Feature selection-based decision model for UAV path planning on rough terrains

Path planning and obstacle avoidance in 3D terrain have been identified as a monumental challenge for a UAV in a variety of autonomous missions, such as disaster management, and search and rescue operations. In large terrain areas, it is a key problem for traditional approaches to search within the point-cloud maps to find a global path for a UAV considering the flight safety, maneuverability, weather constraints, and fuel cost. Hence, this paper proposes a trajectory planning technique for global and local path planning of a fixed-wing UAV above 3D terrain under static and dynamic constraints. For global path generation, a novel feature selection-based decision model has been proposed to select the features of a point-cloud map and transform them into the feature set. The feature set is utilized by an A* multi-directional planner with an extensive search area to deliver an optimal global path. The global path is assumed as the UAV's reference waypoints. The motion of the UAV on reference waypoints is simplified with two coordinates (R, d), where R is the cumulative distance covered by the UAV along the reference waypoints and d is its offset distance from the reference line segment in time t. For the local path planning, offset trajectories are generated along with reference waypoints to avoid collisions. Cost functions have been added so that the best global and local path can be chosen, taking into account altitude, weather, and fuel constraints. The simulation results and comparison show that the proposed approach outperforms various other 3D UAV path planning techniques in complex terrain.

Automated summary

This paper presents a trajectory planning technique for global and local path planning of a UAV in 3D terrain. It proposes a feature selection-based decision model and an A* multi-directional planner with an extensive search area to generate optimal global paths. Offset trajectories are also generated for local path planning to avoid collisions. The simulation results show that this approach outperforms other 3D UAV path planning techniques.