Unmanned ground vehicle-unmanned aerial vehicle relative navigation robust adaptive localization algorithm

IF 1.4 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Jun Dai, Songlin Liu, Hao Xiangyang, Zongbin Ren, Xiao Yang, Yunzhu Lv
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引用次数: 0

Abstract

The unmanned aerial vehicle (UAV) and the unmanned ground vehicle (UGV) can complete complex tasks through information sharing and ensure the mission execution capability of multiple unmanned carrier platforms. At the same time, cooperative navigation can use the information interaction between multi-platform sensors to improve the relative navigation and positioning accuracy of the entire system. Aiming at the problem of deviation of the system model due to gross errors in sensor measurement data or strong manoeuvrability in complex environments, a robust and adaptive UGV-UAV relative navigation and positioning algorithm is proposed. In this paper, the relative navigation and positioning of UGV-UAV is studied based on inertial measurement unit (IMU)/Vision. Based on analyzing the relative kinematics model and sensor measurement model, a leader (UGV)-follow (UAV) relative navigation model is established. In the implementation of the relative navigation and positioning algorithm, the robust adaptive algorithm and the non-linear Kalman filter (extended Kalman filter [EKF]) algorithm are combined to dynamically adjust the system state parameters. Finally, a mathematical simulation of the accompanying and landing process in the UGV-UAV cooperative scene is carried out. The relative position, velocity and attitude errors calculated by EKF, Robust-EKF and Robust-Adaptive-EKF algorithms are compared and analyzed by simulating two cases where the noise obeys the Gaussian distribution and the non-Gaussian distribution. The results show that under the non-Gaussian distribution conditions, the accuracy of the Robust-Adaptive-EKF algorithm is improved by about two to three times compared with the EKF and Robust-EKF and can almost reach the relative navigation accuracy under the Gaussian distribution conditions. The robust self-adaptive relative navigation and positioning algorithm proposed in this paper has strong adaptability to the uncertainty and deviation of system modelling in complex environments, with higher accuracy and stronger robustness.

Abstract Image

无人机相对导航鲁棒自适应定位算法
无人机(UAV)和无人地面车(UGV)可以通过信息共享完成复杂任务,确保多个无人运载平台的任务执行能力。同时,协同导航可以利用多平台传感器之间的信息交互,提高整个系统的相对导航和定位精度。针对传感器测量数据中的粗误差或复杂环境中的强机动性导致系统模型发生偏差的问题,提出了一种鲁棒自适应的无人值守无人值守地面传感器相对导航定位算法。本文研究了基于惯性测量单元(IMU)/Vision的UGV无人机相对导航定位。在分析相对运动学模型和传感器测量模型的基础上,建立了无人机的相对导航模型。在相对导航定位算法的实现中,将鲁棒自适应算法和非线性卡尔曼滤波器(扩展卡尔曼滤波器[EKF])算法相结合,动态调整系统状态参数。最后,对UGV-UAV协同场景中的伴随和着陆过程进行了数学仿真。通过模拟噪声服从高斯分布和非高斯分布的两种情况,对EKF、鲁棒EKF和鲁棒自适应EKF算法计算的相对位置、速度和姿态误差进行了比较和分析。结果表明,在非高斯分布条件下,鲁棒自适应EKF算法的精度比EKF和鲁棒EKF提高了约两到三倍,几乎可以达到高斯分布条件的相对导航精度。本文提出的鲁棒自适应相对导航定位算法对复杂环境下系统建模的不确定性和偏差具有较强的适应性,具有较高的精度和较强的鲁棒性。
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来源期刊
Iet Science Measurement & Technology
Iet Science Measurement & Technology 工程技术-工程:电子与电气
CiteScore
4.30
自引率
7.10%
发文量
41
审稿时长
7.5 months
期刊介绍: IET Science, Measurement & Technology publishes papers in science, engineering and technology underpinning electronic and electrical engineering, nanotechnology and medical instrumentation.The emphasis of the journal is on theory, simulation methodologies and measurement techniques. The major themes of the journal are: - electromagnetism including electromagnetic theory, computational electromagnetics and EMC - properties and applications of dielectric, magnetic, magneto-optic, piezoelectric materials down to the nanometre scale - measurement and instrumentation including sensors, actuators, medical instrumentation, fundamentals of measurement including measurement standards, uncertainty, dissemination and calibration Applications are welcome for illustrative purposes but the novelty and originality should focus on the proposed new methods.
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