{"ID":2841009,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.12618","arxiv_id":"2511.12618","title":"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.","short_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 the...","url_abs":"https://arxiv.org/abs/2511.12618","url_pdf":"https://arxiv.org/pdf/2511.12618v1","authors":"[\"Jordan Leyva\",\"Nahim J. Moran Vera\",\"Yihan Xu\",\"Adrien Durasno\",\"Christopher U. Romero\",\"Tendai Chimuka\",\"Gabriel O. Huezo Ramirez\",\"Ziqian Dong\",\"Roberto Rojas-Cessa\"]","published":"2025-11-16T14:27:21Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}