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Path Planning and Navigation Inside Off-World Lava Tubes and Caves

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arxiv 1803.02818 v1 pith:VDNBJNYU submitted 2018-03-07 cs.RO

classification cs.RO
keywords cavesnavigationrobotspherexcavecave-likecooperativedetailed
verification ladder T0 review T1 audit T2 compute T3 formal

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Detailed surface images of the Moon and Mars reveal hundreds of cave-like openings. These cave-like openings are theorized to be remnants of lava-tubes and their interior maybe in pristine conditions. These locations may have well preserved geological records of the Moon and Mars, including evidence of past water flow and habitability. Exploration of these caves using wheeled rovers remains a daunting challenge. These caves are likely to have entrances with caved-in ceilings much like the lava-tubes of Arizona and New Mexico. Thus, the entrances are nearly impossible to traverse even for experienced human hikers. Our approach is to utilize the SphereX robot, a 3 kg, 30 cm diameter robot with computer hardware and sensors of a smartphone attached to rocket thrusters. Each SphereX robot can hop, roll or fly short distances in low gravity, airless or low-pressure environments. Several SphereX robots maybe deployed to minimize single-point failure and exploit cooperative behaviors to traverse the cave. There are some important challenges for navigation and path planning in these cave environments. Localization systems such as GPS are not available nor are they easy to install due to the signal blockage from the rocks. These caves are too dark and too large for conventional sensor such as cameras and miniature laser sensors to perform detailed mapping and navigation. In this paper, we identify new techniques to map these caves by performing localized, cooperative mapping and navigation.

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Cited by 1 Pith paper

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  1. Think Hierarchically, Act Dynamically: Hierarchical Multi-modal Fusion and Reasoning for Vision-and-Language Navigation

    cs.CV 2025-04 conditional novelty 5.0 of 10

    MFRA combines a hierarchical DIRformer-style fusion backbone with instruction-guided attention and a GRU history encoder, reporting improved VLN benchmark scores.

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