Volume 38, Number 2, April 2020
|238 - 245
|17 July 2020
The Obstacle-Avoidance Path Planning for UAV Based on IOCAD
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
To plan the path for UAV flying in the complex, dense and irregular obstacles environment, this paper proposed an obstacle collision-avoidance detection model and designed an UAV path planning algorithm based on irregular obstacles collision-avoidance detection (IOCAD), which includes irregular obstacles pretreatment method. The proposed method uses the grid method to model the environment. Rough set theory and convexity filling are used to pretreat the obstacles, and the ray method is used to select the available points. The intersection detection and the distance detection are held for the obstacle to the flight path. The objective function minimizes the distance from the obstacle to the flight path to get planned paths. The simulation results show that the proposed method can effectively plan the paths with the constraints of the assumed environment and UAV performances. It is shown that the performance of the proposed method is sensitive to the grid length and safety distance. The optimized values for the grid length and safety distance are 0.5 km and 0.4 km respectively.
针对复杂密集不规则障碍物环境下无人机路径规划问题，采用不规则障碍物预处理方法，建立障碍物避碰检测模型，并设计了基于不规则障碍物避碰检测（irregular obstacles collision-avoidance detection，IOCAD）的无人机路径规划算法。该算法以栅格法规划环境建模为基础，采用粗糙集思想、凸化填充法等对障碍物进行预处理，并利用射线法筛选出环境中可飞路径点，以障碍物到飞行路径距离最小为目标函数，对可飞路径段和障碍物进行相交检测与距离检测，解算出不规则障碍物环境下无人机路径。在既定的路径规划环境及无人机性能约束下，仿真结果表明：该算法能快速规划出对应避障路径，且栅格粒度大小、安全裕度值的设置对算法性能有明显影响；当栅格粒度为0.5 km，安全裕度为0.4 km时，可显著缩短航程并有效减少路径点数，验证了该算法的有效性。
Key words: irregular obstacle collision-avoidance detection(IOCAD) / UAV / path planning / rough set theory / grid method / convexity filling / ray method
关键字 : 不规则障碍物避碰检测 / 无人机 / 路径规划 / 粗糙集思想 / 栅格法 / 凸化填充 / 射线法
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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