Mathematical modelling of multi-UAV scenario planning based on 3D LiDAR

被引：0

作者：

Chai R. ^{[1
]}

机构：

[1] Henan Industry and Trade Vocational College, Zhengzhou

来源：

International Journal of Information and Communication Technology | 2024年 / 24卷 / 06期

关键词：

3D LiDAR; mathematical modelling; multi-UAV; scene planning;

D O I：

10.1504/IJICT.2024.138558

中图分类号：

学科分类号：

摘要：

In order to improve the operation efficiency of multi-UAV groups, this paper studies the mathematical modelling of multi-UAV scene planning, takes 3D LiDAR technology as the base navigation technology, and uses the bacterial foraging algorithm as the multi-objective optimisation algorithm. Moreover, this paper appropriately improves the defects of the algorithm, and introduces the bacterial population in the algorithm into the log-linear model to improve the two basic behaviours of the algorithm, the trend and the migration, so that the local search of the algorithm is more accurate. In addition, this paper introduces Gauss-Cauchy variation to ensure the diversity of bacterial populations and ensure that the algorithm results are close to the global optimal value. Through experimental research, it is known that the algorithm proposed in this paper can drive the drone to conform to the flight trajectory as a whole, achieve the expected fusion positioning accuracy, and meet the requirements of autonomous cruising. The average registration time is 120 milliseconds, which meets the real-time perception of the scene and pose estimation requirements during cruising. The experimental study shows that the multi-UAV scene planning method based on 3D LiDAR can effectively improve the optimal control effect of multi-UAV. Copyright © The Author(s) 2024. Published by Inderscience Publishers Ltd. This is an Open Access Article distributed under the CC BY-NC-ND license. (http://creativecommons.org/licenses/by-nc-nd/4.0/)

引用

共 20 条

[11]

Petrlik M., Baca T., Hert D., Vrba M., Krajnik T., Saska M., A robust UAV system for operations in a constrained environment, IEEE Robotics and Automation Letters, 5, 2, pp. 2169-2176, (2020)

[12]

Qin H., Meng Z., Meng W., Chen X., Sun H., Lin F., Ang M.H., Autonomous exploration and mapping system using heterogeneous UAVs and UGVs in GPS-denied environments, IEEE Transactions on Vehicular Technology, 68, 2, pp. 1339-1350, (2019)

[13]

Quan L., Han L., Zhou B., Shen S., Gao F., Survey of UAV motion planning, IET Cyber-systems and Robotics, 2, 1, pp. 14-21, (2020)

[14]

Selecky M., Faigl J., Rollo M., Analysis of using mixed reality simulations for incremental development of multi-UAV systems, Journal of Intelligent & Robotic Systems, 95, 1, pp. 211-227, (2019)

[15]

Smuleac A., Herbei M., Popescu G., Popescu T., Popescu C.A., Barliba C., Smuleac L., 3D modeling of patrimonium objectives using laser technology, Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca, Horticulture, 76, 1, pp. 106-113, (2019)

[16]

Tang Y., Hu Y., Cui J., Liao F., Lao M., Lin F., Teo R.S., Vision-aided multi-UAV autonomous flocking in GPS-denied environment, IEEE Transactions on Industrial Electronics, 66, 1, pp. 616-626, (2018)

[17]

Ulku E.E., Dogan B., Demir O., Bekmezci I., Sharing location information in multi-UAV systems by common channel multi-token circulation method in FANETs, Elektronika ir Elektrotechnika, 25, 1, pp. 66-71, (2019)

[18]

Wang D., Fan T., Han T., Pan J., A two-stage reinforcement learning approach for multi-UAV collision avoidance under imperfect sensing, IEEE Robotics and Automation Letters, 5, 2, pp. 3098-3105, (2020)

[19]

Zhou X., Yi Z., Liu Y., Huang K., Huang H., Survey on path and view planning for UAVs, Virtual Reality & Intelligent Hardware, 2, 1, pp. 56-69, (2020)

[20]

Zohdi T., The game of drones: rapid agent-based machine-learning models for multi-UAV path planning, Computational Mechanics, 65, 1, pp. 217-228, (2020)

← 1 2 →