Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots
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Abstract (arXiv)
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 45 min, as well as a longer simulation study with three systems.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Nov 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 0; 2026: 2.
7 papers.
- 2024Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robotscited by 48
- 2024Safe Guaranteed Exploration for Non-linear Systemscited by 8
- 2022UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubTcited by 7
- 2023Grid Cell-Inspired Fragmentation and Recall for Efficient Map Buildingcited by 2
- 2024Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Explorationcited by 1
- 2026PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learningcited by 1
- 2026Pro-Bench: Prompt-Robust Open-Vocabulary Visual Grounding Across Real-World Heterogeneous Environmentscited by 0
- 2026Pro-Bench: Prompt-Robust Open-Vocabulary Visual Grounding Across Real-World Heterogeneous Environments22 Sep 2026
- 2026PIMbot: A Self-Adaptive Attack Framework for Adversarial Manipulation of Multi-Robot Reinforcement Learning21 May 2026
- 2024Bayesian Optimization Framework for Efficient Fleet Design in Autonomous Multi-Robot Exploration21 Aug 2024
- 2024Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots3 May 2024
- 2024Safe Guaranteed Exploration for Non-linear Systems9 Feb 2024
- 2023Grid Cell-Inspired Fragmentation and Recall for Efficient Map Building11 Jul 2023
- 2022UAVs Beneath the Surface: Cooperative Autonomy for Subterranean Search and Rescue in DARPA SubT16 Jun 2022