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This research advances autonomous UAV navigation in degraded environments by replacing conservative convex safety margins with non-convex, shape-conforming regions derived from signed distance fields. By leveraging complementary sensor observations and adaptive visibility metrics without requiring ground-truth feedback, the method significantly increases usable free space while maintaining distribution-free collision guarantees. The approach represents a meaningful step forward for machine learning in robotics operating within confined spaces like caves or collapsed structures where perception is inherently limited.
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SCORE: Shape-Conforming Regions for Flight in Enclosed, Degraded Environments

arXiv2 min read

arXiv:2608.15289v1 Announce Type: new Abstract: Autonomous UAVs enter enclosed environments such as caves and collapsed structures that confine the vehicle and degrade perception. Conformal prediction provides a distribution-free guarantee by calibrating how far an obstacle keep-out must expand to absorb perception error at a target coverage level. However, existing keep-out regions use convex primitives whose bulges consume narrow passages and grow as perception degrades. Our main contribution defines the nonconformity score on a signed distance field (SDF). This produces a non-convex keep-out that tightly follows obstacle geometry and avoids the unnecessary bulging of equal-margin convex regions. Two supporting components keep this geometry usable as perception degrades. First, a voxelwise union of complementary sensor observations certifies voxels that any single sensor misses. Second, the margin around the obstacle adapts to measured visibility without weather labels or the online ground-truth feedback that single-pass flight cannot provide. Results on real subterranean data show that the resulting distribution-free, shape-conforming keep-out retains more usable free space than convex baselines at the same certified coverage, and produces safer closed-loop flight.

Abstract:Autonomous UAVs enter enclosed environments such as caves and collapsed structures that confine the vehicle and degrade perception. Conformal prediction provides a distribution-free guarantee by calibrating how far an obstacle keep-out must expand to absorb perception error at a target coverage level. However, existing keep-out regions use convex primitives whose bulges consume narrow passages and grow as perception degrades. Our main contribution defines the nonconformity score on a signed distance field (SDF). This produces a non-convex keep-out that tightly follows obstacle geometry and avoids the unnecessary bulging of equal-margin convex regions. Two supporting components keep this geometry usable as perception degrades. First, a voxelwise union of complementary sensor observations certifies voxels that any single sensor misses. Second, the margin around the obstacle adapts to measured visibility without weather labels or the online ground-truth feedback that single-pass flight cannot provide. Results on real subterranean data show that the resulting distribution-free, shape-conforming keep-out retains more usable free space than convex baselines at the same certified coverage, and produces safer closed-loop flight.

8 pages, 4 figures. Technical appendix available on request

Robotics (cs.RO)

arXiv:2608.15289 [cs.RO]

(or arXiv:2608.15289v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.15289 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 271 words

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