Detection and Mapping of a Toxic Cloud Using UAVs and Emergent Techniques
Unmanned aerial vehicles have gained a lot of interest in recent times, due to their potential use in several civil applications. This paper focuses on the use of an autonomous swarm of drones to detect and map a toxic cloud. A possible real-world scenario is the accidental release of hazardous gases into the air, resulting from fire or an explosion at an industrial site. The proposed method is based on the concept of swarm intelligence: each drone (agent) performs basic interactions with the environment and with other drones, without need for a centralized coordination technique. More precisely, the method combines collision avoidance, flocking, stigmergy-based communication, and a cloud exploration behavior called inside-outside. For the experiments we developed a simulator using the NetLogo environment, and tested different combinations of these emergent behaviors on two scenarios. Parameters were tuned using differential evolution and separate scenarios. Results show that the combined use of different emergent techniques is beneficial, as the proposed method outperformed random flight as well as an exhaustive search throughout the explored area. In addition, results show little variance considering two different cloud shapes.
KeywordsDifferential evolution Drone Fractals Mapping NetLogo Quadcopter Stigmergy Swarm intelligence Toxic cloud Unmanned aerial vehicle
This work was carried out in the framework of the SCIADRO project, co-funded by the Tuscany Region (Italy) under the Regional Implementation Programme for Underutilized Areas Fund (PAR FAS 2007–2013) and the Research Facilitation Fund (FAR) of the Ministry of Education, University and Research (MIUR).
- 2.Brust, M.R., Zurad, M., Hentges, L., Gomes, L., Danoy, G., Bouvry, P.: Target tracking optimization of UAV swarms based on dual-pheromone clustering. In: 2017 3rd IEEE International Conference on Cybernetics (CYBCONF), pp. 1–8, June 2017Google Scholar
- 4.Cimino, M.G.C.A., Lazzeri, A., Vaglini, G.: Combining stigmergic and flocking behaviors to coordinate swarms of drones performing target search. In: 2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1–6, July 2015Google Scholar
- 5.Cimino, M.G.C.A., Lazzeri, A., Vaglini, G.: Using differential evolution to improve pheromone-based coordination of swarms of drones for collaborative target detection. In: Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2016, pp. 605–610. SCITEPRESS - Science and Technology Publications, Lda, Portugal (2016)Google Scholar
- 6.Fractint. https://www.fractint.org/. Accessed Sept 2018
- 7.Gallego, V., Rossi, M., Brunelli, D.: Unmanned aerial gas leakage localization and mapping using microdrones. In: 2015 IEEE Sensors Applications Symposium (SAS), pp. 1–6, April 2015Google Scholar
- 8.Hartmann, K., Giles, K.: UAV exploitation: a new domain for cyber power. In: International Conference on Cyber Conflict, CYCON 2016-Augus, pp. 205–221 (2016)Google Scholar
- 9.Kovacina, M.A., Palmer, D., Yang, G., Vaidyanathan, R.: Multi-agent control algorithms for chemical cloud detection and mapping using unmanned air vehicles. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, pp. 2782–2788, September 2002Google Scholar
- 10.Mani, G.: Mapping contaminated clouds using UAV - a simulation study. In: IEEE India Conference (2013)Google Scholar
- 12.Netlogo. http://ccl.northwestern.edu/netlogo. Accessed Sept 2018
- 14.Reynolds, C.W.: Flocks, herds and schools: a distributed behavioral model. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1987, pp. 25–34. ACM, New York (1987)Google Scholar
- 15.Rossi, M., Brunelli, D.: Analyzing the transient response of MOX gas sensors to improve the lifetime of distributed sensing systems. In: 5th IEEE International Workshop on Advances in Sensors and Interfaces IWASI, pp. 211–216, June 2013Google Scholar