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
ORBSLAM3-Enhanced Autonomous Toy Drones: Pioneering Indoor Exploration
Murad Tukan, Fares Fares, Yotam Grufinkle, Ido Talmor, Loay Mualem, Vladimir Braverman, Dan Feldman
Multi-sensory Anti-collision Design for Autonomous Nano-swarm Exploration
Mahyar Pourjabar, Manuele Rusci, Luca Bompani, Lorenzo Lamberti, Vlad Niculescu, Daniele Palossi, Luca Benini
Interactive Semantic Map Representation for Skill-based Visual Object Navigation
Tatiana Zemskova, Aleksei Staroverov, Kirill Muravyev, Dmitry Yudin, Aleksandr Panov
Autonomous Exploration and General Visual Inspection of Ship Ballast Water Tanks using Aerial Robots
Mihir Dharmadhikari, Paolo De Petris, Mihir Kulkarni, Nikhil Khedekar, Huan Nguyen, Arnt Erik Stene, Eivind Sjøvold, Kristian Solheim, Bente Gussiaas, Kostas Alexis