-
CiteScore
1.83
Impact Factor
Chinese Journal of Information Fusion, 2024, Volume 1, Issue 2: 109-125

Free Access | Research Article | 27 September 2024
1 School of Information and Communication Engineering, North University of China, Taiyuan 030051, China
* Corresponding author: Fengbao Yang, email: [email protected]
Received: 29 July 2024, Accepted: 23 September 2024, Published: 27 September 2024  

Abstract
In response to the challenges associated with the inefficiency and poor quality of 3D path planning for Unmanned Aerial Systems (UAS) operating in vast airspace, a novel two-layer path planning method is proposed based on a divide-and-conquer methodology. This method segregates the solution process into two distinct stages: heading planning and path planning, thereby ensuring the planning of both efficiency and path quality. Firstly, the path planning phase is formulated as a multi-objective optimization problem, taking into account the environmental constraints of the UAV mission and path safety. Subsequently, the multi-dimensional environmental data is transformed into a two-dimensional probabilistic map. An improved ant colony algorithm is proposed to efficiently generate high-quality sets of headings, facilitating the preliminary heading planning for UAVs. Then, the three-dimensional environment of the heading regions is extracted, and an improved Dung Beetle algorithm with multiple strategies is proposed to optimize the three-dimensional path in the secondary layer accurately. The efficacy and quality of the proposed path planning methodology are substantiated through comprehensive simulation analysis.

Graphical Abstract
A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace

Keywords
two-dimensional probabilistic map
trajectory planning
optimization algorithm
two-Layer path planning

References

[1] Sepulveda, E., & Smith, H. (2017). Technology challenges of stealth unmanned combat aerial vehicles. The Aeronautical Journal, 121(1243), 1261-1295.

[2] Qin, J., & Wu, X. (2016, October). Modeling and simulation on the earliest launch time of ship-to-air missile of warship formation in cooperative air-defense. In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (pp. 375-379). IEEE.

[3] Gao, Y., Li, D. S., & Zhong, H. (2020). A novel target threat assessment method based on three-way decisions under intuitionistic fuzzy multi-attribute decision making environment. Engineering Applications of Artificial Intelligence, 87, 103276.

[4] Zhang, R., Chai, R., Chai, S., Xia, Y., & Tsourdos, A. (2023). Design and practical implementation of a high efficiency two-layer trajectory planning method for AGV. IEEE transactions on industrial electronics, 71(2), 1811-1822.

[5] Bashir, N., Boudjit, S., Dauphin, G., & Zeadally, S. (2023). An obstacle avoidance approach for UAV path planning. Simulation modelling practice and theory, 129, 102815.

[6] Ren, Z., Rathinam, S., Likhachev, M., & Choset, H. (2022). Multi-objective path-based D* lite. IEEE Robotics and Automation Letters, 7(2), 3318-3325.

[7] Chong, L., Jian, L., & XueQuan, L. (2020). Static rectangle expansion A* algorithm for pathfinding. IEEE Transactions on Games, 14(1), 23-35.

[8] Ali, H., Xiong, G., Haider, M. H., Tamir, T. S., Dong, X., & Shen, Z. (2023). Feature selection-based decision model for UAV path planning on rough terrains. Expert Systems with Applications, 232, 120713.

[9] Diao, Q., Zhang, J., Liu, M., & Yang, J. (2023). A Disaster Relief UAV Path Planning Based on APF-IRRT* Fusion Algorithm. Drones, 7(5), 323.

[10] Zhang, J., An, Y., Cao, J., Ouyang, S., & Wang, L. (2023). UAV trajectory planning for complex open storage environments based on an improved RRT algorithm. IEEE Access, 11, 23189-23204.

[11] Hao, G., Lv, Q., Huang, Z., Zhao, H., & Chen, W. (2023). Uav path planning based on improved artificial potential field method. Aerospace, 10(6), 562.

[12] Liu, J., Yan, Y., Yang, Y., & Li, J. (2024). An Improved Artificial Potential Field UAV Path Planning Algorithm Guided by RRT Under Environment-aware Modeling: Theory and Simulation. IEEE Access.

[13] Wu, J., Wang, H., Wang, Y., & Liu, Y. (2021). UAV reactive interfered fluid path planning. Acta Automatica Sinica, 47(1), 1-16.

[14] Yang, T., Yang, F., & Li, D. (2024). A New Autonomous Method of Drone Path Planning Based on Multiple Strategies for Avoiding Obstacles with High Speed and High Density. Drones, 8(5), 205.

[15] Dewangan, R. K., Shukla, A., & Godfrey, W. W. (2019). Three dimensional path planning using Grey wolf optimizer for UAVs. Applied Intelligence, 49, 2201-2217.

[16] Hu, K., & Mo, Y. (2024). A novel unmanned aerial vehicle path planning approach: sand cat optimization algorithm incorporating learned behaviour. Measurement Science and Technology, 35(4), 046203.

[17] Zheng, R. Z., Zhang, Y., & Yang, K. (2022). A transfer learning-based particle swarm optimization algorithm for travelling salesman problem. Journal of Computational Design and Engineering, 9(3), 933-948.

[18] Xue, J., & Shen, B. (2023). Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. The Journal of Supercomputing, 79(7), 7305-7336.

[19] Miao, C., Chen, G., Yan, C., & Wu, Y. (2021). Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm. Computers & Industrial Engineering, 156, 107230.

[20] Shen, Q., Zhang, D., Xie, M., & He, Q. (2023). Multi-strategy enhanced dung beetle optimizer and its application in three-dimensional UAV path planning. Symmetry, 15(7), 1432.

[21] Lyu, L., Jiang, H., & Yang, F. (2024). Improved Dung Beetle Optimizer Algorithm with Multi-Strategy for global optimization and UAV 3D path planning. IEEE Access.

[22] Jiachen, H., & Li-hui, F. (2024). Robot path planning based on improved dung beetle optimizer algorithm. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 46(4), 235.

[23] Wai, R. J., & Prasetia, A. S. (2019). Adaptive neural network control and optimal path planning of UAV surveillance system with energy consumption prediction. IEEE Access, 7, 126137-126153.

[24] Liu, Y., Zheng, Z., Qin, F., Zhang, X., & Yao, H. (2022). A residual convolutional neural network based approach for real-time path planning. Knowledge-Based Systems, 242, 108400.

[25] Luo, X., Wang, Q., Gong, H., & Tang, C. (2024). UAV path planning based on the average TD3 algorithm with prioritized experience replay. IEEE Access.

[26] Zhou, C., Gu, S., Wen, Y., Du, Z., Xiao, C., Huang, L., & Zhu, M. (2020). The review unmanned surface vehicle path planning: Based on multi-modality constraint. Ocean Engineering, 200, 107043.

[27] Huang, Z. M., Chen, W. N., Li, Q., Luo, X. N., Yuan, H. Q., & Zhang, J. (2020). Ant colony evacuation planner: An ant colony system with incremental flow assignment for multipath crowd evacuation. IEEE Transactions on Cybernetics, 51(11), 5559-5572.

[28] Yang, T., Yang, F., & Li, D. (2022). An Air Target Course Prediction Method Based on Sub-Regions Divide and Conquer With Double Variable Weight. IEEE Access, 10, 117871-117885.


Cite This Article
APA Style
Yang, T. & Yang, F. (2024). A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast airspace. Chinese Journal of Information Fusion, 1(2), 109–125. https://doi.org/10.62762/CJIF.2024.596648

Article Metrics
Citations:

Crossref

0

Scopus

0

Web of Science

0
Article Access Statistics:
Views: 458
PDF Downloads: 32

Publisher's Note
IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions
IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Chinese Journal of Information Fusion

Chinese Journal of Information Fusion

ISSN: 2998-3371 (Online) | ISSN: 2998-3363 (Print)

Email: [email protected]

Portico

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Copyright © 2024 Institute of Emerging and Computer Engineers Inc.