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  1. NTU Theses and Dissertations Repository
  2. 生物資源暨農學院
  3. 生物機電工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101286
完整後設資料紀錄
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dc.contributor.advisor陳世芳zh_TW
dc.contributor.advisorShih-Fang Chenen
dc.contributor.author白騏瑞zh_TW
dc.contributor.authorCi-Ruei Baien
dc.date.accessioned2026-01-13T16:13:09Z-
dc.date.available2026-01-14-
dc.date.copyright2026-01-13-
dc.date.issued2025-
dc.date.submitted2025-12-29-
dc.identifier.citation行政院主計總處(2022)。109年農林漁牧業普查初步統計結果。網址:https://www.dgbas.gov.tw/News_Content.aspx?n=3602&s=226901
行政院農業部(2016)。智慧農業4.0計畫推動成果。網址:https://www.moa.gov.tw/ws.php?id=2505139
Bekker, M. G. (1969). Introduction to terrain-vehicle systems. University of Michigan Press.
Bruyninckx, H. (2008). Robotics software: The future should be open. IEEE Robotics & Automation Magazine, 15(1), 9-11. https://doi.org/10.1109/M-RA.2008.915411
Chereau, R., & Breckon, T. P. (2013, October). Robust motion filtering as an enabler to video stabilization for a tele-operated mobile robot. In Electro-Optical Remote Sensing, Photonic Technologies, and Applications VII; and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing (Vol. 8897, pp. 162-182). SPIE. https://doi.org/10.1117/12.2028360
Gonzalez, R., Apostolopoulos, D., & Iagnemma, K. (2019). Improving rover mobility through traction control: Simulating rovers on the Moon. Autonomous Robots, 43(8), 1977-1988. https://doi.org/10.1007/s10514-019-09846-3
Gul, O. M. (2024). Energy-aware 3D path planning by autonomous ground vehicle in wireless sensor networks. World Electric Vehicle Journal, 15(9), 383. https://doi.org/10.3390/wevj15090383
Hansen, J. G., & de Figueiredo, R. P. (2024). Active object detection and tracking using gimbal mechanisms for autonomous drone applications. Drones, 8(2), 55. https://doi.org/10.3390/drones8020055
Hua, C., Zhang, W., Fu, H., Zhang, Y., Yu, B., Jiang, C., Wei, Y., Chen, Z., & Kuang, X. (2025). The prediction method and application of off-road mobility for ground vehicles: A review. World Electric Vehicle Journal, 16(1), 47. https://doi.org/10.3390/wevj16010047
Janulevičius, A., & Damanauskas, V. (2022). Prediction of tractor drive tire slippage under different inflation pressures. Journal of Terramechanics, 101, 23-31. https://doi.org/10.1016/j.jterra.2022.03.001
Kaveti, P., & Singh, H. (2020). ROS rescue: Fault tolerance system for robot operating system. In A. Koubaa (Ed.), Robot Operating System (ROS): The complete reference (Vol. 5, pp. 381-397). Springer. https://doi.org/10.1007/978-3-030-45956-7_12
Liu, X., Yang, Y., Ma, C., Li, J., & Zhang, S. (2020). Real-time visual tracking of moving targets using a low-cost unmanned aerial vehicle with a 3-axis stabilized gimbal system. Applied Sciences, 10(15), 5064. https://doi.org/10.3390/app10155064
Lyu, H. K., Park, C. H., Han, D. H., Kwak, S. W., & Choi, B. (2018). Orchard free space and center line estimation using Naive Bayesian classifier for unmanned ground self-driving vehicle. Symmetry, 10(9), 355. https://doi.org/10.3390/sym10090355
Macenski, S., Foote, T., Gerkey, B., Lalancette, C., & Woodall, W. (2022). Robot Operating System 2: Design, architecture, and uses in the wild. Science Robotics, 7(66), eabm6074. https://doi.org/10.1126/scirobotics.abm6074
Maruyama, Y., Kato, S., & Azumi, T. (2016, October). Exploring the performance of ROS2. In Proceedings of the 2016 13th International Conference on Embedded Software and Systems (ICESS) (pp. 1-10). IEEE. https://doi.org/10.1145/2968478.2968502
Plett, G. L. (2015). Battery management systems: Vol. I. Battery modeling. Artech House.
Qu, J., Gu, Y., Qiu, Z., Guo, K., & Zhu, Q. (2025). Development of an orchard inspection robot: A ROS-based LiDAR-SLAM system with hybrid A*-DWA navigation. Sensors, 25(21), 6662. https://doi.org/10.3390/s25216662
Quigley, M., Gerkey, B., Conley, K., Faust, J., Foote, T., Leibs, J., Berger, E., Wheeler, R., & Ng, A. (2009, May 12-17). ROS: An open-source Robot Operating System [Conference presentation]. ICRA Workshop on Open Source Software, Kobe, Japan.
Rauf, O., Ning, Y., Ming, C., & Haoxiang, M. (2024). Evaluation of ground pressure, bearing capacity, and sinkage in rigid-flexible tracked vehicles on characterized terrain in laboratory conditions. Sensors, 24(6), 1779. https://doi.org/10.3390/s24061779
Sani, E., Sgorbissa, A., & Carpin, S. (2024, May). Improving the ROS 2 navigation stack with real-time local costmap updates for agricultural applications. In Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 17701-17707). IEEE. https://doi.org/10.1109/ICRA57147.2024.10610984
Shi, Y., Liu, J., Huang, D., Xu, M., Zhai, S., Zhang, W., & Jiang, P. (2023). Prediction and experimental study of tire slip rate based on chassis sinkage amount. Agriculture, 13(3), 665. https://doi.org/10.3390/agriculture13030665
Taghavifar, H., & Mardani, A. (2014). On the modeling of energy efficiency indices of agricultural tractor driving wheels applying adaptive neuro-fuzzy inference system. Journal of Terramechanics, 56, 37-47. https://doi.org/10.1016/j.jterra.2014.08.002
Tremblay, O., & Dessaint, L. A. (2009). Experimental validation of a battery dynamic model for EV applications. World Electric Vehicle Journal, 3(2), 289-298. https://doi.org/10.3390/wevj3020289
Verma, M., Lafarga, V., Baron, M., & Collette, C. (2020). Active stabilization of unmanned aerial vehicle imaging platform. Journal of Vibration and Control, 26(19-20), 1791-1803. https://doi.org/10.1177/1077546320905494
Vu, C. T., Chen, H. C., & Liu, Y. C. (2024, November). Toward autonomous navigation for agriculture robots in orchard farming. In Proceedings of the 2024 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE) (pp. 1-8). IEEE. https://doi.org/10.1109/RASSE64357.2024.10773736
Wong, J. Y. (2022). Theory of ground vehicles (5th ed.). John Wiley & Sons.
Xu, Q., Huang, Q., Jiang, C., Li, X., & Wang, Y. (2025). Video stabilization: A comprehensive survey from classical mechanics to deep learning paradigms. Modelling, 6(2), 49. https://doi.org/10.3390/modelling6020049
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/101286-
dc.description.abstract隨著臺灣農業勞動力逐漸老化且人力資源日益短缺,自主田間載具已成為推動智慧農業發展之重要基礎。然而,實際農業作業環境中普遍存在地形起伏不平之特性,易導致載具行進不穩、影像震動劇烈,以及能源續航不足等問題,進而限制自主作業系統之可靠性與實用性。本研究針對上述挑戰,設計並建置一款田間自主載具,以機器人作業系統2 (Robot Operating System 2, ROS2)作為核心控制架構,並分別從行動性能、視覺穩定性與電力續航力三個面向進行系統化評估與驗證。
在行動性能方面,本研究透過滑移率(Slip Ratio)評估不同輪胎之行走效率。實驗結果顯示,在顛簸地表下,充氣胎相較於泡棉胎具有較佳之地形適應能力,能有效降低輪胎滑移現象,提升整體推進效率。在視覺穩定性方面,本研究導入二軸穩定器,以補償地形起伏所引起之車體姿態擾動。實驗結果顯示,該系統能有效抑制影像震動與視角偏移,使影像垂直位移之標準差由4.255 cm降低至2.090 cm,傾斜角標準差由4.293°降低至0.999°,提升影像品質與穩定度。在電力續航方面,所搭載之鋰電池系統在連續導控與拍攝模式下,平均可運作約157至173 min,其巡航里程足以覆蓋約25.6公畝(0.256公頃,約0.63英畝)之室外果園,能滿足中小型農田之巡檢作業需求。
綜合上述結果,本研究透過田間自主載具之系統整合與性能評估,並結合ROS2之模組化設計與通訊架構,驗證載具於行走穩定性、影像穩定性與續航能力之表現。研究結果顯示,該載具可作為精準農業監測任務之穩定作業平台,並提供農業自主載具平台設計與配置選擇之實證基礎。
zh_TW
dc.description.abstractWith the aging of the agricultural workforce and the increasing shortage of labor in Taiwan, autonomous field vehicles have become an essential foundation for the development of smart agriculture. However, real agricultural environments are characterized by uneven terrain, which often leads to unstable vehicle motion, severe image vibration, and insufficient energy endurance, thereby limiting the reliability and practicality of autonomous field operations. To address these challenges, this study designs and implements an autonomous field vehicle using Robot Operating System 2 (ROS2) as the core control framework. The system is systematically evaluated from three aspects: mobility performance, visual stability, and power endurance.
In terms of mobility performance, the slip ratio is adopted to evaluate the traveling efficiency of different tire types. Experimental results indicate that, under uneven terrain conditions, pneumatic tires exhibit better terrain adaptability than foam-filled tires by effectively reducing wheel slip and improving overall propulsion efficiency. For visual stability, a two-axis gimbal is integrated to compensate for vehicle body disturbances caused by terrain irregularities. Experimental results demonstrate that the proposed stabilization system effectively suppresses image vibration and viewpoint deviation, reducing the standard deviation of vertical displacement from 4.255 cm to 2.090 cm and the standard deviation of tilt angle from 4.293° to 0.999°, thereby improving image quality and stability. In terms of power endurance, the integrated lithium battery system achieves an average operating time of approximately 157 to 173 minutes under continuous navigation and image acquisition modes. The corresponding cruising range is sufficient to cover approximately 0.256 hectares (about 0.63 acres) of outdoor orchards, meeting the inspection requirements of small- to medium-scale farmlands.
Based on the above results, this study verifies the performance of the autonomous field vehicle in terms of mobility stability, visual stability, and endurance through systematic system integration and performance evaluation, combined with the modular design and communication architecture of ROS2. The developed vehicle demonstrates its suitability as a stable operational platform for precision agricultural monitoring and provides empirical evidence to support platform design and configuration selection for agricultural autonomous field vehicles.
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dc.description.provenanceSubmitted by admin ntu (admin@lib.ntu.edu.tw) on 2026-01-13T16:13:09Z
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dc.description.provenanceMade available in DSpace on 2026-01-13T16:13:09Z (GMT). No. of bitstreams: 0en
dc.description.tableofcontents致謝 i
摘要 ii
ABSTRACT iii
目次 v
圖次 vii
表次 viii
中英文名詞暨縮寫對照 ix
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 3
2.1 機器人作業系統(ROS) 3
2.1.1 傳統機器人系統之限制與模組化需求 3
2.1.2 ROS於實務部署之限制與ROS2之發展 3
2.1.3 ROS2於農業自主載具之應用 5
2.2 行動性能:輪地互動 6
2.2.1 輪地互動之常見評估指標 6
2.2.2 滑移率(Slip Ratio)之定義與特性 7
2.2.3 滑移率於農業載具輪胎性能評估之應用 7
2.3 田間影像穩定 8
2.3.1 影像穩定技術之分類 8
2.3.2 雲台穩定之原理與常用領域 9
2.3.3 雲台於田間載具之應用 9
2.4 電池續航力與放電特性分析 10
2.4.1 農業機器人之能源需求與挑戰 10
2.4.2 鋰電池放電特性曲線分析 10
第三章 材料與方法 11
3.1 載具應用場域與硬體配置 11
3.1.1 應用場域與作物環境特性 11
3.1.2 載具硬體配置與感測器規格 12
3.2 載具運作流程與ROS2節點建置 15
3.2.1 載具運作流程 15
3.2.2 ROS2節點建置 17
3.3 行動性能 19
3.3.1 試驗環境與載具配置 19
3.3.2 地形分類與顛簸分析 20
3.3.3 行動性能評估指標:滑移率(Slip Ratio) 22
3.4 視覺穩定性 23
3.4.1 田間地形擾動對拍照桿與影像品質之影響 23
3.4.2 二軸穩定器試驗場域設置 25
3.4.3 影像處理與直線擬合 26
3.4.4 實際尺度轉換 27
3.4.5 穩定性指標定義與評估 28
3.5 電力續航 30
3.5.1 電路配置 30
3.5.2 電池續航力 31
第四章 結果與討論 33
4.1 行動性能試驗結果 33
4.1.1 地勢情境一(平整草地)下泡棉胎與充氣胎之滑移率比較 33
4.1.2 地勢情境二(顛簸草地)下泡棉胎與充氣胎之滑移率比較 34
4.2 視覺穩定性試驗結果 35
4.2.1 影像穩定性能量化分析 35
4.2.2 影像姿態之視覺比較 36
4.3 電力續航試驗結果 37
第五章 結論與建議 39
5.1 結論 39
5.2 建議 40
參考文獻 41
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dc.language.isozh_TW-
dc.subject自主載具-
dc.subject機器人作業系統2 (ROS2)-
dc.subject滑移率-
dc.subject二軸穩定器-
dc.subject電力管理-
dc.subject智慧農業-
dc.subjectAutonomous Vehicles-
dc.subjectRobot Operating System 2 (ROS2)-
dc.subjectSlip Ratio-
dc.subjectTwo- axis Gimbal-
dc.subjectPower Management-
dc.subjectSmart Farming-
dc.title基於ROS2之田間載具優化設計zh_TW
dc.titleOptimized Design of a Field Robot Based on ROS2en
dc.typeThesis-
dc.date.schoolyear114-1-
dc.description.degree碩士-
dc.contributor.oralexamcommittee胡明哲;廖國基zh_TW
dc.contributor.oralexamcommitteeMing-Che Hu;Kuo-Chi Liaoen
dc.subject.keyword自主載具,機器人作業系統2 (ROS2)滑移率二軸穩定器電力管理智慧農業zh_TW
dc.subject.keywordAutonomous Vehicles,Robot Operating System 2 (ROS2)Slip RatioTwo- axis GimbalPower ManagementSmart Farmingen
dc.relation.page43-
dc.identifier.doi10.6342/NTU202504857-
dc.rights.note同意授權(限校園內公開)-
dc.date.accepted2025-12-30-
dc.contributor.author-college生物資源暨農學院-
dc.contributor.author-dept生物機電工程學系-
dc.date.embargo-lift2030-12-29-
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