摘要
提出了无人机与智能车的协同导航系统,该系统选取了基于特征匹配的图像拼接技术与基于霍夫变换的图形识别方法,实现了场景还原和障碍物的识别;提出了基于A*算法的路径规划策略,实现了智能车的避障;与传统的智能车循迹相比,该系统通过设计合理的传感器安装位置以及智能车循迹算法,以最少的传感器实现了自主循迹功能,并以超声波测距的方式完成了对移动障碍物的躲避.仿真实验表明,该系统可以实现对无人、狭小、幽闭空间的探测及数据传输等相关操作.
A method for cooperative navigation based on quad-rotor unmanned helicopter and intelligent vehicle was presented. Firstly, with the image mosaic technology and Hough transform theory, the designed system is able to rebuild the real scene and identify obstacles. Secondly, a new method based on the A ＊ algorithm is proposed to provide a reasonable path for the vehicle to avoid obstacles. Compared with traditional tracking method, the vehicle uses fewer sensors to achieve the goal point by the proposed method. Thirdly, vehicle can stop automatically when it detects any moving obstacles by ultrasonic. The simulation shows the result that the system can be used in unmanned and narrow environment.
出处
《沈阳大学学报（自然科学版）》
CAS
2015年第5期385-389,共5页
Journal of Shenyang University：Natural Science
基金
国家自然科学基金资助项目(61473073)
中央高校基本科研业务费(N130417006
L1517004)
中国博士后自然科学基金(2013T60294)
辽宁省高等学校优秀人才支持计划(LJQ2014028)
关键词
无人机
智能车
图像处理
路径规划
智能循迹
协同导航
unmanned helicopter
intelligent vehicle
image processing
path planning
intelligent tracking method
cooperative navigation
