Method of Calculating Capsule-Shaped Air Corridors of Safe Routes for a Group of Unmanned Aerial Vehicles
Keywords:
UAVs, air corridors, agricultural tasks, Rapidly-Exploring Random Tree*, Loose Octree, autonomous navigation, group planningAbstract
The paper considers the problem of constructing safe routes for a group of unmanned aerial vehicles in a limited airspace over an agricultural area. The relevance of the study is due to the growing use of UAV groups in the agro-industrial complex for monitoring, mapping, and processing fields, which requires ensuring flight safety in conditions of high air traffic density, limited communication, and exposure to external factors. A particular challenge is the need for autonomous missions in the presence of navigation errors and natural impacts. A route planning method is proposed based on representing the trajectory of each device as a capsule air corridor – a three-dimensional volume of a fixed radius formed along the trajectory segments. Spatial redundancy ensures safe spacing of trajectories at the planning stage, eliminating conflicts during subsequent autonomous flight operations without the need for continuous coordination between agents. The capsule radius includes a reserve for possible deviations from the planned trajectory, which ensures resistance to navigation errors. The method is based on the sequential formation of routes for each device according to a four-phase scheme, including a vertical ascent from the starting point to the operating altitude, a horizontal transition to the entrance to the processing zone, a return from the exit from the zone to the starting point of the descent, and a vertical descent to the initial position. Each new route is built considering the already reserved air corridors through an analytical check of geometric intersections between the capsules of different trajectories and convex polyhedrons of the processing zones. To improve computational efficiency, hierarchical spatial filtering is used based on bounding parallelepipeds, which allows for the rapid cutting off of obviously non-intersecting objects at the preliminary stage and performing an accurate geometric check only for potentially conflicting route segments. Numerical experiments were carried out for groups of 2 to 32 devices on a typical agricultural plot of one square kilometer. A nonlinear increase in the planning time and the number of iterations with an increase in the number of agents was found, which is due to the need to build each subsequent route in an already partially occupied space with an increasing number of spatial constraints. The length of routes shows a tendency to increase, especially pronounced at the initial stages of scaling, which is associated with the need to bypass already reserved air corridors.
References
2. Shaitura N.S. [Monitoring agricultural lands using unmanned aerial vehicles]. Prakticheskie aspekty primenenija sovremennyh bespilotnyh letatel'nyh apparatov [Practical aspects of the use of modern unmanned aerial vehicles]. 2022. pp. 46–57. (In Russ.).
3. Nikolakopoulos I.A., Petropoulos G.P. Obtaining a Land Use/Cover Cartography in a Typical Mediterranean Agricultural Field Combining Unmanned Aerial Vehicle Data with Supervised Classifiers. Land. 2025. vol. 14. no. 3. DOI: 10.3390/land14030643.
4. Pavlova A.I. [Application of unmanned and geoinformation technologies for digital soil mapping]. Informacionnye tehnologii, sistemy i pribory v APK. Agroinfo-2021 [Information technologies, systems and devices in agroindustrial complex. Agroinfo-2021]. vol. 2021. pp. 97–99. (In Russ.).
5. Hassler S.C., Baysal-Gurel F. Unmanned aircraft system (UAS) technology and applications in agriculture. Agronomy. 2019. vol. 9. no. 10. DOI: 10.3390/agronomy9100618.
6. Velusamy P., Rajendran S., Mahendran R.K., Naseer S., Shafiq M., Choi J.G. Unmanned Aerial Vehicles (UAV) in precision agriculture: Applications and challenges. Energies. 2021. vol. 15. no. 1. DOI: 10.3390/en15010217.
7. Makam S., Komatineni B.K., Meena S.S., Meena U. Unmanned aerial vehicles (UAVs): an adoptable technology for precise and smart farming. Discover Internet of Things. 2024. vol. 4. DOI: 10.1007/s43926-024-00066-5.
8. Sharon G., Stern R., Felner A., Sturtevant N. R. Conflict-based search for optimal multi-agent pathfinding. Artificial Intelligence. 2015. vol. 219. pp. 40–66. DOI: 10.1016/j.artint.2014.11.006.
9. Wang Z., Zhang Z., Dou W., Hu G., Zhang L., Zhang M. Extending Conflict-Based Search for Optimal and Efficient Quadrotor Swarm Motion Planning. Drones. 2024. vol. 8. no. 12. DOI: 10.3390/drones8120719.
10. Andreychuk A., Yakovlev K., Boyarski E., Stern R. Improving continuous-time conflict based search. Proceedings of the AAAI Conference on Artificial Intelligence. 2021. vol. 35. no. 13. pp. 11220–11227. DOI: 10.1609/aaai.v35i13.17338.
11. Liu X., Su Y., Wu Y., Guo Y. Multi-conflict-based optimal algorithm for multi-UAV cooperative path planning. Drones. 2023. vol. 7. no. 3. DOI: 10.3390/drones7030217.
12. Cap M., Novak P., Vokrinek J., Pechoucek M. Multi-agent RRT*: Sampling-based cooperative pathfinding. arXiv preprint arXiv:1302.2828. 2013.
13. Karaman S., Frazzoli E. Sampling based algorithms for optimal motion planning. The International Journal of Robotics Research. 2011. vol. 30. no. 7. pp. 846–894. DOI: 10.1177/0278364911406761.
14. Silver D. Cooperative pathfinding. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 2005. vol. 1. no. 1. pp. 117–122. DOI: 10.1609/aiide.v1i1.18726.
15. Barer M., Sharon G., Stern R., Felner A. Suboptimal variants of the conflict based search algorithm for the multi agent pathfinding problem. Proceedings of the International Symposium on Combinatorial Search. 2021. vol. 5. no. 1. pp. 19–27. DOI: 10.1609/socs.v5i1.18315.
16. Van Den Berg J., Guy S.J., Lin M., Manocha D. Reciprocal n-body collision avoidance. Robotics Research: The 14th International Symposium ISRR. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. pp. 3–19.
17. Van den Berg J., Lin M., Manocha D. Reciprocal velocity obstacles for real-time multi-agent navigation. IEEE International Conference on Robotics and Automation. 2008. pp. 1928–1935. DOI: 10.1109/ROBOT.2008.4543489.
18. Yan X., Jiang D., Miao R., Li Y. Formation control and obstacle avoidance algorithm of a multi-USV system based on virtual structure and artificial potential field. Journal of Marine Science and Engineering. 2021. vol. 9. no. 2. DOI: 10.3390/jmse9020161.
19. Wu W., Zhang X., Miao Y. Starling behavior inspired flocking control of fixed wing unmanned aerial vehicle swarm in complex environments with dynamic obstacles. Biomimetics. 2022. vol. 7. no. 4. DOI: 10.3390/biomimetics7040214.
20. Alqudsi Y. Synchronous task allocation and trajectory optimization for autonomous drone swarm. 1st International Conference on Emerging Technologies for Dependable Internet of Things (ICETI). 2024. pp. 1–8. DOI: 10.1109/ICETI63946.2024.10777195.
21. Kotov D.V., Lebedev O.B. [Control of movement of a group of UAVs with observance of the geometric structure of the formation based on alternative collective adaptation]. Izvestija JuFU. Tehnicheskie nauki – Bulletin of SFedU. Technical sciences. 2024. vol. 1. pp. 155–167. (In Russ.).
22. Saenko I.B., Lauta O.S., Mityakov E.S., Sokolov A.P. [Algorithm for swarm control of UAVs with elements of cluster analysis]. Informacija i kosmos – Information and Space. 2024. no. 4. pp. 68–75. (In Russ.).
23. Ulrich T. Loose octrees. Game Programming Gems. 2000. vol. 1. pp. 434–442.
24. Pournin L., Weber M., Tsukahara M., Ferrez J.A., Ramaioli M., Liebling T.M. Three dimensional distinct element simulation of spherocylinder crystallization. Granular Matter. 2005. vol. 7. pp. 119–126. DOI: 10.1007/s10035-004-0188-4.
25. Bretscher O. Linear algebra with applications. NJ: Prentice Hall, 1997. 587 p.
26. Lien J.M., Amato N.M. Approximate convex decomposition of polyhedra and its applications. Computer Aided Geometric Design. 2008. vol. 25. no. 7. pp. 503–522.
27. Rendering W.P.B. Physically based rendering. Procedia IUTAM. 2015. vol. 13. pp. 3.
28. Kang G., Kim Y.B., You W.S., Lee Y.H., Oh H.S., Moon H., Choi H.R. Sampling based path planning with goal oriented sampling. IEEE International Conference on Advanced Intelligent Mechatronics (AIM). 2016. pp. 1285–1290. DOI: 10.1109/AIM.2016.7576947.
Published
How to Cite
Section
Copyright (c) Дмитрий Андреевич Аникин, Антон Игоревич Савельев

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms: Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).