estimation. IEEE Pervasive Computing. 24-33. 20. Freire, E., T. Bastos-Filho, M. Sarcinelli-Filho and R. Carelli. 2004. A new mobile robot control approach via fusion of control signals. IEEE Transactions on Systems, Man and Cybernetics, Part B. 34(1): 419-429. 21. Gutmann, J. S. and C. Schlegel. 1996. AMOS: Comparison of scan matching approaches for self-localization in indoor environments. Proceedings of the 1st Euromicro Workshop on Advanced Mobile Robots. 61-67. 22. Hardt, H. J., D. Wolf and R. Hisson. 1996. The dead reckoning localization system of the wheeled mobile robot ROMANE. Proceeding of the International Conference on Multisensor Fusion and Integration for Intelligent Systems. 603-610. 23. Hart, P. E., N. J. Nilsson and B. Raphael. 1968. A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions on Systems Science and Cybernetics. SSC4 4 (2): 100–107. 24. Hough, P. V. C. 1958. Machine analysis of bubble chamber pictures. In International Conference on High Energy Accelerators and Instrumentation. 25. Imou, K., T. Okamoto, T. Torii, M. Ishida, H. Takanaga, A. Okado and N. Itokawa. 1998. Autonomous tractor for forage production. Proceedings of 13th International Congress on Agricultural Engineering. 3: 361-368. 26. Junya, T., T. Kiyoshi, T. Yutaka, I. Itashi, K. Masayoshi and T. Koji. 2003. 2-D localization of autonomous farm vehicle using laser distance sensor. Journal of the Japanese Society of Agricultural Machinery. 67(5): 45-53. 27. Kalman, R. E. 1960. A new approach to linear filtering and prediction problem. Trans. ASME: J. Basic Eng. 82(D): 32-45. 28. Kondo, N. and K. Ting. 1998. Robotics for plant production. Artificial Intelligence Review. 12(1): 227-243. 29. Kise, M., N. Noguchi, K. Ishii, H. Terao. 2001. Development of agricultural autonomous tractor with an RTK-GPS and a FOG. Proceedings of the Fourth IFAC Symposium on Intelligent Autonomous Vehicles. 99-104. 30. Kise, M., Q. Zhang, M. F. Rovira. 2005. A stereovision-based crop row detection method for tractor-automated guidance. Biosystems Engineering. 90(4): 357-367. 31. Koenig, S. and M. Likhachev. 2005. Fast replanning for navigation in unknown terrain. IEEE Transaction on Robotics and Automation, vol. 21, no. 3. 32. Leonard, J. and H. Durrant-Whyte. 1991. Mobile robot localization by tracking geometric beacons. Robotics and Automation. 7(3): 376-382. 33. Li, M., K. Imos, K. Wakabayashi and S. Yokoyama. 2009. Review of research on agricultural vehicle autonomous guidance. International Journal of Agricultural and Biological Engineering. 2(3): 1-16. 34. Lieb, D., A. Lookingbill and S. Thrun. 2005. Adaptive road following using self-supervised learning and reverse optical ow. Proceedings of Robotics: Science and Systems. 35. Lu, F. and E. Milios. 1997. Globally consistent range scan alignment for environment mapping. Autonomous Robots. 4(4): 333-349. 36. Marchant, J. A. and R. Brivot. Real-time tracking of plant rows using a Hough transform. Journal of Real-Time Imaging. 1(5): 363-71. 37. Maybeck, P. S. 1979. Stochastic models, estimation and control. Academic Press. 38. Martinez-Cantin, R., N. de Freitas and J. A. Castellanos. 2007. Analysis of particle methods for simultaneous robot localization and mapping and a new algorithm: Marginal-SLAM. Proceedings of the IEEE International Conference on Robotics and Automation. 2415 – 2420. 39. Miettinen, M., M. Ohman, A. Visala and P. Forsman. 2007. Simultaneous localization and mapping for forest harvesters. Proceedings of IEEE International Conference on Robotics and Automation. 517-522. 40. Montemerlo, M., S. Thrun, D. Koller and B. Wegbreit. 2002. FastSLAM: a factored solution to the simultaneous localization and mapping problem. Proceedings of the National Conference on Artificial Intelligence. 593-598. 41. Montemerlo, M., S. Thrun, D. Koller and B. Wegbreit. 2003. FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. Proceedings of the International Joint Conferences on Artificial Intelligence. 1151-1156. 42. Nagasaka, Y., H. Saito, K. Tamaki, M. Seki, K. Kobayashi and K. Taniwaki. 2009. An autonomous rice transplanter guided by global positioning system and inertial measurement unit. Journal of field robotics. 26(6-7): 537-548. 43. Nettleton, E.W., P.W. Gibbens and H.F. Durrant-Whyte. 2000. Closed form solutions to the multiple platform simultaneous localization and map building (SLAM) problem. Sensor Fusion: Architectures, Algorithms, and Applications IV: 4051. 44. Noguchi, N., M. Kise, K. Ishii and H. Terao. 2002. Field automation using robot tractor. Proceedings of Conference on Automation Technology for Off-Road Equipment. 239-245. 45. Ohman, M., M. Miettinen, K. Kannas, J. Jutila, A. Visala and P. Forsman. 2007. Tree measurement and simultaneous localization and mapping system for forest harvesters. Proceeding of International Conference on Field and Service Robotics Chamonix. 42: 369-378. 46. Ota, T., J. Bontsema, S. Hayashi, K. Kubota, E. j. Van Henten, E.A. Van Os and K. Ajiki. 2007. Development of a cucumber leaf picking device for greenhouse production. Journal of Biosystems Engineering. 98: 381-390. 47. Pavlath, G. A. 1994. Fiber-optic gyroscopes. Proceedings of Lasers and Electro-Optics Society Annual Meeting. 2: 237-238. 48. Russell, S.J. and P. Norvig. 2002. Artificial intelligence: a modern approach. 2nd ed., New Jersey: Prentice Hall. 49. Schiele, B. and J. L. Crowley. 1994. A comparison of position estimation techniques using occupancy grids. Proceedings of the IEEE International Conference on Robotics and Automation. 1628-1634. 50. Shannon, C. E. 1948. A mathematical theory of communication. The Bell Systems Technical Journal. 27: 379-423. 51. Smith, R., and P. Cheeseman. 1986. On the representation and estimation of Spatial Uncertainty. The International Journal of Robotics Research, Multi-media Archives. 5(4): 56-68. 52. Smith, R., M. Self and P. Cheeseman. 1990. Estimating uncertain spatial relationships in robotics. Autonomous Robot Vehicles. 1: 167-193. 53. Subramanian, V., T. F. Burks and A. A. Arroyo. 2006. Development of machine vision and laser radar based autonomous vehicle guidance systems for citrus grove navigation. Journal of Computers and Electronics in Agriculture. 53(2): 130-143. 54. Surmann, H., A. Nuchter and J. Hertzberg. 2003. An autonomous mobile robot with 3D range finder for 3D exploration and digitalization of indoor enrironment. Robotics and Autonomous System. 45: 181-198. 55. Stentz, A. 1994. Optimal and efficient path planning for partially-known environments. Proceedings of the IEEE International Conference on Robotics and Automation. 3310 - 3317. 56. Stentz, A. 1995. The focussed D* algorithm for real-rime replanning. In Proceedings of the International Joint Conference on Artificial Intelligence. 57. Tanigaki, K., T. Fujiura, A. Akase and J. Imagawa.2008. Cherry-harvesting robot. Computers and Electronics in Agriculture. 63: 65-72. 58. Thuilot, B., C. Cariou, P. Martinet, and M. Berducat. 2002. Automatic guidance of a farm tractor relying on a single CP-DGPS. Autonomous Robots. 13(1): 53-71. 59. Tofael, A., T. Tomohiro, K. Masayuki, H. Tsuyoshi, H. Hideo, Q. Zhang. 2006. Navigation Using a Laser Range Finder for Autonomous Tractor (Part 1) － Positioning of Implement. Journal of the Japanese Society of Agricultural Machinery. 68(1): 68-77. 60. Thrun, S., D. Fox and W. Burgard. 2000. Monte Carlo localization with mixture proposal Distribution. In Proceedings of the AAAI National Conference on Artificial Intelligence. 859-865. 61. Thrun, S., D. Fox, W. Burgard and F. Dellaert. 2001. Robust Monte Carlo localization for mobile robots. Artificial Intelligence.128(1): 99-141. 62. Thrun, S., D. Koller, Z. Ghahmarani, H. Durrant-Whyte, and A. Y. Ng. 2002 Simultaneous mapping and localization with sparse extended information filters: Theory and Initial Results. The International Journal of Robotics Research. 23(7): 693-716. 63. Thrun, S., W. Burgard and D. Fox 2005. Probabilistic Robotics 2rd ed., Massachusetts:The MIT Press. 64. Tungadi, F. and L. Kleeman. 2009. Loop exploration for SLAM with fusion of advanced sonar features and laser polar scan matching. The International Conference on Intelligent Robots and Systems. 388-394. 65. Welch, G. and G. Bishop. 1995. An introduction to the Kalman filter. University of North Carolina at Chapel Hill. 66. Wolf, J., W. Burgard and H. Burkhardt. 2005. Robust vision-based localization by combining an image-retrieval system with Monte Carlo localization. IEEE Transactions on Robotics. 21(2): 208-216. 67. Younse, P. J. and T. F. Burks. 2007. Greenhouse robot navigation using KLT feature tracking for visual vdometry. International Commission of Agricultural Engineering. 9:1-22 68. Zhang, Q., J. Reid and N. Noguchi. 1999. Agricultural vehicle navigation using multiple guidance sensors. Proceedings of the International Conference on Field and Service Robotics. 99-7013. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/43197 | - |
| dc.description.abstract | 本研究著力於發展可用於田間工作之自主式行走機器人。為達成此目的所開發之系統，針對於未知環境探索採用SLAM技術，而自主導航的部分則是採用Field D* Lite演算法。原有系統以粒子濾波器為基礎之系統，具有運算時間過長等問題。為解決遭遇到之瓶頸，採用以擴展式卡爾曼濾波器為基礎之SLAM演算法，建構機器人運動模型，以高斯分佈描述機器人的運動誤差，並運用雷射測距儀，對環境擷取「線」特徵與「角點」特徵 (結合Harris偵測演算法和雷射光束模型)，作為定位校正的量測依據。於路徑規劃部分，採用Field D* Lite演算法，可以針對不斷變動的環境，快速調整路徑。本系統平均每一步定位誤差小於9%，平均運算時間為1.26秒。若於已知地圖情況下，則每部平均運算時間為0.45秒單步運行時間較舊有系統提升速度達4倍。於建構地圖時，平均速度約為5.2公分/秒，於自主導行時，平均速度為7.5公分/秒，整體而言已具備農業移動平台之實用價值。 | zh_TW |
| dc.description.abstract | In this thesis, an autonomous mobile robot applied to agricultural environment is proposed. To explore the unknown field and navigate with map, the system use SLAM algorithm and the auto path planning technique. Instead of particle filter which costs more time at calculate, our research focus on EKF-based SLAM. Depending on the theory of Kalman filter, all the error (including the motion model and measurement model) follow the Gaussian distribution. The system is equipped with the two-dimensional laser range finder to get the data, and extracts line feature and the corner feature (using Harris corner detection and the beam model of laser) from the contour of environment. Field D* lite algorithm is the main part of the navigation system. It can update the path when the map is changed in real time. The error in localization is less than 9 %, and the average running time is 1.26 seconds per step. Excluding the mapping step, the average running time is speed up to 0.45 seconds per step. With the procedure for SLAM, the average speed is 5.2cm/s. With the procedure for auto navigation, the average speed is 7.5cm/s. The result is feasible for agricultural applications. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:42:05Z (GMT). No. of bitstreams: 1 ntu-100-R98631005-1.pdf: 17959823 bytes, checksum: ff4a200728e4fe2d4479bf6ff54412c8 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 誌 謝 i
摘 要 iii Abstract iv 目 錄 v 圖目錄 viii 表目錄 x 第一章 緒 論 1 1.1 前言 1 1.2 研究目的 2 第二章 文獻探討 4 2.1 機率機器人 4 2.2 機器人定位 7 2.2.1 擴展式卡爾曼濾波器定位 8 2.2.2 蒙地卡羅定位 10 2.3 機器人同步定位與建構地圖 12 2.3.1 Online SLAM 與 Full SLAM 12 2.3.2 EKF SLAM 13 2.3.3 Sparse Extended Information SLAM 14 2.3.4 Scan Matching SLAM 14 2.3.5 Factor Solution To SLAM 15 2.3.6 Marginal SLAM 15 2.4 機器人運動模型 16 2.5 導航系統與路徑規劃演算法 17 2.5.1 全球定位系統 (Global Positioning System) 導航 17 2.5.2 特徵導航 18 2.5.3 路徑規劃演算法 18 2.6 機器人於農業上之運用 19 2.6.1 固定式農業機器人 20 2.6.2 移動式農業機器人 20 2.6.3 SLAM技術於戶外農業機器人之應用 22 第三章 材料與方法 23 3.1 田間機器人系統架構 23 3.1.1 硬體架構 23 3.1.2 軟體程式架構 26 3.1.3 多線程程式架構 27 3.1.4 系統運作流程 28 3.2 機器人模型 30 3.2.1 空間座標系統 30 3.2.2 機器人運動模型 31 3.2.3 感測器模型 32 3.2.4 雷射資料點分層結構 34 3.3 同步定位與建構地圖 36 3.3.1 EKF SLAM之特徵點選取 36 3.3.2 EKF SLAM 43 3.4 機率格點地圖建置 48 3.5 機器人運動規劃 49 3.5.1 路徑規劃 49 3.5.2 導航 52 3.6 系統完整演算流程 53 3.7 實驗規劃與系統驗證 54 第四章 結果與討論 55 4.1 系統軟體開發 55 4.1.1 軟體程式架構 55 4.1.2 系統運動誤差統計與模型建立 56 4.2 EKF 定位演算法 61 4.2.1 特徵點擷取 61 4.2.2 特徵點選取 64 4.2.3 EKF SLAM演算法之實現 65 4.2.4 Occupancy Grid Map之實現 68 4.3 Field D* Lite之實現 69 4.4 系統誤差分析 70 4.5 系統特徵點描述與機器人定位誤差分佈 71 4.6 分層比對接合之實現 73 4.7 封閉迴圈接合實驗 76 4.8 與舊有系統之比較 80 4.8.1 系統運算時間比較 82 4.8.2 系統定位誤差之比較 84 4.9 個案研究 85 第五章 結論與建議 88 5.1 結論 88 5.2 建議 89 參考文獻 90 | |
| dc.language.iso | zh-TW | |
| dc.title | 基於擴展式卡爾曼濾波器之
田間機器人即時同步定位與建構地圖演算法 | zh_TW |
| dc.title | A Real-Time SLAM Algorithm Based on EKF
for Field Robot | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 顏炳郎(Ping-Lang Yen),王傑智(Chieh-Chih Wang) | |
| dc.subject.keyword | 擴展式卡爾曼濾波器,SLAM,田間機器人,field D* lite, | zh_TW |
| dc.subject.keyword | EKF SLAM,SLAM,field robot,field D* lite, | en |
| dc.relation.page | 96 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-08-16 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農藝學研究所 | zh_TW |
| 顯示於系所單位： | 農藝學系 | |
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