Abstract
Due to the special requirements of modern military, unmanned combat aircraft (UAVs) attracts more and more attention from the researchers all over the world. It possesses many advantages, such as zero life risk, stronger combat capability ,and better adaptability to harsh combat environments than manual-controlled aircraft. The wide application of UAVs on the battlefield determines their important position in the war. With the rapid development of military science and technology. Nevertheless, there is a conflict between large amount of computing data in complex battlefield environments and limited computing capability of onboard computers. Therefore, a stable and efficient path planning algorithm is critical. This paper aims to dramatically improve the efficiency of path planning by using Laguerre diagram method. Through combing the Laguerre diagram method and the Dijkstra algorithm, the path planning algorithm we proposed in this paper could obtain feasible solutions with lower computational cost by decreasing path nodes and reducing the scale of the problem further. Furthermore, the current research mainly focuses on 2D scenes, which is inconsistent with the actual UAV flight situation. Therefore, this paper proposes a Laguerre diagram based path planning algorithm in 3D scenes. Simulation shows that the execution time of the stage of path planning reduced to one-third compare to the Dijkstra algorithm on the grid.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beard, R.W., et al.: Autonomous vehicle technologies for small fixed-wing UAVs. J. Aerosp. Comput. Inf. Commun. 2(1), 2003–6559 (2005)
Contreras-Cruz, M.A., Ayala-Ramirez, V., Hernandez-Belmonte, U.H.: Mobile robot path planning using artificial bee colony and evolutionary programming. Appl. Soft Comput. J. 30, 319–328 (2015)
Dai, T., Li, B.: PORP: parallel optimization strategy of route planning for self-driving vehicles. J. Zhejiang Univ. (Eng. Sci.). 56, 329–337 (2022)
Erlandsson, T., Niklasson, L.: Automatic evaluation of air mission routes with respect to combat survival. Inf. Fusion 20, 88–98 (2014)
Guo, Q., Zheng, Z., Yue, X.: Path-planning of automated guided vehicle based on improved Dijkstra algorithm. In: Control & Decision Conference (2017)
Inanc, T., Muezzinoglu, M.K., Misovec, K., Murray, R.M.: Framework for low-observable trajectory generation in presence of multiple radars. J. Guid. Control. Dyn. 31(6), 1740–1749 (2008)
Jin, H., Cui, W., Fu, H.: Improved RRT-connect algorithm for urban low-altitude UAV route planning. J. Phys. Conf. Ser. 1948(1), 012048 (2021)
Liu, P., Wang, M., Cui, J., Li, H.: Top-k competitive location selection over moving objects. Data Sci. Eng. 6(4), 392–401 (2021)
de Berg, M.: Computational Geometry: Algorithms and Applications (2005)
Tina, E.: Route planning for air missions in hostile environments. J. Defense Model. Simul. App. Methodol. Technol. JDMS 12(3), 289–303 (2015)
Zhan, W., Wei, W., Chen, N., Chao, W.: Efficient UAV path planning with multiconstraints in a 3d large battlefield environment. Math. Probl. Eng. 2014(pt.3), 1–12 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, D., Chen, X., Ding, P., Huang, J. (2023). A Penetration Path Planning Algorithm for UAV Based on Laguerre Diagram. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13423. Springer, Cham. https://doi.org/10.1007/978-3-031-25201-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-25201-3_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25200-6
Online ISBN: 978-3-031-25201-3
eBook Packages: Computer ScienceComputer Science (R0)