CoSafe-Nav: An Intelligent Connectivity-Aware Navigation System for UAVs in Perception-Degraded Environments

Reliable UAV navigation is challenging when direct geometric perception is limited and only sparse environmental observations are available. This paper presents CoSafe-Nav, an integrated navigation framework that uses ultra-wideband (UWB) line-of-sight (LOS) observations to construct a candidate corridor and guide trajectory generation. The framework addresses fixed-altitude planar navigation with known, pre-deployed anchors and an available UAV pose estimate. LOS-supported segments are accumulated online, and a grid-based Euclidean distance transform (EDT) provides a common clearance representation for path ranking and trajectory refinement. The path planner combines travel distance with a local inverse-distance penalty. The trajectory stage corrects interior control points using the EDT gradient, regenerates the spatial curve, and assigns execution timing. Evaluation comprises two simulation scenarios, component comparisons, an anchor-availability study, and indoor UAV trials. CoSafe-Nav completed all ten navigation tasks in each simulation scenario and four of five indoor trials. In the path-planning comparison, mean EDT corridor clearance increased from 0.76 to 0.98 m and from 0.63 to 0.91 m, accompanied by longer routes. These means describe successful tasks within each configuration. The trajectory comparison also showed a lower acceleration integral for the safety-guided method. The results support the feasibility of the integrated processing chain under the stated pre-instrumented deployment conditions.

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Publication Details

Journal
Symmetry
Published
2026-09-27
DOI
https://doi.org/10.3390/sym18101613
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

CoSafe-Nav: An Intelligent Connectivity-Aware Navigation System for UAVs in Perception-Degraded Environments

Xueyong Xu, Chenchen Fu, Xiangxiang Xing, Hengkai Zhong et al.
Symmetry
Robotic Path Planning Algorithms
article

CoSafe-Nav: An Intelligent Connectivity-Aware Navigation System for UAVs in Perception-Degraded Environments

Xueyong Xu, Chenchen Fu, Xiangxiang Xing, Hengkai Zhong, Yuhang Xu, Xiang Liu, Weiwei Wu, Jinchen Wang, Yan Lyu
article en

Abstract

Reliable UAV navigation is challenging when direct geometric perception is limited and only sparse environmental observations are available. This paper presents CoSafe-Nav, an integrated navigation framework that uses ultra-wideband (UWB) line-of-sight (LOS) observations to construct a candidate corridor and guide trajectory generation. The framework addresses fixed-altitude planar navigation with known, pre-deployed anchors and an available UAV pose estimate. LOS-supported segments are accumulated online, and a grid-based Euclidean distance transform (EDT) provides a common clearance representation for path ranking and trajectory refinement. The path planner combines travel distance with a local inverse-distance penalty. The trajectory stage corrects interior control points using the EDT gradient, regenerates the spatial curve, and assigns execution timing. Evaluation comprises two simulation scenarios, component comparisons, an anchor-availability study, and indoor UAV trials. CoSafe-Nav completed all ten navigation tasks in each simulation scenario and four of five indoor trials. In the path-planning comparison, mean EDT corridor clearance increased from 0.76 to 0.98 m and from 0.63 to 0.91 m, accompanied by longer routes. These means describe successful tasks within each configuration. The trajectory comparison also showed a lower acceleration integral for the safety-guided method. The results support the feasibility of the integrated processing chain under the stated pre-instrumented deployment conditions.

SymmetryVol. 18(10)
China Aerospace Science and Industry Corporation (China) (CN), Southeast University (CN)
Openalex Percentile: Top 14%
Robotic Path Planning Algorithms
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CoSafe-Nav: An Intelligent Connectivity-Aware Navigation System for UAVs in Perception-Degraded Environments — Xueyong Xu, Chenchen Fu, et al. · Symmetry (2026) | TGRS Research Map | TGRS