Autonomous Exploration
Autonomous exploration focuses on enabling robots to independently map and navigate unknown environments, optimizing efficiency and completeness of coverage. Current research emphasizes efficient mapping techniques using various sensor modalities (LiDAR, RGB-D cameras), incorporating semantic information for improved understanding, and leveraging advanced algorithms like deep reinforcement learning, active inference, and graph-based methods for planning optimal exploration trajectories. This field is crucial for advancing robotics in diverse applications, including search and rescue, planetary exploration, and industrial inspection, by enabling robots to operate effectively in unstructured and dynamic settings.
Papers
Maritime Vessel Tank Inspection using Aerial Robots: Experience from the field and dataset release
Mihir Dharmadhikari, Nikhil Khedekar, Paolo De Petris, Mihir Kulkarni, Morten Nissov, Kostas Alexis
Risk-Aware Coverage Path Planning for Lunar Micro-Rovers Leveraging Global and Local Environmental Data
Shreya Santra, Kentaro Uno, Gen Kudo, Kazuya Yoshida