Multicopter interception control based on visual servo and virtual tube in a cluttered environment

doi:10.23919/JSEE.2025.000037

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (4): 1094-1102.doi: 10.23919/JSEE.2025.000037

• CONTROL THEORY AND APPLICATION • Previous Articles

Multicopter interception control based on visual servo and virtual tube in a cluttered environment

Yangjie LYU(), Yan GAO(), Guoyuan QI()

School of Control Science and Engineering, Tiangong University, Tianjin 300387, China

Received:2024-04-01 Online:2025-08-18 Published:2025-09-04
Contact: Yan GAO E-mail:1207245615@qq.com;gaoyan@tiangong.edu.cn;guoyuanqisa@qq.com
About author:
LYU Yangjie was born in 1998. She received her B.S. degree in electrical engineering and automation from Normal University of Anyang, Anyang, China, in 2022. She is currently an M.S. candidate with the Department of Control Science and Engineering at Tiangong University. Her research interest is vision-based multicopter control. E-mail: 1207245615@qq.com

GAO Yan was born in 1994. He received his B.S. degree in control science and engineering from Harbin Institute of Technology, Harbin, China in 2017, and Ph.D. degree in control science and engineering from Beihang University, Beijing, China in 2023. He is currently a lecturer at Tiangong University, Tianjin, China. His research interests include multiagent systems, distributed control, and multicopter control. E-mail: gaoyan@tiangong.edu.cn

QI Guoyuan was born in 1970. He received his B.S. degree in mathematics from Normal University of Mudanjiang, Mudanjiang, China, in 1992, M.S. degree in control theory and control engineering from Heilongjiang University, Harbin, China, in 2001, and Ph.D. degree in control theory and control engineering from Nankai University, Tianjin, China, in 2004. He is a professor at the Department of Control Science and Engineering, Tiangong University, Tianjin, where he is a chair professor. His research interests include nonlinear dynamics, modeling and control of nonlinear systems, chaos analysis, and circuit implementation. E-mail: guoyuanqisa@qq.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62303350).

Abstract

Abstract:

This paper presents a method of multicopter interception control based on visual servo and virtual tube in a cluttered environment. The proposed hybrid heuristic function improves the efficiency of the A* algorithm. The revised objective function makes the virtual tube generating curve not only smooth but also close to the path points generated by the A* algorithm. In six different simulation scenarios, the efficiency of the modified A* algorithm is 6.2% higher than that of the traditional A* algorithm. The efficiency of path planning and virtual tube planning is verified by simulations. The effectiveness of interception control is verified by a software-in-loop (SIL) simulation.

Key words: interception control, visual servo, virtual tube

Yangjie LYU, Yan GAO, Guoyuan QI. Multicopter interception control based on visual servo and virtual tube in a cluttered environment[J]. Journal of Systems Engineering and Electronics, 2025, 36(4): 1094-1102.

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References 38

1	JAVED S, HASSAN A, AHMAD R, et al State-of-the-art and future research challenges in UAV swarms. IEEE Internet of Things Journal, 2024, 11 (11): 19023- 19045. doi: 10.1109/JIOT.2024.3364230
2	ADNAN M H, ZUKARNAIN Z A, AMODU O A Fundamental design aspects of UAV-enabled MEC systems: a review on models, challenges, and future opportunities. Computer Science Review, 2024, 51, 100615. doi: 10.1016/j.cosrev.2023.100615
3	REIMANN J, VACHTSEVANOS G UAVs in urban operations: target interception and containment. Journal of Intelligent and Robotic Systems, 2006, 47 (4): 383- 396. doi: 10.1007/s10846-006-9089-6
4	KUROKI Y, YOUNG G S, HAUPT S E UAV navigation by an expert system for contaminant mapping with a genetic algorithm. Expert Systems with Applications, 2010, 37 (6): 4687- 4697. doi: 10.1016/j.eswa.2009.12.039
5	BEST G, GARG R, KELLER J, et al Multi-robot, multi-sensor exploration of multifarious environments with full mission aerial autonomy. The International Journal of Robotics Research, 2024, 43 (4): 485- 512. doi: 10.1177/02783649231203342
6	FRANCOS R M, BRUCKSTEIN A M On the role and opportunities in teamwork design for advanced multi-robot search systems. Frontiers in Robotics and AI, 2023, 10, 1089062. doi: 10.3389/frobt.2023.1089062
7	AGGARWAL S, KUMAR N Path planning techniques for unmanned aerial vehicles: a review, solutions, and challenges. Computer Communications, 2020, 149, 270- 299. doi: 10.1016/j.comcom.2019.10.014
8	AB WAHAB M N, NAZIR A, KHALIL A, et al Improved genetic algorithm for mobile robot path planning in static environments. Expert Systems with Applications, 2024, 249, 123762. doi: 10.1016/j.eswa.2024.123762
9	MAO P D, FU R, QUAN Q Optimal virtual tube planning and control for swarm robotics. The International Journal of Robotics Research, 2022, 43 (5): 602- 627.
10	GAO Y, BAI C G, QUAN Q Distributed control for a multi-agent system to pass through a connected quadrangle virtual tube. IEEE Trans. on Control of Network Systems, 2022, 10 (2): 693- 705.
11	QUAN Q, FU R, LI M X, et al Practical distributed control for VTOL UAVs to pass a virtual tube. IEEE Trans. on Intelligent Vehicles, 2021, 7 (2): 342- 353.
12	LYU S L, GAO Y, CHE J X, et al. Autonomous drone racing: time-optimal spatial iterative learning control within a virtual tube. Proc. of the IEEE International Conference on Robotics and Automation, 2023: 3197−3203.
13	FICCO M, GRANATA D, PALMIERI F, et al A systematic approach for threat and vulnerability analysis of unmanned aerial vehicles. Internet of Things, 2024, 26, 101180. doi: 10.1016/j.iot.2024.101180
14	SINGH S, KUMAR S R, MUKHERJEE D Time-constrained interception with bounded field of view and input using barrier Lyapunov approach. Journal of Guidance, Control, and Dynamics, 2024, 47 (2): 384- 393. doi: 10.2514/1.G007770
15	CHANG I C, YEN C E, CHANG H F, et al An integrated YOLOv5 and hierarchical human-weight-first path planning approach for efficient UAV searching systems. Machines, 2024, 12 (1): 65. doi: 10.3390/machines12010065
16	LIN S G, GARRATT M A, LAMBERT A J Monocular vision-based real-time target recognition and tracking for autonomously landing a UAV in a cluttered shipboard environment. Autonomous Robots, 2017, 41 (4): 881- 901. doi: 10.1007/s10514-016-9564-2
17	HART P E, NILSSON N J, RAPHAEL B A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. on Systems Science and Cybernetics, 1968, 4 (2): 100- 107. doi: 10.1109/TSSC.1968.300136
18	STENTZ A The focused D* algorithm for real-time replanning. Proc. of the International Joint Conference on Artificial Intelligence, 1995, 95, 1652- 1659.
19	HARABOR D, GRASTIEN A Online graph pruning for pathfinding on grid maps. Proc. of the AAAI Conference on Artificial Intelligence, 2011, 1114- 1119. doi: 10.1609/aaai.v25i1.7994
20	KARAMAN S, WALTER M R, PEREZ A, et al. Anytime motion planning using the RRT*. Proc. of the IEEE International Conference on Robotics and Automation, 2011: 1478−1483.
21	BOHLIN R, KAVRAKI L E. Path planning using lazy PRM. Proc. of the IIEEE International Conference on Robotics and Automation, 2000: 521−528.
22	SRILAKSHMI K, RAO G S, SWARNASRI K, et al. Optimization of ANFIS controller for solar/battery sources fed UPQC using a hybrid algorithm. Electrical Engineering, 2024, 106: 3743−3770.
23	QUAN Q, GAO Y, BAI C G Distributed control for a robotic swarm to pass through a curve virtual tube. Robotics and Autonomous Systems, 2023, 162, 104368. doi: 10.1016/j.robot.2023.104368
24	GAO Y, BAI C G, ZHANG L, et al Multi-UAV cooperative target encirclement within an annular virtual tube. Aerospace Science and Technology, 2022, 128, 107800. doi: 10.1016/j.ast.2022.107800
25	MAO P D, QUAN Q. Making robotics swarm flow more smoothly: a regular virtual tube model. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022: 4498−4504.
26	GLADIS K P A, MADAVARAPU J B, KUMAR R R, et al In-out YOLO glass: indoor-outdoor object detection using adaptive spatial pooling squeeze and attention YOLO network. Biomedical Signal Processing and Control, 2024, 91, 105925. doi: 10.1016/j.bspc.2023.105925
27	ALRUWAILI M, SIDDIQI M H, ATTA M N, et al Deep learning and ubiquitous systems for disabled people detection using YOLO models. Computers in Human Behavior, 2024, 154, 108150. doi: 10.1016/j.chb.2024.108150
28	MINAEIAN S, LIU J, SON Y J Vision-based target detection and localization via a team of cooperative UAV and UGVs. IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2015, 46 (7): 1005- 1016.
29	BEHDAD Z, DEMIR O T, SUNG K W, et al Multi-static target detection and power allocation for integrated sensing and communication in cell-free massive MIMO. IEEE Trans. on Wireless Communications, 2024, 23 (9): 11580- 11596. doi: 10.1109/TWC.2024.3383209
30	AYDIN B, SINGHA S Drone detection using yolov5. Eng, 2023, 4 (1): 416- 433. doi: 10.3390/eng4010025
31	MIRANDA-MOYA A, CASTANEDA H, WANG H S Fixed-time differentiator-based adaptive nonsingular fast terminal image-based visual servoing for a quadrotor UAV subject to turbulent wind. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (3): 2807- 2818. doi: 10.1109/TAES.2024.3354229
32	AHMAD K, PARK J E, ILYAS T, et al Accurate and robust pollinations for watermelons using intelligence guided visual servoing. Computers and Electronics in Agriculture, 2024, 219, 108753. doi: 10.1016/j.compag.2024.108753
33	QUAN Q. Introduction to multicopter design and control. Singapore: Springer, 2017.
34	CHAUMETTE F, HUTCHINSON S. Visual servo control. I. basic approaches. IEEE Robotics & Automation Magazine, 2006, 13(4): 82−90.
35	YANG K, QUAN Q. An autonomous intercept drone with image-based visual servo. Proc. of the IEEE International Conference on Robotics and Automation, 2020: 2230−2236.
36	DANIELSSON P E Euclidean distance mapping. Computer Graphics and Image Processing, 1980, 14 (3): 227- 248. doi: 10.1016/0146-664X(80)90054-4
37	DAI X H, KE C X, QUAN Q, et al RFlySim: automatic test platform for UAV autopilot systems with FPGA-based hardware-in-the-loop simulations. Aerospace Science and Technology, 2021, 114, 106727. doi: 10.1016/j.ast.2021.106727
38	WANG S, DAI X H, KE C X, et al. RflySim: a rapid multicopter development platform for education and research based on Pixhawk and MATLAB. Proc. of the International Conference on Unmanned Aircraft Systems, 2021: 1587−1594.

Parameter	Value	Parameter	Value
${\alpha _1}$	0.8	${\alpha _3}$	0.8
${\alpha _2}$	0.2	$c$	1

Scenario	Modified A* algorithm/s	A* algorithm/s	Reduced time percentage/%
1	0.025	0.028	+10.7
2	0.032	0.036	+11.1
3	0.033	0.037	+10.8
4	0.032	0.030	−6.7
5	0.034	0.035	+2.9
6	0.034	0.037	+8.1
Average	0.032	0.034	+6.2

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Multicopter interception control based on visual servo and virtual tube in a cluttered environment

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