Complex Environment Path Planning for Unmanned Aerial Vehicles

Sensors

Jiwu Li¹  Xin Feng¹  Hongwei Yang¹  Jing Zhang¹  Geng Sun² 

[1] College of Computer Science and Technology, Chang Chun University of Science and Technology, Changchun 130022, China;College of Computer Science and Technology, Jilin University, Changchun 130012, China;

关键词: unmanned aerial vehicles;    narrow passages;    path planning;    pruning;    trajectory prediction;   

DOI  :  10.3390/s21155250

来源: DOAJ

【 摘 要 】

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT${}^{*}$. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

【 授权许可】

Unknown