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.

翻译：电池供电的多旋翼无人机能够快速测绘未知环境，但其任务性能往往受限于能量而非单纯几何因素。传统的探索策略以覆盖范围或时间为优化目标，可能因高机动性轨迹造成能量浪费。本文针对多旋翼无人机在初始未知环境中的能量感知自主三维探索问题展开研究。我们提出能量感知自主探索框架，这是一种基于前沿的模块化框架，将能量作为前沿选择过程中的显式决策变量。该框架将前沿聚类为视觉一致区域，为信息量最大的聚类规划动态可行的候选轨迹，并通过离线功率估计回路预测轨迹执行能耗。随后通过双层规划架构，在保证探索进度与安全执行的前提下，以最小化预测轨迹能量为目标选择下一目标点。我们在完整探索流程中，基于旋翼转速功率模型，在复杂度递增的模拟三维环境中评估了本框架性能。与基于距离和基于信息增益的前沿基准方法相比，本框架在保持竞争力探索时间与相当地图质量的同时，持续降低总能耗，为前沿探索提供了可直接集成的实用化能量感知层。