Unknown Indoor

Research on unknown indoor environments focuses on enabling autonomous robots, particularly drones and ground robots, to navigate and map these spaces efficiently. Current efforts leverage deep reinforcement learning, coupled with advanced mapping techniques like occupancy grid and semantic mapping, to improve exploration strategies and reduce redundant searches. These advancements are crucial for applications ranging from search and rescue to assistive technologies for visually impaired individuals, improving both the speed and robustness of autonomous navigation in complex indoor settings. The development of energy-efficient algorithms for swarms of robots is also a key area of investigation, addressing limitations in battery life and computational power.