EAAE: Energy-Aware Autonomous Exploration for UAVs in Unknown 3D Environments
Jacob Elskamp, Moji Shi, Leonard Bauersfeld, Davide Scaramuzza, Marija Popovi\'c

TL;DR
This paper introduces EAAE, a modular framework for energy-aware autonomous exploration of UAVs in unknown 3D environments, optimizing energy use while maintaining exploration efficiency.
Contribution
EAAE explicitly incorporates energy considerations into frontier-based exploration, clustering frontiers, planning energy-efficient trajectories, and predicting energy consumption for UAVs.
Findings
EAAE reduces total energy consumption compared to baselines.
EAAE maintains exploration time and map quality.
EAAE is effective across complex simulated environments.
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…
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Taxonomy
TopicsUAV Applications and Optimization · Robotics and Sensor-Based Localization · Robotic Path Planning Algorithms
