EAAE: Energy-Aware Autonomous Exploration for UAVs in Unknown 3D Environments
2026-03-16 • Robotics
Robotics
AI summaryⓘ
The authors focus on helping drones explore 3D spaces more efficiently by saving battery power. They created a system called Energy-Aware Autonomous Exploration (EAAE) that plans drone routes based on how much energy each move will use, not just on coverage or speed. Their method groups exploration targets and picks paths that use less energy while still covering new areas safely. Tests in simulations showed EAAE uses less battery than other common methods without taking more time or reducing map quality. This makes drone exploration smarter about energy without losing effectiveness.
multirotor UAV3D explorationfrontier-based explorationenergy-aware planningtrajectory planningpower estimationinformation gaindynamic feasibilitymappingautonomous navigation
Authors
Jacob Elskamp, Moji Shi, Leonard Bauersfeld, Davide Scaramuzza, Marija Popović
Abstract
Battery-powered multirotor unmanned aerial vehicles (UAVs) can rapidly map unknown environments, but mission performance is often limited by energy rather than geometry alone. Standard exploration policies that optimise for coverage or time can therefore waste energy through manoeuvre-heavy trajectories. In this paper, we address energy-aware autonomous 3D exploration for multirotor UAVs in initially unknown environments. We propose Energy-Aware Autonomous Exploration (EAAE), a modular frontier-based framework that makes energy an explicit decision variable during frontier selection. EAAE clusters frontiers into view-consistent regions, plans dynamically feasible candidate trajectories to the most informative clusters, and predicts their execution energy using an offline power estimation loop. The next target is then selected by minimising predicted trajectory energy while preserving exploration progress through a dual-layer planning architecture for safe execution. We evaluate EAAE in a full exploration pipeline with a rotor-speed-based power model across simulated 3D environments of increasing complexity. Compared to representative distance-based and information gain-based frontier baselines, EAAE consistently reduces total energy consumption while maintaining competitive exploration time and comparable map quality, providing a practical drop-in energy-aware layer for frontier exploration.