高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于超宽带阵列与里程计的多机器人相对定位

刘冉 曹志强 邓天睿 邓忠元 肖宇峰

刘冉, 曹志强, 邓天睿, 邓忠元, 肖宇峰. 基于超宽带阵列与里程计的多机器人相对定位[J]. 电子与信息学报, 2022, 44(10): 3476-3484. doi: 10.11999/JEIT210812
引用本文: 刘冉, 曹志强, 邓天睿, 邓忠元, 肖宇峰. 基于超宽带阵列与里程计的多机器人相对定位[J]. 电子与信息学报, 2022, 44(10): 3476-3484. doi: 10.11999/JEIT210812
LIU Ran, CAO Zhiqiang, DENG Tianrui, DENG Zhongyuan, XIAO Yufeng. Multiple Robots Localization Based on the Fusion of Ultra-Wideband Array and Odometry[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3476-3484. doi: 10.11999/JEIT210812
Citation: LIU Ran, CAO Zhiqiang, DENG Tianrui, DENG Zhongyuan, XIAO Yufeng. Multiple Robots Localization Based on the Fusion of Ultra-Wideband Array and Odometry[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3476-3484. doi: 10.11999/JEIT210812

基于超宽带阵列与里程计的多机器人相对定位

doi: 10.11999/JEIT210812
基金项目: 国家自然科学基金 (12175187),国家重点研发计划 (2019YFB1310805)
详细信息
    作者简介:

    刘冉:男,副研究员,研究方向为机器人导航定位、室内定位、SLAM

    曹志强:男,硕士生,研究方向为室内定位、多机器人定位

    邓天睿:男,硕士生,研究方向为室内定位

    邓忠元:男,硕士生,研究方向为室内定位

    肖宇峰:男,教授,研究方向为特种机器人

    通讯作者:

    刘冉 ran.liu.86@hotmail.com

  • 中图分类号: TP301.6; TP242.6

Multiple Robots Localization Based on the Fusion of Ultra-Wideband Array and Odometry

Funds: The National Natural Science Foundation of China (12175187), The National Key Research Program (2019YFB1310805)
  • 摘要: 精准的相对定位是实现多机器人协作与编队控制的关键。在弱全球定位系统(GPS)的室内环境中,视觉或激光雷达(LiDAR)通过特征匹配的方式确定机器人间相对位置,但在非视距环境下难以工作。针对这一问题,该文提出一种基于多超宽带(UWB)节点的移动机器人相对定位方法。首先,利用每个机器人携带的多个UWB节点构成UWB阵列,通过非线性优化实现移动机器人间相对姿态估计。为进一步提升估计精度,利用里程计对非线性优化结果进行约束,通过图优化算法对滑动窗口内的相对位姿与里程计进行优化,保证了算法的实时性。然而,图优化过程中难以确定相对位姿估计的误差,对定位结果影响较大。因此,利用粒子滤波融合里程计和滑动窗口优化后的相对位姿,进一步提升相对姿态估计的精度。实验结果表明,该方法在12×6 m的室内环境中,能够达到0.312 m的平均定位误差以及4.903°平均角度误差,且具有良好的实时性。
  • 图  1  多机器人相对定位示意图

    图  2  算法框架图

    图  3  粒子滤波算法流程框图

    图  4  数据采集平台

    图  5  不同实验的设置

    图  6  实验1-b的估计轨迹

    图  7  实验1-a的评估结果

    图  8  实验2估计轨迹

    图  9  计算时间:不同估计方法的时间消耗

    表  1  实验1:不同估计方法在不同的UWB节点配置下,位置误差以及角度误差的评估结果

    估计方法UWB节点距离配置
    0.3 m0.5 m0.7 m
    位置误差(m)角度误差(º)位置误差(m)角度误差(º)位置误差(m)角度误差(º)
    里程计0.96±0.4310.01±4.540.79±0.326.27±3.540.62±0.233.71±2.38
    非线性优化(仅UWB)0.48±0.267.57±5.570.39±0.245.27±4.340.37±0.225.05±3.63
    滑动窗口(w=5)0.42±0.187.02±5.530.35±0.174.77±4.150.33±0.184.69±3.95
    滑动窗口(w=30)0.38±0.144.46±2.240.33±0.133.75±2.360.32±0.133.19±2.63
    滑动窗口(w=80)0.33±0.112.99±2.330.30±0.102.55±2.060.30±0.122.35±2.56
    滑动窗口(w=160)0.37±0.183.49±1.860.30±0.142.91±1.760.31±0.153.09±2.34
    粒子滤波(UWB距离+里程计)[22]0.35±0.205.31±3.910.49±0.236.26±3.840.35±0.224.84±3.12
    粒子滤波(滑动窗口+里程计)0.25±0.102.91±2.760.22±0.102.70±2.270.25±0.092.02±1.47
    下载: 导出CSV

    表  2  实验2:不同估计方法在w=30及UWB间距0.5 m情况下评估结果

    估计方法位置误差(m)角度误差(º)
    里程计1.46622.076
    非线性优化(仅UWB)0.5287.413
    滑动窗口(w=30)0.4245.648
    粒子滤波(UWB距离+里程计)[22]0.5347.234
    粒子滤波(滑动窗口+里程计)0.3124.903
    下载: 导出CSV
  • [1] GUO Xiansheng, ANSARI N, HU Fangzi, et al. A survey on fusion-based indoor positioning[J]. IEEE Communications Surveys & Tutorials, 2020, 22(1): 566–594.
    [2] MOON S, CHOI Y, KIM D, et al. Outdoor swarm flight system based on RTK-GPS[J]. Journal of KIISE, 2016, 43(12): 1315–1324. doi: 10.5626/JOK.2016.43.12.1315
    [3] SAKURAMA K, KOSAKA Y, and NISHIDA S I. Formation control of swarm robots with multiple proximity distance sensors[J]. International Journal of Control, Automation and Systems, 2018, 16(1): 16–26. doi: 10.1007/s12555-016-0741-z
    [4] PIASCO N, MARZAT J, and SANFOURCHE M. Collaborative localization and formation flying using distributed stereo-vision[C]. 2016 IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweden, 2016: 1202–1207.
    [5] PREISS J A, HONIG W, SUKHATME G S, et al. Crazyswarm: A large nano-quadcopter swarm[C]. 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017: 3299–3304.
    [6] 杨海, 李威, 张禾, 等. 复杂坏境下基于SINS/UWB的容错组合定位技术研究[J]. 仪器仪表学报, 2017, 38(9): 2177–2185. doi: 10.3969/j.issn.0254-3087.2017.09.011

    YANG Hai, LI Wei, ZHANG He, et al. Fault tolerant integrated positioning system based on SINS/UWB in complex environment[J]. Chinese Journal of Scientific Instrument, 2017, 38(9): 2177–2185. doi: 10.3969/j.issn.0254-3087.2017.09.011
    [7] 胡久松, 刘宏立, 肖郭璇, 等. 应用于WiFi室内定位的自适应仿射传播聚类算法[J]. 电子与信息学报, 2018, 40(12): 2889–2895.

    HU Jiusong, LIU Hongli, XIAO Guoxuan, et al. Adaptive affine propagation clustering algorithm for WiFi indoor positioning[J]. Journal of Electronics &Information Technology, 2018, 40(12): 2889–2895.
    [8] LIU Ran, YUEN C, DO T N, et al. Cooperative positioning for emergency responders using self IMU and peer-to-peer radios measurements[J]. Information Fusion, 2020, 56: 93–102. doi: 10.1016/j.inffus.2019.10.009
    [9] SCARAMUZZA D, ACHTELIK M C, DOITSIDIS L, et al. Vision-controlled micro flying robots: From system design to autonomous navigation and mapping in GPS-denied environments[J]. IEEE Robotics & Automation Magazine, 2014, 21(3): 26–40.
    [10] SASKA M, BACA T, THOMAS J, et al. System for deployment of groups of unmanned micro aerial vehicles in GPS-denied environments using onboard visual relative localization[J]. Autonomous Robots, 2017, 41(4): 919–944. doi: 10.1007/s10514-016-9567-z
    [11] WANG Xinzuo and LI Wei. Design of an accurate yet low-cost distributed module for vehicular relative positioning: Hardware prototype design and algorithms[J]. IEEE Transactions on Vehicular Technology, 2019, 68(5): 4494–4501. doi: 10.1109/TVT.2019.2901743
    [12] TOMIC S, BEKO M, and DINIS R. RSS-based localization in wireless sensor networks using convex relaxation: Noncooperative and cooperative schemes[J]. IEEE Transactions on Vehicular Technology, 2015, 64(5): 2037–2050. doi: 10.1109/TVT.2014.2334397
    [13] XU Hao, WANG Luqi, ZHANG Yichen, et al. Decentralized visual-inertial-UWB fusion for relative state estimation of aerial swarm[C]. 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2020: 8776–8782.
    [14] GHANEM E, O’KEEFE K, and KLUKAS R. Testing vehicle-to-vehicle relative position and attitude estimation using multiple UWB ranging[C]. 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), Victoria, Canada, 2020: 1–5.
    [15] 吴玉秀, 孟庆浩, 曾明. 基于声音的分布式多机器人相对定位[J]. 自动化学报, 2014, 40(5): 798–809.

    WU Yuxiu, MENG Qinghao, and ZENG Ming. Sound based relative localization for distributed multi-robot systems[J]. Acta Automatica Sinica, 2014, 40(5): 798–809.
    [16] FENG Daquan, WANG Chunqi, HE Chunlong, et al. Kalman-filter-based integration of IMU and UWB for high-accuracy indoor positioning and navigation[J]. IEEE Internet of Things Journal, 2020, 7(4): 3133–3146. doi: 10.1109/JIOT.2020.2965115
    [17] 赵晨, 乔钢, 周锋. 基于正交移动双水下自主潜航器的水下合作目标定位方法[J]. 电子与信息学报, 2021, 43(3): 834–841. doi: 10.11999/JEIT200570

    ZHAO Chen, QIAO Gang, and ZHOU Feng. Underwater cooperative target localization method based on double orthogonal moving autonomous underwater vehicles[J]. Journal of Electronics &Information Technology, 2021, 43(3): 834–841. doi: 10.11999/JEIT200570
    [18] BAI Nan, TIAN Yuan, LIU Ye, et al. A high-precision and low-cost IMU-based indoor pedestrian positioning technique[J]. IEEE Sensors Journal, 2020, 20(12): 6716–6726. doi: 10.1109/JSEN.2020.2976102
    [19] SAN MARTÍN J, CORTÉS A, ZAMORA-CADENAS L, et al. Precise positioning of autonomous vehicles combining UWB ranging estimations with on-board sensors[J]. Electronics, 2020, 9(8): 1238. doi: 10.3390/electronics9081238
    [20] MAGNAGO V, CORBALÁN P, PICCO G P, et al. Robot localization via odometry-assisted Ultra-wideband ranging with stochastic guarantees[C]. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019: 1607–1613.
    [21] GUO Kexin, QIU Zhirong, MENG Wei, et al. Ultra-wideband based cooperative relative localization algorithm and experiments for multiple unmanned aerial vehicles in GPS denied environments[J]. International Journal of Micro Air Vehicles, 2017, 9(3): 169–186. doi: 10.1177/1756829317695564
    [22] LIU Ran, YUEN C, DO T N, et al. Cooperative relative positioning of mobile users by fusing IMU inertial and UWB ranging information[C]. 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017: 5623–5629.
    [23] HARDY J, STRADER J, GROSS J N, et al. Unmanned aerial vehicle relative navigation in GPS denied environments[C]. 2016 IEEE/ION Position, Location and Navigation Symposium (PLANS), Savannah, USA, 2016: 344–352.
    [24] CHOU C, WANG Di, SONG Dezhen, et al. On the tunable sparse graph solver for pose graph optimization in visual SLAM problems[C]. 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019: 1300–1306.
    [25] ASCHER C, KESSLER C, WANKERL M, et al. Dual IMU indoor navigation with particle filter based map-matching on a smartphone[C]. 2010 International Conference on Indoor Positioning and Indoor Navigation, Zurich, Switzerland, 2010: 1–5.
    [26] THRUN S, BURGARD W, and FOX D. Probabilistic Robotics[M]. Cambridge: The MIT Press, 2005.
    [27] SCHOUTEN G and STECKEL J. RadarSLAM: Biomimetic SLAM using ultra-wideband pulse-echo radar[C]. 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan, 2017: 1–8.
  • 加载中
图(9) / 表(2)
计量
  • 文章访问数:  880
  • HTML全文浏览量:  461
  • PDF下载量:  118
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-08-10
  • 修回日期:  2021-10-01
  • 网络出版日期:  2021-11-19
  • 刊出日期:  2022-10-19

目录

    /

    返回文章
    返回