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Search of Non-Stationary Object by Multicopter Swarm Based on Thermal Motion Equivalent Method
https://doi.org/10.17587/mau.26.316-325
Abstract
The paper presents the issue result of searching a non-stationary object in a topologically closed bounded area by a multi-agent system of quadrocopter-type aircraft. Thermal motion equivalent method (TMEM) method is proposed as a control algorithm for the multi-agent system. The TMEM is based on the similarity of the motion of molecules. In the case of TMEM it is possible to estimate swarm stability and performance of swarm problem solving using integral parameters already known from thermodynamics: RMS velocity, frequency of interactions and others. The aircraft motion similar to a molecular dynamic in TMEM is provided by the control system of each agent interacted with others by data exchanging. The paper demonstrates the effectiveness of applying the TMEM method to the search for a dynamic target in a bounded area based on numerous simulations in a specially designed MASPlatform environment representing a virtual polygon. The MASPlatform software implements the motion of thirty agents in a space of 300 by 300 meters. The dynamics of each agent is represented by a system of nonlinear differential equations with a quaternionic controller. The inference of efficiency is made based on multiple simulations of the ICD search issue.
In this paper, estimates of swarm numbers as a function of the search space are obtained. The paper proposes an approach to estimating the search time of a non-stationary object with known dynamics by a swarm, whose agents interact using TMEM in a bounded area, based on the thermodynamic parameter "mean free path length". This parameter is important for real systems where there are constraints on the agent’s energy resource.
About the Authors
E. A. Heiss
Tula State University
Russian Federation
Russian Federation
Postgraduate Student
Tula, 300012
A. V. Kozyr
Tula State University
Russian Federation
Russian Federation
Tula, 300012
O. O. Morozov
Tula State University
Russian Federation
Russian Federation
Tula, 300012
References
1. Savkin A. V., Huang H. Multi-UAV Navigation for Optimized Video Surveillance of Ground Vehicles on Uneven Terrains, IEEE Transactions on Intelligent Transportation Systems, 2023, vol. 24, no. 9, pp. 10238—10242, doi: 10.1109/TITS.2023.3270969.
2. Li X., Yang L. Design and Implementation of UAV Intelligent Aerial Photography System, 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, Nanchang, China, 2012, pp. 200—203, doi: 10.1109/IHMSC.2012.144.
3. Betti Sorbelli F. UAV-Based Delivery Systems: A Systematic Review, Current Trends, and Research Challenges. ACM Journal on Autonomous Transportation Systems, 2024, doi: 10.1145/3649224.
4. Golam M., Akter R., Tuli E. A., Kim D.-S., Lee J. -M. Lightweight Blockchain Assisted Unauthorized UAV Access Prevention in the Internet of Military Things, 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, Republic of, 2022, pp. 890—894, doi: 10.1109/ICTC55196.2022.9953024.
5. Filimonov A. B., Filimonov N. B., Nguyen Т. К., Pham Q. P. Optimization of UAV Flight Routes during Group Patrolling of Extended Territories as a Multiple Task of a Traveling Salesman with Several Depots, Mekhatronika, Avtomatizatsiya, Upravlenie. 2024, vol. 25, no. 5, pp. 259—265 (In Russ.), doi: 10.17587/mau.25.259-265.
6. Wood J., Hedrick J. Space Partitioning and Classification for Multi-target Search and Tracking by Heterogeneous Unmanned Aerial System Teams, AIAA Infotech at Aerospace Conference and Exhibit, 2011, doi: 10.2514/6.2011-1443.
7. Liu X., Liu Y., Chen Y. Reinforcement Learning in MultipleUAV Networks: Deployment and Movement Design, IEEE Transactions on Vehicular Technology, pp. 1-1, doi: 10.1109/TVT.2019.2922849.
8. Liu Z., Gao X., Fu X. A Cooperative Search and Coverage Algorithm with Controllable Revisit and Connectivity Maintenance for Multiple Unmanned Aerial Vehicles, Sensors, 2018, vol. 18, no. 5.
9. Pyke L., Stark C. Dynamic Pathfinding for a Swarm Intelligence Based UAV Control Model Using Particle Swarm Optimisation, Frontiers in Applied Mathematics and Statistics, 2021, no. 7, doi: 10.3389/fams.2021.744955.
10. He Y., Zeng Q., Liu J., Xu G., Xiaoyi D. Path planning for indoor UAV based on Ant Colony Optimization. 25th Chinese Control and Decision Conference, CCDC, 2013, pp. 2919—2923, doi: 10.1109/CCDC.2013.6561444.
11. Bolaji A., Khader A. T., Al-Betar M., Awadallah M. Artificial bee colony algorithm, its variants and applications: A survey. Journal of Theoretical and Applied Information Technology, 2013, no. 47, pp. 434—459.
12. Heiss E. A., Morozov O. O., Kozyr A. V., Efromeev A. G. Methodology for the Synthesis of a Multicopter Controller Acting as a Swarm Agent using the Thermal Motion Equivalent Method, Mekhatronika, Avtomatizatsiya, Upravlenie, 2024, vol. 25, no. 1, pp. 43—52 (in Russian), doi: 10.17587/mau.25.43-52.
13. Heiss E., Kozyr A., Morozov O. Synchronization of the Coupled Swarm Agents’ Movement Interacting by the Thermal Motion Equivalent Method, International Conference of Young Specialists on Micro/Nanotechnologies and Electron Devices, EDM, 2024, pp. 1600—1607.
14. Heiss E., Kozyr A., Morozov O. Interaction Between Area Boundary and Formation of Agents Interacting by the Thermal Motion Equivalent Method, Proceedings — 2024 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2024, 2024, pp. 771—777.
15. Heiss, E., Morozov O., Efromeev A. Assessing the similarity of atoms’ thermal motion behavior by swarm agents. 4th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency (SUMMA), Lipetsk, IEEE, 2022, pp. 92—96.
16. Bansal S., Goel R., Maini R. Ground Vehicle and UAV Collaborative Routing and Scheduling for Humanitarian logistics using Random Walk Based Ant Colony Optimization, Scientia Iranica, 2022, vol. 29, no. 2, pp. 632—644.
17. Liu Z., Qiu C., Zhang Z. Sequence-to-Sequence MultiAgent Reinforcement Learning for Multi-UAV Task Planning in 3D Dynamic Environment. Applied Sciences, 2022, no. 12, pp. 12181, doi: 10.3390/app122312181.
18. Kostjukov V. A., Medvedev M. Y., Pshikhopov V. Kh. Algorithms for Path Planning in a Group of Mobile Robots in an Environment with Obstacles with a Given Template, Mekhatronika, Avtomatizatsiya, Upravlenie, 2023, vol. 24, no. 1, pp. 33—45 (in Russian), doi: 10.17587/mau.24.33-45.
19. Jing Y. Research on Multi-UAV Swarm Control Based on Olfati-Saber Algorithm with Variable Speed Virtual Leader, IoT as a Service, Cham: Springer International Publishing, 2021, pp. 14—22.
20. Sh o R., Tao R., Liu Y., Yang Y., Li D., Chen J. UAV cooperative search in dynamic environment based on hybrid-layered APF, EURASIP Journal on Advances in Signal Processing, 2021, vol. 2021, no. 1, pp. 101.
21. Sutantyo D., Kernbach S., Nepomnyashchikh V., Levi P. Multi-Robot Searching Algorithm Using Levy Flight and Artificial Potential Field, 2010 8th IEEE International Workshop on Safety, Security, and Rescue Robotics, Bremen, Germany, 2011, pp. 1—6.
22. Filimonov A. B., Filimonov N. B. The Concept of Fairway in Problems of Potential Guidance of Mobile Robots, Optoelectronics, Instrumentation and Data Processing, 2022, vol. 58, pp. 366—372, doi: 10.3103/S8756699022040057.
23. Evdokimov I. N., Eliseev N. Yu. Molecular mechanisms of viscosity of liquid and gas. Part 1. Basic concepts, Moscow, Gubkin Russian State University of Oil and Gas, 2005, 59 p.
24. Sivukhin D. V. General course of physics. Thermodynamics and molecular physics, vol. 2, Moscow, PHYSMATLIT, 2021, 544 с.
Review
For citations:
Heiss E., Kozyr A.V., Morozov O.O. Search of Non-Stationary Object by Multicopter Swarm Based on Thermal Motion Equivalent Method. Mekhatronika, Avtomatizatsiya, Upravlenie. 2025;26(6):316-325. (In Russ.) https://doi.org/10.17587/mau.26.316-325
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