Organizations: Institute of Cyber-Systems and Control, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China. · Huzhou Institute of Zhejiang University, Huzhou 313000, China. · University of Electronic Science and Technology of China, Chengdu 611731, China. · Differential Robotics Technology Company, Hangzhou 311121, China.
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.
Figures & tables
Fig. 2 : System overview of DPNet.
Fig. 3 : Examples of automatic ground-truth labeling. (a) Valid: one inner vertex and all outer vertices are visible, so the dead-end geometry is directly identifiable. (b) Valid: the distant dead end is partially clipped in the input depth, but the same geometric visibility criterion still holds. (c) Valid: one inner vertex is visible and the out-of-view outer vertex is not occluded, providing sufficient context for shape inference. (d) Invalid: no inner vertices are visible, making the structure ambiguous (Mask=0).
Fig. 4 : Dead-end-aware collision checking.
Fig. 5 : Dead-end-aware trajectory selection. The two subfigures are consecutive snapshots. (a) The UAV bypasses the dead end. (b) After the dead end leaves the field of view, the smoothness penalty prevents the planner from selecting a trajectory that re-enters the dead end.
Fig. 6 : Planner behavior comparison in a representative simulation scenario.
Diff.
Method
Succ. (%)
Time (s)
Length (m)
Easy
EGO-Planner
80.0
14.54
23.00
Primitive-Planner
86.0
19.91
29.90
Faster
97.0
18.47
25.83
Ours
100.0
13.77
24.35
Hard
EGO-Planner
76.0
11.65
22.13
Primitive-Planner
47.0
9.06
22.34
TABLE I : Benchmark comparison under limited local perception.
Fig. 7 : The real-world experiments. (a)–(c) Key observations of true dead ends. (d) The opened control passage used for comparison.
School of Information Science and Engineering, Shandong University · Faculty of Engineering and Information Technology, University of Technology Sydney · School of Artificial Intelligence, Shandong University +1