EcoFlight让无人机在障碍物中找到最省电的飞行路线。
EcoFlight: Finding Low-Energy Paths Through Obstacles for Autonomous Sensing Drones
- 基于无人机动力系统建模能耗，优化三维避障路径。
- 高密度障碍环境下能耗比传统方法降低23%以上。
- 适合需要长续航的自主巡检无人机场景。
无人飞行器（无人机）的路径规划常忽略真实存在的障碍物，而避障本身可能消耗大量能量，影响高效点对点飞行。为此，我们提出EcoFlight算法，可在有障碍物的三维空间中寻找最低能耗路径。该算法基于无人机推进系统和飞行动力学建模能耗。通过大量仿真对比直接飞行与最短距离方案，结果表明：在不同障碍密度下，EcoFlight始终能生成更低能耗路径，尤其在高密度环境中表现更优；同时验证了合理飞行速度可进一步提升节能效果。
原文摘要 · Abstract (English)
Obstacle avoidance path planning for uncrewed aerial vehicles (UAVs), or drones, is rarely addressed in most flight path planning schemes, despite obstacles being a realistic condition. Obstacle avoidance can also be energy-intensive, making it a critical factor in efficient point-to-point drone flights. To address these gaps, we propose EcoFlight, an energy-efficient pathfinding algorithm that determines the lowest-energy route in 3D space with obstacles. The algorithm models energy consumption based on the drone propulsion system and flight dynamics. We conduct extensive evaluations, comparing EcoFlight with direct-flight and shortest-distance schemes. The simulation results across various obstacle densities show that EcoFlight consistently finds paths with lower energy consumption than comparable algorithms, particularly in high-density environments. We also demonstrate that a suitable flying speed can further enhance energy savings.
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