EAAE: Energy-Aware Autonomous Exploration for UAVs in Unknown 3D 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 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.

Figures (8)

• Figure 1: We introduce EAAE, an energy-aware decision layer for frontier-based exploration using UAVs. By integrating trajectory-level energy prediction into frontier selection, EAAE enables more energy-efficient exploration. Here, EAAE selects an energy-optimal frontier (blue) instead of the geometrically closest one (grey) under the same field of view (FoV) constraints.
• Figure 2: We introduce EAAE, a frontier-based exploration framework that enables energy-aware goal selection by predicting the energy of candidate trajectories. Our pipeline fuses UAV depth data into an OctoMap (Sec. III-A), clusters frontiers into goals (Sec. III-B), estimates energy offline (Sec. III-C), and executes the chosen goal with reactive local planning (Sec. III-D). By integrating energy awareness into planning, EAAE favours informative frontiers that are cheaper to reach.
• Figure 3: Divisive $k$-means cutoff geometry used to enforce view-consistent frontier clusters.
• Figure 4: Frontier extraction and clustering from the map representation. Left: OctoMap occupancy visualisation. Right: frontier detection and clustering, where yellow voxels denote frontier voxels and pink points denote frontier cluster centroids.
• Figure 5: Frontier exploration cycle in EAAE. We: (a) detect frontier voxels from the occupancy map, (b) cluster frontiers via divisive $k$-means into view-consistent regions, (c) filter clusters for feasibility, (d) sample collision-free viewpoints with yaw aligned toward each cluster, and (e) generate candidate global trajectories for offline energy evaluation.