Fangjia Lian
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Fangjia Lian has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Qisong Yang
College of Combat Support, Rocket Force University of Engineering, Xi’an 710025, China
3 shared publications - Bangjie Li
Rocket Force University of Engineering, Xi'an, Shaanxi, China
2 shared publications - Desong Du
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
2 shared publications - Hongwei Zhu
Rocket Force University of Engineering, Xi'an, Shaanxi, China
2 shared publications - Bangjie Li
Rocket Force University of Engineering, Xi’an 710025, China
1 shared publication - Hongwei Zhu
Rocket Force University of Engineering, No. 2 Tongxin Road, Xi’an 710025, China
1 shared publication
Latest Publications
- Open Access
- 3 April 2026
- Published by Association for Computing Machinery (ACM)
- p. 119-125
End-to-End UAV Wind Disturbance Control via Strong Rank-Revealing QR Decomposition
- 3 April 2026
- Published by Association for Computing Machinery (ACM)
- p. 119-125
Open AccessEnd-to-End UAV Wind Disturbance Control via Strong Rank-Revealing QR Decomposition
- 13 December 2025
- Published by Elsevier in Pattern Recognition
- Vol. 173
A reinforcement learning framework for energy-optimal UAV path planning in wind fields
- 13 December 2025
- Published by Elsevier in Pattern Recognition
- Vol. 173
A reinforcement learning framework for energy-optimal UAV path planning in wind fields
- Open Access
WA-LPA*: An Energy-Aware Path-Planning Algorithm for UAVs in Dynamic Wind Environments
Energy optimization is crucial for unmanned aerial vehicle (UAV) path planning, particularly in complex wind-field environments. Most existing path-planning algorithms rely on simplified energy consumption models, which often fail to adequately capture the effects of wind fields. To address this limitation, a wind-adaptive lifelong planning A* algorithm (WA-LPA*) is proposed for energy-aware path planning in dynamic wind environments. WA-LPA* constructs a composite heuristic function incorporating wind-field alignment factors and integrates a hierarchical height-aware optimization strategy. Meanwhile, an adaptive replanning mechanism is designed based on the change characteristics of the wind field. Simulation experiments conducted across representative scenarios demonstrate that, compared to conventional algorithms that neglect wind-field effects, WA-LPA* achieves energy efficiency improvements of 5.9–29.4%.
Open AccessWA-LPA*: An Energy-Aware Path-Planning Algorithm for UAVs in Dynamic Wind Environments
Energy optimization is crucial for unmanned aerial vehicle (UAV) path planning, particularly in complex wind-field environments. Most existing path-planning algorithms rely on simplified energy consumption models, which often fail to adequately capture the effects of wind fields. To address this limitation, a wind-adaptive lifelong planning A* algorithm (WA-LPA*) is proposed for energy-aware path planning in dynamic wind environments. WA-LPA* constructs a composite heuristic function incorporating wind-field alignment factors and integrates a hierarchical height-aware optimization strategy. Meanwhile, an adaptive replanning mechanism is designed based on the change characteristics of the wind field. Simulation experiments conducted across representative scenarios demonstrate that, compared to conventional algorithms that neglect wind-field effects, WA-LPA* achieves energy efficiency improvements of 5.9–29.4%.