Autonomous Exploration of Unknown Indoor Environments Using Multi-UAV System

This paper introduces an autonomous exploration approach for mapping unknown indoor environments using a co-ordinated system of multiple UAVs (Unmanned Aerial Vehicles). The approach is based on a master-slave architecture, where a central master drone manages the actions of several slave drones to ensure maximum coverage and efficiency. This setup enables real-time communication and dynamic path planning, allowing the drone swarm to efficiently navigate complex indoor environments, avoid obstacles, and gather accurate mapping data. The algorithm is implemented using the Robot Operating System (ROS) and integrated with the Gazebo and PX4 simulators, making it easy to transition from simulation to real-world deployment. Simulations carried out in warehouse and office environments reveal that the UAV swarm clearly outperforms traditional single-drone approaches in mapping accuracy, exploration speed, and energy efficiency. These observations highlight the potential of the system in real-world applications such as disaster response, architectural planning, and inventory management-situations that are time-sensitive and demand precise spatial data. Overall, the study underscores the benefits of collaborative UAV systems and lays a strong foundation for future progress in autonomous indoor mapping and navigation powered by swarm technology.