EcoFlight: Finding Low-Energy Paths Through Obstacles for Autonomous Sensing Drones
Abstract
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.
Cite
@article{arxiv.2511.12618,
title = {EcoFlight: Finding Low-Energy Paths Through Obstacles for Autonomous Sensing Drones},
author = {Jordan Leyva and Nahim J. Moran Vera and Yihan Xu and Adrien Durasno and Christopher U. Romero and Tendai Chimuka and Gabriel O. Huezo Ramirez and Ziqian Dong and Roberto Rojas-Cessa},
journal= {arXiv preprint arXiv:2511.12618},
year = {2025}
}
Comments
Autonomous drone, A* algorithm, 3D environments, path planning, obstacle avoidance, energy efficiency, MIT Conference