基於多感測融合的室內無人車導航

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鄭竣元(Chun-Yuan Cheng) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於多感測融合的室內無人車導航

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摘要(中)

本論文主旨為設計與實現一個適用於室內環境的自主移動機器人(Autonomous Mobile Robot, AMR)，其能夠自主行走、自動導航及避障，主要應用於校園室內環境的配送服務，如送餐和送件。同時，針對無人車的導航準確性進行深入性的研究，探索並比較多感測器融合技術在不同演算法下的性能表現，進而優化其導航與定位的精準度。
本論文首先利用光學雷達(LiDAR)結合同步定位與地圖構建(Simultaneous Localization and Mapping, SLAM)方法對整個實驗場域進行建圖，隨後對於系統中選用的感測器做資料前處理，消除雜訊獲取更精確的姿態訊息。此外，為確保不同感測器資料在時間上的一致性，使用資料時間同步技術，減少了延遲與融合誤差，也能準確地反映無人車即時的姿態，為多感測器融合奠定了良好的基礎。在完成上述準備工作後，分別運用擴展卡爾曼濾波(Extended Kalman Filter, EKF)和無跡卡爾曼濾波(Unscented Kalman Filter, UKF)兩種演算法針對單一感測器和多感測器進行無人車狀態預測，執行自主導航與避障任務。本論文的重點在於透過實驗數據，分析不同演算法及感測器配置下的導航精準度表現，並調適這兩個種演算法中的參數設置，以提升其預測精準度，最終選擇平均誤差最小的方案作為實際應用。另外，本研究還探討了其他優化策略，進一步提升導航系統的精準度。
本研究在建立於Linux操作系統，使用機器人作業系統(Robot Operating System, ROS)作軟體開發框架實驗，利用TCP/IP協議進行數據傳輸與通訊，並在硬體部分以NVIDIA Jetson AGX Xavier做為系統核心，無人車底盤則採用ZW3840差速輪架構，實現軟硬結合的協作設計，完成複雜的指派任務。本論文的實驗結果顯示，在20公尺的導航路徑上，系統的平均導航誤差僅為0.13公尺，相當於1%左右的相對誤差，此一研究成果可顯示自主機器人在校園室內環境中的自主導航與避障的可行性，並有潛力應用於各種配送服務。

摘要(英)

The main objective of this thesis is to design and implement an Autonomous Mobile Robot (AMR) for indoor environments, capable of autonomous walking, navigation, and obstacle avoidance. The system is primarily applied to campus delivery services, such as food and document delivery. This study also investigates the navigation accuracy of AMR by exploring and comparing multi-sensor fusion performance under different algorithms to optimize navigation and localization precision.
LiDAR combined with Simultaneous Localization and Mapping (SLAM) maps the experimental environment, followed by sensor data preprocessing to eliminate noise and improve pose accuracy. Data synchronization techniques ensure consistency across sensor data, reducing delay and fusion errors while accurately reflecting the robot′s real-time state. Two algorithms, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF), are applied to single and multi-sensor configurations for vehicle state prediction, enabling autonomous navigation and obstacle avoidance. Experimental data is analyzed to evaluate navigation accuracy under different configurations, with parameters adjusted to enhance prediction precision. The algorithm with the smallest average error is selected for implementation, while additional optimization strategies are explored to improve system accuracy further.
The system is built on the Linux operating system using Robot Operating System (ROS) for software development and TCP/IP for data communication. NVIDIA Jetson AGX Xavier is the system′s core, and the ZW3840 differential drive chassis provides the hardware foundation. Experimental results show the system achieves an average navigation error of 0.13 meters over a 20-meter path, corresponding to a relative error of about 1%. These findings demonstrate the feasibility of autonomous navigation and obstacle avoidance in indoor campus environments and highlight its potential for different delivery services.

關鍵字(中)

★ 自主移動機器人
★ 同步定位與地圖建構
★ 多感測融合
★ 導航
★ ROS

關鍵字(英)

★ Autonomous Mobile Robot
★ Simultaneous Localization and Mapping
★ Multi-sensor Fusion
★ Navigation
★ ROS

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vii
表目錄 ix
第一章緒論 1
1.1研究背景與動機 1
1.2文獻回顧 2
1.3論文目標 4
1.4論文架構 4
第二章系統架構與軟硬體介紹 5
2.1系統架構 5
2.2硬體架構 6
2.2.1 無人車運算平台介紹 7
2.2.2 無人車硬體設備及感測器介紹 7
2.2.3 電腦設備介紹 11
2.3軟韌體介紹 12
2.3.1 ROS系統 12
2.3.2感測器之官方調適軟體 16
第三章同時定位與地圖建構 18
3.1各種地圖構建演算法的簡介與比較 18
3.2 Cartographer SLAM演算法 19
3.2.1整體架構與原理 20
3.2.2參數調整與基本設置 21
第四章多感測融合演算法 27
4.1資料預處理 27
4.1.1 IMU相關處理(校正、濾波、協方差矩陣缺失之補償) 28
4.1.2 ZED2標定 30
4.1.3利用ORB-SLAM3估算VIO 31
4.1.4資料時間同步 32
4.2擴展卡爾曼濾波器(Extended Kalman Filter, EKF) 33
4.2.1運作原理 33
4.2.2實作過程 34
4.3無跡卡爾曼濾波器(Unscented Kalman Filter, UKF) 34
4.3.1運作原理 35
4.3.2實作過程 36
第五章導航與避障 38
5.1 ROS Navigation 38
5.1.1建立地圖與TF座標轉換 38
5.1.2 AMCL定位 40
5.1.3路徑規劃與避障 40
5.1.4底盤運動控制 43
5.2其他優化導航方式－AprilTag 44
5.2.1 AprilTag介紹 44
5.2.2 AprilTag座標轉換 45
5.2.3 AprilTag修正導航精準度 46
第六章實驗結果 48
6.1單獨感測器精準度比較 48
6.2多感測器融合精準度比較 49
6.2.1 Odom+IMU 50
6.2.2 Odom+IMU+VO(ZED2) 51
6.2.3 Odom+IMU+VIO(ORB-SLAM3) 52
6.3實際導航結果 52
6.4 AprilTag修正精準度結果 54
第七章結論與未來展望 55
7.1結論 55
7.2未來展望 55
參考文獻 56

參考文獻

[1] "經濟部統計處 – 產業經濟統計," [Online]. Available: https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=9&html=1&menu_id=18808&bull_id=6878. [Accessed: Dec. 2024].
[2] "經濟部統計處 – 產業經濟統計," [Online]. Available: https://www.moea.gov.tw/Mns/dos/bulletin/Bulletin.aspx?kind=9&html=1&menu_id=18808&bull_id=11322. [Accessed: Dec. 2024].
[3] Z. -X. Li, G. -H. Cui, C. -L. Li and Z. -S. Zhang, "Comparative Study of Slam Algorithms for Mobile Robots in Complex Environment," 2021 6th International Conference on Control, Robotics and Cybernetics (CRC), Shanghai, China, 2021, pp. 74-79.
[4] B. Garigipati, N. Strokina and R. Ghabcheloo, "Evaluation and comparison of eight popular Lidar and Visual SLAM algorithms," 2022 25th International Conference on Information Fusion (FUSION), Linkoping, Sweden, 2022, pp. 1-8.
[5] 陳柏伸，可即時分析人行道路況之全自主外送機器人設計與實作，國立虎尾科技大學，資訊工程研究所，碩士論文(陳國益指導)，2021年。
[6] Y. Jiang, M. Leach, L. Yu and J. Sun, "Mapping, Navigation, Dynamic Collision Avoidance and Tracking with LiDAR and Vision Fusion for AGV Systems," 2023 28th International Conference on Automation and Computing (ICAC), Birmingham, United Kingdom, 2023, pp. 1-6.
[7] C. Wang, M. Wang, W. Zhan and X. Ye, "Algorithm Research Based on 2D LiDAR-Binocular Camera Fusion," 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Beijing, China, October 2022, pp. 297-300.
[8] H. Deilamsalehy and T. C. Havens, "Sensor fused three-dimensional localization using IMU, camera and LiDAR," 2016 IEEE SENSORS, Orlando, FL, USA, 2016, pp. 1-3.
[9] Z. Liu, H. Li and L. Wang, "FVL-OutLoc: Fusion of Vision and 2D LiDAR for Accurate Outdoor Localization," 2021 China Automation Congress (CAC), Beijing, China, October 2021, pp. 7405-7410.
[10] 鄭珮甄，基於自適應EKF改進室內全向AMR之多感測器融合SLAM，國立中興大學，電機工程研究所，碩士論文(蔡清池指導)，2023年。
[11] 吳浩暉，以擴展卡爾曼濾波器融合雙眼視覺與慣性里程計於室內定位，國立臺北科技大學，自動化科技研究所，碩士論文(陳金聖指導)，2019年。
[12] C. Huang and Z. Ma, "Research Progress and Application of Multi-Sensor Data Fusion Technology in AGVs," 2024 5th International Conference on Mechatronics Technology and Intelligent Manufacturing (ICMTIM), Nanjing, China, 2024, pp. 256-261.
[13] C. Can, W. Peng, T. Bi and H. Zhu, "Research on Automotive Safety Based on Multi-Sensor Information Fusion Technology," 2024 Asia-Pacific Conference on Software Engineering, Social Network Analysis and Intelligent Computing (SSAIC), New Delhi, India, 2024, pp. 750-754.
[14] M. H T, M. L and M. Sajjad, "ROS Powered Autonomous Mobile Robot for Indoor Applications," 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS), Hassan, India, 2024, pp. 1-7.
[15] S. Akhlaghi, N. Zhou and Z. Huang, "Adaptive adjustment of noise covariance in Kalman filter for dynamic state estimation," 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, USA, 2017, pp. 1-5.
[16] M. S. S. M, N. A. Wahab, M. S. A. Mahmud, H. Alqaraghuli, E. Samsuria and M. Z. Romdlony, "Efficient Autonomous Navigation in Dynamic Environments: Algorithm Evaluation and Multi-Robot Coordination," 2024 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), Shah Alam, Malaysia, 2024, pp. 433-438.
[17] G. Yuan, M. Zhang, S. Wei, L. Ma, K. Shen and Y. Sun, "Intelligent lead and navigation system for mobile robots combined with multi-sensor data," 2024 43rd Chinese Control Conference (CCC), Kunming, China, 2024, pp. 4433-4438.
[18] R. Ni, J. Liu, H. Li, Z. Cao, X. Wang and Y. Liu, "Research on Robot Path Planning Based on Fusion Algorithm of Optimized A* and DWA," 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP), Xian, China, 2024, pp. 1436-1439.
[19] 陳昭亮，基於2D LiDAR之動態避障VFH+D演算法改良，國立中興大學，機械工程研究所，碩士論文(何俊逸指導)，2022年。
[20] J. Kallwies, B. Forkel and H. -J. Wuensche, "Determining and Improving the Localization Accuracy of AprilTag Detection," 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020, pp. 8288-8294.
[21] J. Chen, Y. Gao and S. Li, "Real-time Apriltag Inertial Fusion Localization for Large Indoor Navigation," 2020 Chinese Automation Congress (CAC), Shanghai, China, 2020, pp. 6912-6916.
[22] Y. Zhang et al., "GCMVF-AGV: Globally Consistent Multiview Visual–Inertial Fusion for AGV Navigation in Digital Workshops," in IEEE Transactions on Instrumentation and Measurement, vol. 72, pp. 1-16, 2023.
[23] "NVIDIA Jetson AGX Xavier," [Online]. Available: https://www.nvidia.com/zh-tw/autonomous-machines/embedded-systems/jetson-agx-xavier/. [Accessed: Dec. 2024].
[24] "ZW3840差速輪底盤," [Online]. Available: https://zanrobot.com/shop/heavy-wheels-mcaum/. [Accessed: Dec. 2024].
[25] "USB HUB 3.0," [Online]. Available: https://24h.pchome.com.tw/prod/DCADIJ-A9008XIBT?srsltid=AfmBOoqlLTCOD4S7rwNoBxyIOs6pxDCfnypuyaOwNPXquSEh5fvbJ8YL. [Accessed: Dec. 2024].
[26] "USB Type-C HUB," [Online]. Available:
https://24h.pchome.com.tw/prod/DGCD15-A9008XL5D?srsltid=AfmBOoqLY-RrnD3BIDItfXlf2Nz-A2z_Ked11hziZtV0sKUUxUeNp9Of. [Accessed: Dec. 2024].
[27] "EDIMAX無線網卡," [Online]. Available: https://www.edimax.com/edimax/merchandise/merchandise_detail/data/edimax/tw/wireless_adapters_n150/ew-7811un_v2/. [Accessed: Dec. 2024].
[28] "RPLIDAR-S2," [Online]. Available: https://www.slamtec.com/en/S2. [Accessed: Dec. 2024].
[29] "十軸IMU慣導模組," [Online]. Available: http://www.yahboom.net/study/IMU. [Accessed: Dec. 2024].
[30] "ZED2相機," [Online]. Available: https://www.stereolabs.com/en-tw/store/products/zed-2. [Accessed: Dec. 2024].
[31] "ROS Wiki," [Online]. Available: https://wiki.ros.org/Documentation. [Accessed: Dec. 2024].
[32] "Cartographer SLAM," [Online]. Available: https://google-cartographer-ros.readthedocs.io/en/latest/algo_walkthrough.html. [Accessed: Dec. 2024].
[33] "imu_tools," [Online]. Available: http://wiki.ros.org/imu_tools. [Accessed: Dec. 2024].
[34] "imu_complementary_filter," [Online]. Available: http://wiki.ros.org/imu_complementary_filter?distro=noetic. [Accessed: Dec. 2024].
[35] "Kalibr工具," [Online]. Available: https://github.com/ethz-asl/Kalibr. [Accessed: Dec. 2024].
[36] "aprilgrid標定板," [Online]. Available: https://github.com/ethz-asl/kalibr/wiki/downloads#calibration-targets. [Accessed: Dec. 2024].
[37] "ORB_SLAM3," [Online]. Available: https://github.com/UZ-SLAMLab/ORB_SLAM3. [Accessed: Dec. 2024].
[38] "message_filters," [Online]. Available: http://wiki.ros.org/message_filters. [Accessed: Dec. 2024].
[39] "robot_pose_ekf," [Online]. Available: http://wiki.ros.org/robot_pose_ekf. [Accessed: Dec. 2024].
[40] "robot_localization," [Online]. Available: https://docs.ros.org/en/melodic/api/robot_localization/html/index.html. [Accessed: Dec. 2024].
[41] "ROS Navigation Stack(move_base)," [Online]. Available: http://wiki.ros.org/move_base. [Accessed: Dec. 2024].
[42] "AMCL," [Online]. Available: http://wiki.ros.org/amcl. [Accessed: Dec. 2024].
[43] "OpenMV," [Online]. Available: https://openmv.io. [Accessed: Dec. 2024].
[44] "apriltag_ros," [Online]. Available: http://wiki.ros.org/apriltag_ros. [Accessed: Dec. 2024].

指導教授

王文俊(Wen-June Wang)

審核日期

2025-1-20

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