Wildfire detection in large-scale environments using force-based control for swarms of UAVs

Adepegba, A. A., Miah, S., & Spinello, D. (2016). Multi-agent area coverage control using reinforcement learning. In: Florida artificial intelligence research society conference (pp. 368–373). Alexandrov, V., Kirik, K., & Kobrin, A. (2018). Multi-robot voronoi tessellation based area partitioning algorithm study. Paladyn, Journal of Behavioral Robotics, 9(1), 214–220. https://doi.org/10.1515/pjbr-2018-0014. Alkhatib, A. A. (2014). A review on forest fire detection techniques. International Journal of Distributed Sensor Networks, 10(3), 597–368. https://doi.org/10.1155/2014/597368. Atten, C., Channouf, L., & Danoy, G. et al (2016). Uav fleet mobility model with multiple pheromones for tracking moving observation targets. In European conference on the applications of evolutionary computation (pp. 332–347). Springer. https://doi.org/10.1007/978-3-319-31204-0_22 Aydin, B., Selvi, E., Tao, J., et al. (2019). Use of fire-extinguishing balls for a conceptual system of drone-assisted wildfire fighting. Drones, 3(1), 17. https://doi.org/10.3390/drones3010017. Bailon-Ruiz, R., Lacroix, S., & Bit-Monnot, A. (2018). Planning to monitor wildfires with a fleet of uavs. In 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 4729–4734). https://doi.org/10.1109/IROS.2018.8593859 Barmpoutis, P., Papaioannou, P., Dimitropoulos, K., et al. (2020). A review on early forest fire detection systems using optical remote sensing. Sensors, 20(22), 6442. https://doi.org/10.3390/s20226442. Basilico, N., & Carpin, S. (2015). Deploying teams of heterogeneous uavs in cooperative two-level surveillance missions. In 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 610–615). IEEE. https://doi.org/10.1109/IROS.2015.7353435 Bowman, D., Williamson, G., Yebra, M., et al. (2020). Wildfires: Australia needs national monitoring agency. Nature, 584(7820), 188–191. https://doi.org/10.1038/d41586-020-02306-4. Carpin, S., Burch, D., Basilico, N., et al. (2013). Variable resolution search with quadrotors: Theory and practice. Journal of Field Robotics, 30(5), 685–701. https://doi.org/10.1002/rob.21468. Chang, Y. H., Wu, C. I., & Lin, H. W. (2018). Adaptive distributed fault-tolerant formation control for multi-robot systems under partial loss of actuator effectiveness. International Journal of Control, Automation and Systems, 16(5), 2114–2124. https://doi.org/10.1007/s12555-016-0587-4. Cortés, J., Martínez, S., & Bullo, F. (2005). Spatially-distributed coverage optimization and control with limited-range interactions. ESAIM: Control, Optimisation and Calculus of Variations, 11(4), 691–719. https://doi.org/10.1051/cocv:2005024. Cortes, J., Martinez, S., Karatas, T., et al. (2004). Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation, 20(2), 243–255. https://doi.org/10.1109/TRA.2004.824698. Gazzard, R., McMorrow, J., & Aylen, J. (2016). Wildfire policy and management in england: An evolving response from fire and rescue services, forestry and cross-sector groups. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1696), 20150341. https://doi.org/10.1098/rstb.2015.0341. Ghamry, K. A., Kamel, M. A., & Zhang, Y. (2017). Multiple uavs in forest fire fighting mission using particle swarm optimization. In 2017 International conference on unmanned aircraft systems (ICUAS) (pp. 1404–1409). IEEE. https://doi.org/10.1109/ICUAS.2017.7991527. Gill, A. M., Stephens, S. L., & Cary, G. J. (2013). The worldwide “wildfire’’ problem. Ecological Applications, 23(2), 438–454. https://doi.org/10.1890/10-2213.1. Goldberg, M. (1971). On the densest packing of equal spheres in a cube. Mathematics Magazine, 44(4), 199–208. https://doi.org/10.1080/0025570X.1971.11976147. Haksar, R. N., & Schwager, M. (2018). Distributed deep reinforcement learning for fighting forest fires with a network of aerial robots. In 2018 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 1067–1074). IEEE Press. https://doi.org/10.1109/IROS.2018.8593539 Hao, C., Xiangke, W., Lincheng, S., et al. (2021). Formation flight of fixed-wing UAV swarms: A group-based hierarchical approach. Chinese Journal of Aeronautics, 34(2), 504–515. https://doi.org/10.1016/j.cja.2020.03.006. Hauert, S., Leven, S., & Varga, M., et al. (2011). Reynolds flocking in reality with fixed-wing robots: Communication range versus maximum turning rate. In 2011 IEEE/RSJ international conference on intelligent robots and systems (pp. 5015–5020). IEEE. https://doi.org/10.1109/IROS.2011.6095129 Hunt, E. R., Jones, S., & Hauert, S. (2019). Testing the limits of pheromone stigmergy in high-density robot swarms. Royal Society Open Science, 6(11), 190–225. https://doi.org/10.1098/rsos.190225. Innocente, M., & Grasso, P. (2019). Self-organising swarms of firefighting drones: Harnessing the power of collective intelligence in decentralised multi-robot systems. Journal of Computational Science, 34, 80–101. https://doi.org/10.1016/j.jocs.2019.04.009. Koh, L. P., & Wich, S. A. (2012). Dawn of drone ecology: Low-cost autonomous aerial vehicles for conservation. Tropical Conservation Science, 5(2), 121–132. https://doi.org/10.1177/194008291200500202. Kumar, M., Cohen, K., & HomChaudhuri, B. (2011). Cooperative control of multiple uninhabited aerial vehicles for monitoring and fighting wildfires. Journal of Aerospace Computing, Information, and Communication, 8(1), 1–16. https://doi.org/10.2514/1.48403. Legge, S., Woinarski, J. C., Scheele, B. C., et al. (2021). Rapid assessment of the biodiversity impacts of the 2019–2020 Australian megafires to guide urgent management intervention and recovery and lessons for other regions. Diversity and Distributions. https://doi.org/10.1111/ddi.13428. Leonard, J., Savvaris, A., & Tsourdos, A. (2012). Towards a fully autonomous swarm of unmanned aerial vehicles. In Proceedings of 2012 UKACC international conference on control (pp. 286–291). IEEE. https://doi.org/10.1109/CONTROL.2012.6334644 Leonard, J. J., & Feder, H. J. S. (2000). A computationally efficient method for large-scale concurrent mapping and localization. In J. M. Hollerbach & D. E. Koditschek (Eds.), Robotics research (pp. 169–176). Springer. Molina-Terrén, D. M., Xanthopoulos, G., Diakakis, M., et al. (2019). Analysis of forest fire fatalities in southern Europe: Spain, Portugal, Greece and Sardinia (Italy). International Journal of Wildland Fire, 28(2), 85–98. https://doi.org/10.1071/WF18004. Oakey, A., Waters, T., Zhu, W., et al. (2021). Quantifying the effects of vibration on medicines in transit caused by fixed-wing and multi-copter drones. Drones, 5(1), 22. https://doi.org/10.3390/drones5010022. Ollero, A., & Merino, L. (2006). Unmanned aerial vehicles as tools for forest-fire fighting. Forest Ecology and Management, 234(1), S263. Pausas, J. G., & Keeley, J. E. (2021). Wildfires and global change. Frontiers in Ecology and the Environment, 19(7), 387–395. https://doi.org/10.1002/fee.2359. Penny, S. G., White, R. L., Scott, D. M., et al. (2019). Using drones and sirens to elicit avoidance behaviour in white rhinoceros as an anti-poaching tactic. Proceedings of the Royal Society B, 286(1907), 20191–135. https://doi.org/10.1098/rspb.2019.1135. Pham, H. X., La, H. M., Feil-Seifer, D. et al. (2017). A distributed control framework for a team of unmanned aerial vehicles for dynamic wildfire tracking. In 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) (pp. 6648–6653). https://doi.org/10.1109/IROS.2017.8206579 Pradhan, B., Suliman, M. D. H. B., & Awang, M. A. B. (2007). Forest fire susceptibility and risk mapping using remote sensing and geographical information systems (GIS). Disaster Prevention and Management: An International Journal, 8, 9. https://doi.org/10.1108/09653560710758297. Ramachandran, R. K., Pierpaoli, P., Egerstedt, M., et al. (2022). Resilient monitoring in heterogeneous multi-robot systems through network reconfiguration. IEEE Transactions on Robotics, 38(1), 126–138. https://doi.org/10.1109/TRO.2021.3128313. Roldán-Gómez, J. J., González-Gironda, E., & Barrientos, A. (2021). A survey on robotic technologies for forest firefighting: Applying drone swarms to improve firefighters’ efficiency and safety. Applied Sciences, 11(1), 363. https://doi.org/10.3390/app11010363. Schoenherr, A. A. (2017). A natural history of California. Univ of California Press. Schwarzrock, J., Zacarias, I., Bazzan, A. L., et al. (2018). Solving task allocation problem in multi unmanned aerial vehicles systems using swarm intelligence. Engineering Applications of Artificial Intelligence, 72, 10–20. https://doi.org/10.1016/j.engappai.2018.03.008. Seraj, E., & Gombolay, M. (2020). Coordinated control of uavs for human-centered active sensing of wildfires. In 2020 American Control Conference (ACC) (pp. 1845–1852). https://doi.org/10.23919/ACC45564.2020.9147613 Seraj, E., Silva, A., & Gombolay, M. C. (2019). Safe coordination of human–robot firefighting teams. CoRR abs/1903.06847. arXiv:1903.06847 Seraj, E., Chen, L., & Gombolay, M. C. (2022). A hierarchical coordination framework for joint perception-action tasks in composite robot teams. IEEE Transactions on Robotics, 38(1), 139–158. https://doi.org/10.1109/TRO.2021.3096069. Sherstjuk, V., Zharikova, M., & Sokol, I. (2018). Forest fire-fighting monitoring system based on uav team and remote sensing. In 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO) (pp. 663–668). IEEE. https://doi.org/10.1109/ELNANO.2018.8477527 Spears, W., & Spears, D. (2012). Physicomimetics: Physics-Based Swarm Intelligence. https://doi.org/10.1007/978-3-642-22804-9 Spears, W. M., Spears, D. F., & Heil, R. et al. (2004). An overview of physicomimetics. In International workshop on swarm robotics (pp. 84–97). Springer. https://doi.org/10.1007/978-3-540-30552-1_8 Steffen, A. D. (2020). Drones are delivering medical supplies to the isle of wight. https://www.intelligentliving.co/drones-medical-supplies-isle-of-wight/ Stolfi, D. H., Brust, M. R., & Danoy, G. et al. (2020). A cooperative coevolutionary approach to maximise surveillance coverage of uav swarms. In 2020 IEEE 17th annual consumer communications & networking conference (CCNC) (pp. 1–6). IEEE. https://doi.org/10.1109/CCNC46108.2020.9045643 Tedim, F., Leone, V., Amraoui, M., et al. (2018). Defining extreme wildfire events: Difficulties, challenges, and impacts. Fire, 1(1), 9. https://doi.org/10.3390/fire1010009. Viseras, A., Meißner, M., & Marchal, J. (2021). Wildfire front monitoring with multiple uavs using deep q-learning. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3055651. Yang, X. S. (2014). Swarm intelligence based algorithms: A critical analysis. Evolutionary Intelligence, 7(1), 17–28. https://doi.org/10.1007/s12065-013-0102-2. Yang, J., Qian, J., & Gao, H. (2021). Forest wildfire monitoring and communication uav system based on particle swarm optimization. Journal of Physics: Conference Series.https://doi.org/10.1016/j.jocs.2019.04.009.