Local-bearing-information-based unmanned aerial vehicle collision avoidance trajectory planning

Due to its low cost, low power, and non-cooperative advantage, monocular vision has become a crucial solution for enhancing the Sense and Avoid capability of unmanned aerial vehicles (UAVs). However, due to the restricted optical measurement properties of monocular vision, there are drawbacks to monocular-vision-based obstacle perception; namely, the capability for obstacle range information perception and global obstacle perception is insufficient. In this paper, a local-bearing-angle-based trajectory planning algorithm is proposed for UAV collision avoidance. First, the observability of monocular-vision-based obstacle range perception is theoretically analyzed. The local coordinate system is selected for collision avoidance, and the bearing angle is selected as the observation. Second, the problem of UAV monocular vision collision avoidance trajectory planning is mathematically formulated. The quadratic Bezier curve is selected to model the trajectory, and the trajectory planning problem is transformed into optimization based on a single objective function. The collision avoidance constraint based on the relative bearing angle rate is designed, and the collision avoidance trajectory segment is optimized on the basis of the bearing angle observed within a limited time period. Finally, the receding horizon is adopted to optimize the trajectory sequence, and the sequences are connected to formulate the near-globally optimal trajectory. Simulation experiments reveal that the collision avoidance trajectory planning method is capable of avoiding both static and dynamic obstacles. Compared with global-information-based collision avoidance, the convergence time of the method proposed in this paper is reduced, and the near-globally optimal trajectory can be acquired in real time, which is in accordance with the perception capability of monocular vision. © 2021, Science Press. All right reserved.