Monitoring of Regions struck by Earthquake using Unmanned Aerial Systems Based on New Proposed GPO Meta-heuristic Technique

Document Type : Applied Article

Authors

1 MS , GIS Eng . Dept. of Surveying Eng., College of Engineering, University of Tehran, Tehran, Iran

2 Assosiation , GIS Eng . Dept. of Surveying Eng., College of Engineering, University of Tehran, Tehran, Iran

Abstract

Introduction: Subsequent to earthquakes, an updated and reliable map of environment often is not available; terrestrial substructure is either not appointed or ruined and mission time is turned into a vital element for hazard management, search, and rescue of patients. Referring to these facts, hazard management and monitoring of areas struck by earthquake is one of noteworthy applications of autonomous systems, which can enhance the excellence of search-relief missions. Utilizing of unmanned aerial systems as multi-sensor platforms in destruction surveillance is transformed into a novel economic procedure for enhancing autonomy and efficiency of natural hazard management tasks. Nowadays, tendency in the development of unmanned aerial systems is toward autonomous navigation or hybrid tasks. In this field, development of comprehensive, efficient methodologies for path planning, control, navigation, and processing of UAS sensor information has attracted an increasing momentum among researchers as one of the fundamental steps for achieving to autonomous navigation of aerial systems. In this article, a new meta-heuristic algorithm is proposed based on gravimetric measurements in physical geodesy studies. The aim of this algorithm is achieving an efficient method for solving complex optimization problems with different constraints such as hazards monitoring tasks. Evaluation of the precision, quality of results, success rates, and CPU running times of implemented algorithms demonstrates that gravitational potential optimization algorithm outperforms other methodologies for monitoring of regions struck by an earthquake.

Keywords


 
منابع
[1]. آزموده اردلان، ع؛ صفری، ع (1388). ژئودزی و گرانی، انتشارات دانشگاه تهران:56.
[2]. صفری، ع (1391). ژئودزی فیزیکی، انتشارات دانشگاه تهران:94.
[3]. مقیمی، الف (1393). دانش مخاطرات، انتشارات دانشگاه تهران:79.
[4]. Baiocchi, V., Dominici, D., Milone, M. V., & Mormile, M. (2013). Development of a Software to Plan UAVs Stereoscopic Flight: An Application on Post-Earthquake Scenario in L’Aquila City. In Computational Science and Its Apps–ICCSA 2013. Springer Berlin Heidelberg. (pp. 150-165).
[5]. Besada-Portas, E., de la Torre, L., de la Cruz, J. M., and de Andres-Toro, B. (2010). “Evolutionary trajectory planner for multiple UAVs in realistic scenarios.” IEEE Transactions on Robotics. Vol.26, No.4, PP.619-634.
[6]. Doherty, P. and Rudol, P. (2007). "A UAV search and rescue scenario with human body detection and geolocalization." in AI 2007: Advances in Artificial Intelligence (PP.1-13), Springer Berlin Heidelberg.
[7]. Duan, H. and Huang, L. (2014). "Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning." Neurocomputing. Vol.125, PP.166-171.
[8]. Duan, H. and Li, P. (2014). "Bio-inspired Computation in Unmanned Aerial Vehicles." Springer. Berlin, Heidelberg.
[9]. Ergezer, H. and Leblebicioğlu, K. (2013). "Path planning for UAVs for maximum information collection using evolutionary computation". IEEE Trans. Aerosp. Electron. Syst. Vol 49, No.1, PP.502–520.
[10]. Goerzen, C., Kong, Z. and Mettler, B. (2010). "A survey of motion planning algorithms from the perspective of autonomous UAV guidance." Journal of Intelligent and Robotic Systems. Vol.57, No.1-4, PP.65-100.
[11]. Goodrich, M., Morse, B., Gerhardt, D., Cooper, J., Quigley, M., Adams, J. and Humphrey, C. (2008). “Supporting wilderness search and rescue using a camera-equipped mini UAV” J. Field Robot. Vol. 25, No.1-2, PP.89–110.
[12]. Hargraves, C. R. and Paris, S. W. (1987). "Direct trajectory optimization using nonlinear programming and collocation." Journal of Guidance, Control, and Dynamics. Vol.10, No.4, PP.338-342.
[13]. Kapucu, N., Arslan, T., Demiroz, F. (2010)."Collaborative emergency management and national emergency management network." Disaster Prevention and Management. Vol.19, No.4, PP.452-468.
[14]. Karaboga, D., and Basturk, B. (2008). “On the performance of artificial bee colony (ABC) algorithm.” Applied soft computing. Vol.8, No.1, PP.687-697.
[15]. Pehlivanoglu, Y. V. (2012). "A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV." Aerospace Science and Technology. Vol.16, No.1, PP.47-55.
[16]. Tisdale, J., Kim, Z. and Hedrick, J. K. (2009). "Autonomous UAV path planning and estimation." Robotics & Automation Magazine, IEEE. Vol.16, No.2, PP.35-42.
[17]. Tomic, T., Schmid, K., Lutz, P., Domel, A., Kassecker, M., Mair, E. and Burschka, D. (2012). "Toward a fully autonomous uav: Research platform for indoor and outdoor urban search and rescue." Robotics & Automation Magazine, IEEE. Vol.19, No.3, PP.46-56.
[18]. Zengin, U. and Dogan, A. (2007). "Real-time target tracking for autonomous UAVs in adversarial environments: a gradient search algorithm." IEEE Transactions on Robotics. Vol.23, No.2, PP.294-307.
[20]. Zhan, Z. H., Zhang, J., Li, Y., Liu, O., Kwok, S. K., Ip, W. H., & Kaynak, O. (2010). “An efficient ant colony system based on receding horizon control for the aircraft arrival sequencing and scheduling problem.” Intelligent Transportation Systems, IEEE Transactions on, Vol.11, No.2, PP.399-412.
[20]. Zhang, B., Liu, W., Mao, Z., Liu, J. and Shen, L. (2014). "Cooperative and geometric learning algorithm (CGLA) for path planning of UAVs with limited information." Automatica. Vol.50, No.3, PP.809-820.
[21]. Zhuoning, D., Rulin, Z., Zongji, C., and Rui, Z. (2010). “Study on UAV path planning approach based on fuzzy virtual force.” Chinese Journal of Aeronautics. Vol 23, No.3, PP. 341-350.