Sample-based Fixed-wing UAV Obstacle Avoidance Method

Compared with the quadrotors, the fixed-wing unmanned aerial vehicles (UAVs) have additional flight constraints such as minimum turning radius, minimum flight speed, and maximum climb rate, which makes it challenging to design obstacle avoidance algorithms for the fixed-wing UAVs. In this paper, we present a novel method to generate a collision-free path based on the Dubins kinetic model. The Monte Carlo sampling algorithm is adopted to solve the optimization problem of the non-analytic path. And the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method is utilized to optimize the sampling area for accelerating the solving process. Further, an evaluation function is designed which includes the task-performing efficiency and the fuel consumption to get the optimal flight path. Finally, the simulation results demonstrate the effectiveness of the proposed method.