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Energy-Optimized Planning in Non-Uniform Wind Fields with Fixed-Wing Aerial Vehicles
Apr 02, 2024
Fixed-wing small uncrewed aerial vehicles (sUAVs) possess the capability to remain airborne for extended durations and traverse vast distances. However, their operation is susceptible to wind conditions, particularly in regions of complex terrain where high wind speeds may push the aircraft beyond its operational limitations, potentially raising safety concerns. Moreover, wind impacts the energy required to follow a path, especially in locations where the wind direction and speed are not favorable. Incorporating wind information into mission planning is essential to ensure both safety and energy efficiency. In this paper, we propose a sampling-based planner using the kinematic Dubins aircraft paths with respect to the ground, to plan energy-efficient paths in non-uniform wind fields. We study the planner characteristics with synthetic and real-world wind data and compare its performance against baseline cost and path formulations. We demonstrate that the energy-optimized planner effectively utilizes updrafts to minimize energy consumption, albeit at the expense of increased travel time. The ground-relative path formulation facilitates the generation of safe trajectories onboard sUAVs within reasonable computational timeframes.
Safe Low-Altitude Navigation in Steep Terrain with Fixed-Wing Aerial Vehicles
Jan 09, 2024Fixed-wing aerial vehicles provide an efficient way to navigate long distances or cover large areas for environmental monitoring applications. By design, they also require large open spaces due to limited maneuverability. However, strict regulatory and safety altitude limits constrain the available space. Especially in complex, confined, or steep terrain, ensuring the vehicle does not enter an inevitable collision state(ICS) can be challenging. In this work, we propose a strategy to find safe paths that do not enter an ICS while navigating within tight altitude constraints. The method uses periodic paths to efficiently classify ICSs. A sampling-based planner creates collision-free and kinematically feasible paths that begin and end in safe periodic (circular) paths. We show that, in realistic terrain, using circular periodic paths can simplify the goal selection process by making it yaw agnostic and constraining yaw. We demonstrate our approach by dynamically planning safe paths in real-time while navigating steep terrain on a flight test in complex alpine terrain.