Monitorización mediante vehículos aéreos multicámara. Caso de uso de Unreal Engine
Contenido principal del artículo
- José Francisco López Ruiz+−
- Jaime Rico Ranea+−
- Teodoro Álamo Cantarero+−
- Manuel Gil Ortega Linares+−
- Manuel Vargas Villanueva+−
Resumen
El trabajo propuesto en este artículo parte de la consideración de un vehículo aéreo autónomo, dotado de múltiples cámaras, como agente capaz de proporcionar mayor versatilidad en aplicaciones de monitorización y seguimiento de múltiples objetivos móviles. Dicho agente tendría la capacidad de orientar sus cámaras de forma completamente independiente y éstas estarían dotadas con capacidad de zoom, lo que permitiría ajustar la distancia focal de cada una de ellas a conveniencia. Partiendo de este concepto y de una estrategia de optimización desarrollada en trabajos anteriores, se propone una generalización de la misma que permita la colaboración de varios agentes en una misma misión. De forma complementaria, parte del trabajo se centra en explorar las posibilidades que ofrece Unreal Engine 5 como herramienta de simulación gráfica para la implementación de la propuesta.
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Ahmed, I., Din, S., Jeon, G., Piccialli, F., Fortino, G., 2021. Towards collaborative robotics in top view surveillance: A framework for multiple object tracking by detection using deep learning. IEEE/CAA Journal of Automatica Sinica 8 (7), 1253–1270.
Baek, S., York, G., 2020. Optimal sensor management for multiple target tracking using cooperative unmanned aerial vehicles. In: 2020 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, pp. 1294–1300.
Burke, C., McWhirter, P. R., Veitch-Michaelis, J., McAree, O., Pointon, H. A., Wich, S., Longmore, S., 2019. Requirements and limitations of thermal drones for effective search and rescue in marine and coastal areas. Drones 3 (4), 78.
Collins, J., Chand, S., Vanderkop, A., Howard, D., 2021. A review of physics simulators for robotic applications. IEEE Access 9, 51416–51431.
Epic Games, 2024. The most powerful real-time 3D creation tool: Unreal Engine. https://www.unrealengine.com/en-us, accessed: 2023-10-17.
Jordan, S., Moore, J., Hovet, S., Box, J., Perry, J., Kirsche, K., Lewis, D., Tse, Z. T. H., 2018. State-of-the-art technologies for UAV inspections. IET Radar, Sonar & Navigation 12 (2), 151–164.
Khan, M. A., Ectors, W., Bellemans, T., Janssens, D., Wets, G., 2017. UAV-based traffic analysis: A universal guiding framework based on literature survey. Transportation Research Procedia 22, 541–550.
Koenig, N., Howard, A., 2004. Design and use paradigms for gazebo, an open-source multi-robot simulator. In: 2004 IEEE/RSJ international conference on intelligent robots and systems (IROS)(IEEE Cat. No. 04CH37566). Vol. 3. Ieee, pp. 2149–2154.
Kumar, P., Sonkar, S., Ghosh, A. K., Philip, D., 2020. Real-time vision-based tracking of a moving terrain target from light weight fixed wing UAV using gimbal control. In: 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT). Vol. 1. IEEE, pp. 154–159.
López Ruiz, J. F., Rico, J., Vargas, M., 2024. Enlace a vídeo demostrativo. https://uses0-my.sharepoint.com/:f:/g/personal/mvargas_us_es/EoNpD79xbc9FpxZKeUnZwxMB34Q5rtbs6xgIAoqGl4s8NQ?e=fxHfUb, Accessed: 2024-06-01.
Michel, O., 2004. Cyberbotics ltd. webotsTM: professional mobile robot simulation. International Journal of Advanced Robotic Systems 1 (1), 5.
Robin, C., Lacroix, S., 2016. Multi-robot target detection and tracking: taxonomy and survey. Autonomous Robots 40 (4), 729–760.
Salahat, E., Asselineau, C.-A., Coventry, J., Mahony, R., 2019. Waypoint planning for autonomous aerial inspection of large-scale solar farms. In: IECON 2019-45th Annual Conference of the IEEE Industrial Electronics Society. Vol. 1. IEEE, pp. 763–769.
Senanayake, M., Senthooran, I., Barca, J. C., Chung, H., Kamruzzaman, J., Murshed, M., 2016. Search and tracking algorithms for swarms of robots: A survey. Robotics and Autonomous Systems 75, 422–434.
Shah, S., Dey, D., Lovett, C., Kapoor, A., 2018. Airsim: High-fidelity visual and physical simulation for autonomous vehicles. In: Field and Service Robotics: Results of the 11th International Conference. Springer, pp. 621– 635.
Sharma, R., Pack, D., 2013. Cooperative sensor resource management to aid multi target geolocalization using a team of small fixed-wing unmanned aerial vehicles. In: AIAA Guidance, Navigation, and Control (GNC) Conference. p. 4706.
Song, Y., Naji, S., Kaufmann, E., Loquercio, A., Scaramuzza, D., 2021. Flightmare: A flexible quadrotor simulator. In: Conference on Robot Learning. PMLR, pp. 1147–1157.
Sun, J., Li, B., Jiang, Y., Wen, C., 2016. A camera-based target detection and positioning UAV system for search and rescue (SAR) purposes. Sensors 16 (11), 1778.
Sun, P., Li, S., Zhu, B., Zuo, Z., Xia, X., 2023. Vision-based fixed-time uncooperative aerial target tracking for UAV. IEEE/CAA Journal of Automatica Sinica 10 (5), 1322–1324.
The MathWorks, Inc, 2024. Unreal engine simulation for unmanned aerial vehicles - Matlab and Simulink. https://www.mathworks.com/help/uav/ug/3d-simulation-for-unmanned-aerial-vehicles.html, accessed: 2024-03-28.
Vargas, M., Vivas, C., Alamo, T., 2024. Optimal positioning strategy for multi-camera, zooming drones. IEEE/CAA J. Autom. Sinica 11 (8), In press.
Vargas, M., Vivas, C., Rubio, F. R., Ortega, M. G., 2022. Flying chameleons: A new concept for minimum-deployment, multiple-target tracking drones. Sensors 22 (6), 2359.
Zhao, Y., Wang, X., Wang, C., Cong, Y., Shen, L., 2019. Systemic design of distributed multi-UAV cooperative decision-making for multi-target tracking. Autonomous Agents and Multi-Agent Systems 33 (1), 132–158. DOI: 10.1007/s10458-019-09401-5.