基于多无人机协同的核辐射检测系统设计
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1.东华理工大学电子与电气工程学院 南昌 330013; 2.西南科技大学信息与控制工程学院 绵阳 621010

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TN98

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国家自然科学基金(62341301,12165001)、西南科技大学特殊环境机器人技术四川省重点实验室开放课题(23kftk06)、智能感知与控制四川省重点实验室开放基金(2023RYY02)、东华理工大学研究生创新专项基金(DHYC-202443)项目资助


Research on nuclear radiation detection system based on multi-UAV cooperation
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1.School of Electronic and Electrical Engineering, East China University of Technology,Nanchang 330013, China; 2.School of Information and Control Engineering, Southwest University of Science and Technology,Mianyang 621010, China

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    摘要:

    针对传统单无人机搭载核辐射探测器的检测系统检测精度不高、数据波动大以及鲁棒性不强等问题,文章构建了一种基于多无人机协同的核辐射检测系统。首先,设计了一种多无人机编队控制算法,实现了对多无人机协同控制;其次,改进了一种多传感器扩展卡尔曼数据融合机制,将多个传感器采集到的辐射数据融合成一个更加精确的辐射数据,提高了系统的检测精度;最后,将系统成功部署到了实物平台上,进行了系统的可行性验证。实验结果表明,该系统较单传感器系统可以将检测误差降低50%左右,同时提升了系统的鲁棒性,且改进的扩展卡尔曼数据融合算法较普通的扩展卡尔曼数据融合算法将融合误差降低了21%左右。

    Abstract:

    Aiming at the problems of low detection accuracy, large data fluctuation and weak robustness of the detection system of traditional single UAV equipped with nuclear radiation detector, this paper constructs a nuclear radiation detection system based on multi-UAV cooperation. Firstly, a multi-UAV formation control algorithm is designed to realize the cooperative control of multi-UAV. Secondly, a multi-sensor extended Kalman data fusion mechanism is improved to fuse the radiation data collected by multiple sensors into a more accurate radiation data, which improves the detection accuracy of the system. Finally, the system is successfully deployed to the physical platform, and the feasibility of the system is verified. The experimental results show that the system can reduce the detection error by about 50% compared with the single sensor system, and improve the robustness of the system. The improved extended Kalman data fusion algorithm can reduce the fusion error by about 21% compared with the ordinary extended Kalman data fusion algorithm.

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刘国权,王运文,周书民,楚红雨.基于多无人机协同的核辐射检测系统设计[J].电子测量技术,2026,49(5):1-8

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  • 在线发布日期: 2026-05-08
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