Abstract
This paper proposes an innovative and efficient three-dimensional (3D) autonomous obstacle algorithm for unmanned aerial vehicles (UAVs) which works by generating circular arc trajectories to avoid obstacles. Firstly, information on irregular obstacles is obtained by an onboard detection system; this information is then transformed into standard convex bodies, which are used to generate circular arc avoidance trajectories, and the obstacle avoidance problem is turned into a trajectory tracking strategy. Then, on the basis of the geometric relationship between a UAV and obstacle modeling, the working mechanism of the avoidance algorithm is developed. The rules of obstacle detection, avoidance direction, and the criterion of avoidance success are defined for different obstacle types. Finally, numerical simulations of different obstacle scenarios show that the proposed algorithm can avoid static and dynamic obstacles effectively and can implement obstacle avoidance missions for UAVs well.
Highlights
unmanned aerial vehicles (UAVs) are widely used in military and civilian fields because of their outstanding advantages, such as flexibility, light weight, strong mobility, and good concealment [1]
The real-time autonomous obstacle avoidance problem studied in this article refers to the avoidance of various collisions during flight through the target path by avoidance actions, which are calculated according to the obstacle information based on known data or data coming from an onboard detection system in real time, as shown in the difficulty of this problem lies in the irregularity, dynamics, and complexity of obstacles and three-dimensional space
With the advances of research in this field, many significantly improved path planning algorithms have been proposed, including the Voronoi diagram (VD) [6] based on graph theory, artificial potential field (APF) [7, 8] based on field theory, the RRT [9] algorithm based on sampling theory, the A ∗ [10] algorithm based on heuristic information, and other algorithms based on swarm intelligence optimization theories [11,12,13]
Summary
UAVs are widely used in military and civilian fields because of their outstanding advantages, such as flexibility, light weight, strong mobility, and good concealment [1]. The real-time autonomous obstacle avoidance problem studied in this article refers to the avoidance of various collisions during flight through the target path by avoidance actions, which are calculated according to the obstacle information based on known data or data coming from an onboard detection system in real time, as shown in. With the advances of research in this field, many significantly improved path planning algorithms have been proposed, including the Voronoi diagram (VD) [6] based on graph theory, artificial potential field (APF) [7, 8] based on field theory, the RRT [9] algorithm based on sampling theory, the A ∗ [10] algorithm based on heuristic information, and other algorithms based on swarm intelligence optimization theories [11,12,13]
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