[1]徐耀松,王传为.果蝇算法和改进D-S证据理论的四轴飞行器障碍辨识[J].智能系统学报,2020,15(3):499-506.[doi:10.11992/tis.201809011]
 XU Yaosong,WANG Chuanwei.FOA and improved D-S evidence theory for quadcopter obstacle identification[J].CAAI Transactions on Intelligent Systems,2020,15(3):499-506.[doi:10.11992/tis.201809011]
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果蝇算法和改进D-S证据理论的四轴飞行器障碍辨识(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年3期
页码:
499-506
栏目:
学术论文—智能系统
出版日期:
2020-05-05

文章信息/Info

Title:
FOA and improved D-S evidence theory for quadcopter obstacle identification
作者:
徐耀松 王传为
辽宁工程技术大学 电气与控制工程学院,辽宁 葫芦岛 125105
Author(s):
XU Yaosong WANG Chuanwei
College of Electrical and Control Engineering, Liaoning Technical University, Huludao 125105, China
关键词:
四轴飞行器避障超声波传感器红外测距传感器激光雷达传感器多传感器信息融合果蝇算法D-S证据理论
Keywords:
quadcopterobstacle avoidanceultrasonic sensorinfrared distance sensorlidar sensormultisensor information fusionFOAD-S evidence theory
分类号:
TP14
DOI:
10.11992/tis.201809011
摘要:
针对四轴飞行器对障碍辨识效果差,精度低的问题,研究了四轴飞行器障碍辨识的方法. 采用超声波传感器、红外测距传感器以及激光雷达传感器的多传感器信息融合的方法, 通过果蝇算法对传感器原始数据证据权进行优化,得到最优权值,按照各个传感器的最优权值,采用改进的D-S证据理论算法对多传感器的数据进行融合, 提高四轴飞行器的障碍辨识精度. 通过分别对单一传感器以及和其他数据融合算法实验对比,研究结果表明: 在相同条件下,本文提出的方法对障碍物的识别准确率更高,对障碍物的响应更加迅速.
Abstract:
Aiming at the problem that the quadrilateral aircraft has poor recognition effect and low precision, we studied the method of quadcopter obstacle recognition using a multisensor based on an ultrasonic sensor, infrared ranging sensor, and lidar sensor. The original data evidence weight of the sensor was optimized using the fruit-fly optimization algorithm (FOA) to obtain the optimal weight. According to the optimal weight of each sensor, an improved D-S evidence theory algorithm was used to fuse the data of multiple sensors to improve the obstacle recognition accuracy of the quadcopter. By comparing the single sensor and other data fusion algorithms, the research results show that under the same condition, the proposed method has a higher recognition accuracy for obstacles and faster response to obstacles.

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备注/Memo

备注/Memo:
收稿日期:2018-09-07。
作者简介:徐耀松,副教授,主要研究方向为煤矿瓦斯灾害前兆预测、电气线路故障诊断、工业远程监控系统开发、嵌入式系统设计。主持及参与国家自然科学基金、辽宁省教育厅基金、企业项目多项。辽宁省优秀青年骨干教师,获得辽宁省科技进步二等奖、中国煤炭工业协会科学技术二等奖、国家安全生产总局安全生产科技成果三等奖、阜新市科技进步一等奖、辽宁省普通高等教育本科教学成果三等奖。发表学术论文20余篇;王传为,硕士研究生,主要研究方向为信息处理与模式识别
通讯作者:王传为.E-mail:2415788230@qq.com
更新日期/Last Update: 1900-01-01