TY - GEN
T1 - Multi-constrained trajectory planning for random signal aided navigation
AU - Hong, Xiaotong
AU - Xv, Li
AU - Yun, Hongquan
AU - Tian, Zhenpo
AU - Zhang, Qiheng
AU - Wang, Yue
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/27
Y1 - 2020/11/27
N2 - UAV can't perform combat missions without the trajectory planning system and the navigation system.Traditional UAV trajectory planning systems are often separated from navigation systems. At the same time, UAV active navigation and scene matching assisted navigation have the problems of low safety and poor robustness. In this paper, a new trajectory planning method for random signal aided navigation environment is proposed. First, the random signal navigation environment model and the trajectory planning constraint model are established. Then, the algorithm of selecting matching area and multi constraint trajectory planning between matching areas are proposed. The simulation results show that the matching area selection algorithm can select more matching areas, and the planned route can pass more matching areas. This method combines UAV trajectory planning with a new navigation method (Random Signal Aided Navigation), which improves the diversity and accuracy of navigation and makes the trajectory planning system more robust. It has great research significance and practical value.
AB - UAV can't perform combat missions without the trajectory planning system and the navigation system.Traditional UAV trajectory planning systems are often separated from navigation systems. At the same time, UAV active navigation and scene matching assisted navigation have the problems of low safety and poor robustness. In this paper, a new trajectory planning method for random signal aided navigation environment is proposed. First, the random signal navigation environment model and the trajectory planning constraint model are established. Then, the algorithm of selecting matching area and multi constraint trajectory planning between matching areas are proposed. The simulation results show that the matching area selection algorithm can select more matching areas, and the planned route can pass more matching areas. This method combines UAV trajectory planning with a new navigation method (Random Signal Aided Navigation), which improves the diversity and accuracy of navigation and makes the trajectory planning system more robust. It has great research significance and practical value.
KW - Matching area
KW - Random signal aided navigation
KW - Trajectory planning
UR - http://www.scopus.com/inward/record.url?scp=85098984750&partnerID=8YFLogxK
U2 - 10.1109/ICUS50048.2020.9274866
DO - 10.1109/ICUS50048.2020.9274866
M3 - Conference contribution
AN - SCOPUS:85098984750
T3 - Proceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020
SP - 1024
EP - 1029
BT - Proceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Unmanned Systems, ICUS 2020
Y2 - 27 November 2020 through 28 November 2020
ER -