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

2026-03-16

AI Summary

This work addresses the inefficiency of conventional exploration strategies for multirotor UAVs in unknown 3D environments, which often neglect energy consumption. To this end, the authors propose the EAAE framework, which uniquely integrates energy prediction deeply into the frontier selection mechanism. By leveraging viewpoint-consistent clustering to generate candidate regions and combining dynamically feasible trajectory planning with a rotor-speed-based power consumption model, EAAE jointly optimizes information gain, safety, and energy efficiency within a two-layer architecture. Experimental results demonstrate that EAAE significantly reduces total energy consumption across various complex simulated environments while maintaining exploration speed and map quality comparable to baseline methods.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• energy-aware
• autonomous exploration
• UAV
• 3D environments
• frontier-based

Innovation

• energy-aware exploration
• frontier-based planning
• UAV trajectory optimization
• power estimation
• autonomous 3D mapping