Autonomous exploration enables unmanned aerial vehicles (UAVs) to map unknown environments without human intervention. While state-of-the-art algorithms primarily focus on maximizing coverage or minimizing exploration time, they often overlook energy efficiency, a critical constraint for battery-powered UAVs. This thesis introduces EAAE, a modular Energy-Aware Autonomous Exploration framework that explicitly incorporates energy consumption into the exploration decision-making process. By combining frontier detection, a K-means divisive clustering algorithm, energy-aware target selection, and dual-layer planning, the framework balances information gain with energy cost. The algorithm is evaluated in simulation using the Agilicious control stack, with a physically realistic power model based on rotor speeds. Two 3D environments of varying complexity are used to compare EAAE against two baselines, an information-gain-based frontier method and distance-based frontier methods. Results show that EAAE consistently achieves the lowest total energy consumption while maintaining competitive or lower exploration times. Moreover, it demonstrates similar performance in terms of information entropy and reduced power variability, contributing to more efficient and robust mapping. This work highlights the importance of integrating energy-awareness into exploration pipelines and provides a foundation for further research into long-endurance autonomous aerial missions.

Autonomous UAV swarms have great potential for applications such as search‐and‐rescue, wildfire monitoring, and environmental mapping, but rapid and reliable coverage of large, obstacle‐filled areas remains challenging. In this paper, we ask: How can decentralised multi-UAV systems be optimised for large-scale priority-aware exploration? To answer this, we present the Fast LIDAR-based Autonomous Multi-UAV Explorer (FLAME), a hierarchical framework for efficient swarm exploration with long‐range, 360° LIDAR sensors. Locally, a dual‐mode exploration planner alternates between high‐speed straight‐line travel and frontier-cost‐based exploration. Globally, each UAV’s mission is optimised to visit user‐defined high‐priority regions early while minimising total mission time, enabling decentralised coordination with minimal duplicate exploration. FLAME also integrates battery‐constrained multi‐mission planning, dividing large tasks into multiple battery‐feasible missions with timely returns to the ground station for recharging or battery swaps. In simulation, FLAME achieves 36% lower mission time compared to state-of-the-art. A high-fidelity simulation confirms the framework’s ability to effectively prioritise user-assigned regions while adhering to operational constraints.

Unmanned Aerial Vehicles (UAVs) play a crucial role in various applications, including disaster response, infrastructure inspection, and search-and-rescue missions. To maximise their effectiveness, UAVs must achieve a high level of autonomy, particularly when navigating cluttered environments. This requires real-time collision avoidance algorithms that efficiently utilise on-board computational resources. Existing approaches either maintain explicit map representations, which provide memory of past observations but require significant computation, or directly process depth images, which are computationally efficient but restrict flight paths to the sensor’s field of view (FOV). This thesis presents a novel, lightweight collision avoidance algorithm that operates directly on depth images while retaining memory of past observations. By aggregating previous depth data, the algorithm enables UAVs to plan trajectories beyond the FOV of onboard sensors without the computational overhead of explicit map maintenance. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods in terms of computational efficiency, flight speed, energy cost, and path length until collision.