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CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge

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arxiv 2201.07067 v1 pith:VY2KEYGP submitted 2022-01-18 cs.RO

classification cs.RO
keywords robotssubterraneancerberusexplorationleggedaerialautonomouschallenge
verification ladder T0 review T1 audit T2 compute T3 formal
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Autonomous exploration of subterranean environments constitutes a major frontier for robotic systems as underground settings present key challenges that can render robot autonomy hard to achieve. This has motivated the DARPA Subterranean Challenge, where teams of robots search for objects of interest in various underground environments. In response, the CERBERUS system-of-systems is presented as a unified strategy towards subterranean exploration using legged and flying robots. As primary robots, ANYmal quadruped systems are deployed considering their endurance and potential to traverse challenging terrain. For aerial robots, both conventional and collision-tolerant multirotors are utilized to explore spaces too narrow or otherwise unreachable by ground systems. Anticipating degraded sensing conditions, a complementary multi-modal sensor fusion approach utilizing camera, LiDAR, and inertial data for resilient robot pose estimation is proposed. Individual robot pose estimates are refined by a centralized multi-robot map optimization approach to improve the reported location accuracy of detected objects of interest in the DARPA-defined coordinate frame. Furthermore, a unified exploration path planning policy is presented to facilitate the autonomous operation of both legged and aerial robots in complex underground networks. Finally, to enable communication between the robots and the base station, CERBERUS utilizes a ground rover with a high-gain antenna and an optical fiber connection to the base station, alongside breadcrumbing of wireless nodes by our legged robots. We report results from the CERBERUS system-of-systems deployment at the DARPA Subterranean Challenge Tunnel and Urban Circuits, along with the current limitations and the lessons learned for the benefit of the community.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Online Adaptive Traversability Estimation through Interaction for Unstructured, Densely Vegetated Environments

    cs.RO 2025-02 conditional novelty 6.0 of 10

    A robot can train a 3D voxel-based traversability model from scratch in situ from self-supervised collision data in about eight minutes, achieving MCC 0.63 and enabling safe point-to-point navigation in dense vegetation.

  2. Map Prediction and Generative Entropy for Multi-Agent Exploration

    cs.RO 2025-01 conditional novelty 6.0 of 10

    Prioritizing exploration tasks by disagreement among a latent diffusion model's predicted maps converges to an accurate predicted map faster than prioritizing by expected information gain.

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