Uncertainty-aware Receding Horizon Exploration and Mapping using Aerial Robots @autonomousrobotslab

Uploaded January 2017 | Updated September 2026, 3 weeks ago

This video is associated with our paper:
* Christos Papachristos, Shehryar Khattak, Kostas Alexis, "Uncertainty-aware Receding Horizon Exploration and Mapping using Aerial Robots", IEEE International Conference on Robotics and Automation (ICRA), May 29-June 3, 2017, Singapore

Open-source code: github.com/unr-arl/rhem_planner

To know more about our research, please check our website: autonomousrobotslab.com

Uncertainty-aware Receding Horizon Exploration and Mapping using Aerial Robots

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$ICRA2022 Talk Autonomous Teamed Exploration of Subterranean Environments with Legged & Aerial Robots ICRA 2022 Talk on our paper: Mihir Kulkarni, Mihir Rahul Dharmadhikari, Marco Tranzatto, Samuel Zimmermann, Victor Reijgwart, Paolo De Petris, Huan Nguyen, Nikhil Vijay Khedekar, Christos Papachristos, Lionel Ott, Roland Siegwart, Marco Hutter, Kostas Alexis, Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots, IEEE International Conference on Robotics and Automation (ICRA), May 23-27, 2022, Philadelphia (PA), USA Abstract: This paper presents a novel strategy for autonomous teamed exploration of subterranean environments using legged and aerial robots. Tailored to the fact that subterranean settings, such as cave networks and underground mines, often involve complex, large-scale and multi-branched topologies, while wireless communication within them can be particularly challenging, this work is structured around the synergy of an onboard exploration path planner that allows for resilient long-term autonomy, and a multi-robot coordination framework. The onboard path planner is unified across legged and flying robots and enables navigation in environments with steep slopes, and diverse geometries. When a communication link is available, each robot of the team shares submaps to a centralized location where a multi-robot coordination framework identifies global frontiers of the exploration space to inform each system about where it should re-position to best continue its mission. The strategy is verified through a field deployment inside an underground mine in Switzerland using a legged and a flying robot collectively exploring for $45textrm{ min}$, as well as a longer simulation study with three systems. GBPlanner 2 Open-Source release: https://github.com/ntnu-arl/gbplanner_ros$

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Uncertainty-aware Receding Horizon Exploration and Mapping using Aerial Robots