EcoFlight: Finding Low-Energy Paths Through Obstacles for Autonomous Sensing Drones

TL;DR

EcoFlight is an energy-efficient path planning algorithm for drones that finds low-energy routes through obstacles by modeling energy consumption and optimizing flight paths, significantly reducing energy use in complex environments.

Contribution

This paper introduces EcoFlight, a novel energy-aware pathfinding algorithm for UAVs that considers drone dynamics and obstacle density to minimize energy consumption.

Findings

EcoFlight reduces energy consumption compared to direct and shortest-distance paths.

Performance improves with optimal flying speed in obstacle-rich environments.

EcoFlight outperforms existing schemes especially in high-density obstacle scenarios.

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.