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This research presents a lightweight neural network architecture specifically designed to predict and avoid dead ends in vision-based UAV navigation using RGB-D inputs. By pruning trajectory libraries based on these predictions, the system enables high-frequency replanning at 50 Hz without requiring manual annotation or fine-tuning for new real-world scenarios. The approach demonstrates significant improvements in success rates and flight efficiency across both simulation and physical experiments, addressing a critical bottleneck in autonomous aerial systems.
DPNet: Efficient Dead-End Prediction and Avoidance for Vision-Based UAV Navigation
arXiv:2608.16640v1 Announce Type: new Abstract: Vision-based Unmanned Aerial Vehicles (UAVs) often suffer from navigation failures in dead ends due to limited sensing accuracy and range. To address this challenge, this paper proposes a systematic solution for efficient dead-end prediction and avoidance. The proposed method introduces a lightweight neural network to predict the relative distance and bearing of potential dead ends within the current field of view using RGB-D inputs. These predictions prune a predefined, compact trajectory library, enabling the planner to proactively avoid dead ends while maintaining navigational smoothness. Notably, our approach transfers across real-world scenarios without manual annotation or fine-tuning on real-world data. The system achieves high-frequency replanning at 50 Hz onboard. Extensive simulation benchmarks demonstrate superior performance in success rate, flight time, and trajectory length, and real-world experiments further validate its effectiveness in complex scenarios.
Abstract:Vision-based Unmanned Aerial Vehicles (UAVs) often suffer from navigation failures in dead ends due to limited sensing accuracy and range. To address this challenge, this paper proposes a systematic solution for efficient dead-end prediction and avoidance. The proposed method introduces a lightweight neural network to predict the relative distance and bearing of potential dead ends within the current field of view using RGB-D inputs. These predictions prune a predefined, compact trajectory library, enabling the planner to proactively avoid dead ends while maintaining navigational smoothness. Notably, our approach transfers across real-world scenarios without manual annotation or fine-tuning on real-world data. The system achieves high-frequency replanning at 50 Hz onboard. Extensive simulation benchmarks demonstrate superior performance in success rate, flight time, and trajectory length, and real-world experiments further validate its effectiveness in complex scenarios.
Robotics (cs.RO)
arXiv:2608.16640 [cs.RO]
(or arXiv:2608.16640v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2608.16640 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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