Abstract
For the rising hazard of pirate attacks, unmanned aerial vehicle (UAV) swarm monitoring is a promising countermeasure. Previous monitoring methods have deficiencies in either adaptivity to dynamic events or simple but effective path coordination mechanisms, and they are inapplicable to the large-area, low-target-density, and long-duration persistent counter-piracy monitoring. This article proposes a self-organized UAV swarm counter-piracy monitoring method. Based on the pheromone map, this method is characterized by (1) a reservation mechanism for anticipatory path coordination and (2) a ship-adaptive mechanism for adapting to merchant ship distributions. A heuristic depth-first branch and bound search algorithm is designed for solving individual path planning. Simulation experiments are conducted to study the optimal number of plan steps and adaptivity scaling factor for different numbers of UAVs. Results show that merely decreasing revisit intervals cannot effectively reduce pirate attacks. Without the ship-adaptive mechanism, the proposed method reduces up to 87.2%, 43.2%, and 5.5% of revisit intervals compared to the Lèvy Walk method, the sweep method, and the baseline self-organized method, respectively, but cannot reduce pirate attacks; while with the ship-adaptive mechanism, the proposed method can reduce pirate attacks by up to 6.7% compared to the best of the baseline methods.
- IMB Piracy Reporting Centre. 2018. Piracy and armed robbery against ships. ICC International Maritime Bureau 2017 Annual Report, London, UK.Google Scholar
- Maisie Pigeon, Emina Sadic, Sean Duncan, Chuck Ridgway, and Kelsey Soeth. 2018. The state of maritime piracy 2017: Assessing the economic and human cost. One Earth Future Foundation Report, Broomfeld, CO.Google Scholar
- Curtis Bell and Ben Lawellin. 2017. Stable seas: Somali waters. One Earth Future Foundation Report, Broomfeld, CO.Google Scholar
- ReCAAP. 2018. Piracy and armed robbery against ships in Asia. ReCAAP 3rd quarter 2018 report, Singapore.Google Scholar
- Ondřej Vaněk, Michal Jakob, Ondřej Hrstka, and Michal Pěchouček. 2013. Agent-based model of maritime traffic in piracy-affected waters. Transport. Res. Part C: Emerg. Technol. 36 (Nov. 2013), 157--176. DOI:http://dx.doi.org/10.1016/j.trc.2013.08.009Google Scholar
- A. Emre Varol and Murat M. Gunal. 2015. Simulating prevention operations at sea against maritime piracy. J. Operat. Res. Society. 66, 12 (Dec. 2015), 2037--2049. DOI:http://dx.doi.org/10.1057/jors.2015.34Google Scholar
- The Diplomat. 2017. Countering the “pirates’ paradise”: Unmanned systems and marine security in Southeast Asia. Retrieved from https://thediplomat.com/2017/10/countering-the-pirates-paradise-unmanned-systems-and-marine-security-in-outheast-asia/.Google Scholar
- ATAC Global. 2019. ATAC anti-piracy drone-UAV advantage. Retrieved from https://www.atacglobal.com/atac-anti-piracy-drone-uav-advantage.Google Scholar
- Turgut Kaymal. 2016. Unmanned aircraft systems for maritime operations: Choosing “a” good design for achieving operational effectiveness. In Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS’16). IEEE, Piscataway, NJ, 763--768. DOI:http://dx.doi.org/10.1109/ICUAS.2016.7502634Google ScholarCross Ref
- Chen Gao, Ziyang Zhen, and Huajun Gong. 2016. A self-organized search and attack algorithm for multiple unmanned aerial vehicles. Aerosp. Sci. Technol. 54 (July 2016), 229--240. DOI:http://dx.doi.org/10.1016/j.ast.2016.03.022Google Scholar
- Ryan C. Skeele and Geoffrey A. Hollinger. 2016. Aerial vehicle path planning for monitoring wildfire frontiers. In Field and Service Robotics, David S. Wettergreen and Timothy D. Barfoot (Eds.). Springer Tracts in Advanced Robotics, Vol. 113. Springer, Cham, 455--467. DOI:http://dx.doi.org/10.1007/978-3-319-27702-8_30Google Scholar
- Cyrille Berger, Mariusz Wzorek, Jonas Kvarnstrom, Gianpaolo Conte, Patrick Doherty, and Alexander Eriksson. 2016. Area coverage with heterogeneous UAVs using scan patterns. In Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR’16). IEEE, Piscataway, NJ, 233--236. DOI:http://dx.doi.org/10.1109/SSRR.2016.7784325Google ScholarCross Ref
- Haifeng Xu, Benjamin Ford, Fei Fang, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Mustapha Nsubaga, and Joshua Mabonga. 2017. Optimal patrol planning for green security games with black-box attackers. In Proceedings of the Conference on Decision and Game Theory for Security (GameSec’17). Lecture Notes in Computer Science, Vol. 10575. Springer, Cham, 458--477. DOI:http://dx.doi.org/10.1007/978-3-319-68711-7_24.Google ScholarCross Ref
- Haifeng Xu, Kai Wang, Phebe Vayanos, and Milind Tambe. 2018. Strategic coordination of human patrollers and mobile sensors with signaling for security. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18).Google Scholar
- Shahrzad Gholami, Amulya Yadav, Long Tran-Thanh, Bistra Dilkina, and Milind Tambe. 2019. Don't put all your strategies in one basket: Playing green security games with imperfect prior knowledge. In Proceedings of the 18th International Conference on Autonomous Agents and Multiagent Systems. IFAAMAS, 395--403.Google Scholar
- Fabio A. A. Andrad, Rune Storvold, and Tor Arne Johansen. 2017. Autonomous UAV surveillance of a ship's path with MPC for maritime situational awareness. In Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS’17). IEEE, Piscataway, NJ, 633--639. DOI:http:/dx.doi.org/10.1109/ICUAS.2017.7991361Google ScholarCross Ref
- Keisuke Watanabe, Kyoko Takashima, Kazuho Mitsumura, Koshi Utsunomiya, and Shiyun Takasaki. 2017. Experimental study on the application of UAV drone to prevent maritime pirate attacks. Int. J. Marine Navig. Safety Sea Transport. 11, 4 (Dec. 2017), 705--710. DOI:http://dx.doi.org/10.12716/1001.11.04.18Google Scholar
- Yan Li, Hai Chen, Meng Joo Er, and Xinmin Wang. 2011. Coverage path planning for UAVs based on enhanced exact cellular decomposition method. Mechatronics 21, 5 (Aug. 2011), 876--885. DOI:http://dx.doi.org/10.1016/j.mechatronics.2010.10.009Google ScholarCross Ref
- José J. Acevedo, Begoña C. Arrue, Iván Maza, and Anibal Ollero. 2015. Distributed cooperation of multiple UAVs for area monitoring missions. In Motion and Operation Planning of Robotic Systems: Background and Practical Approaches, Giuseppe Carbone and Fernando Gomez-Bravo (Eds.). Springer, Cham, Switzerland, 471--494. DOI:http://dx.doi.org/10.1007/978-3-319-14705-5_16Google Scholar
- Marina E. Wosniack, Marcos C. Santos, Ernesto P. Raposo, Gandhi M. Viswanathan, and Marcos G. E. Da Luz. 2017. The evolutionary origins of Levy walk foraging. PLOS Comput. Biol. 13, 10 (Oct. 2017). DOI:https://doi.org/10.1371/journal.pcbi.1005774Google ScholarCross Ref
- G. M. Fricke, J. P. Hecker, J. L. Cannon, and M. E. Moses. 2016. Immune-inspired search strategies for robot swarms. Robotica 34, 8 (Aug. 2016), 1791--1810. DOI:http://dx.doi.org/10.1017/S0263574716000382Google ScholarCross Ref
- Chang-jian Ru, Xiao-ming Qi, and Xu-ning Guan. 2015. Distributed cooperative search control method of multiple UAVs for moving target. Int. J. Aerosp. Eng. 317953 (2015). DOI:http://dx.doi.org/10.1155/2015/317953Google Scholar
- Matej Paradzik and Gökhan Ince. 2016. Multi-agent search strategy based on digital pheromones for UAVs. In Proceedings of the 24th Signal Processing and Communication Application Conference (SIU’16). IEEE, Piscataway, NJ, 233--236. DOI:http://dx.doi.org/10.1109/SIU.2016.7495720Google ScholarCross Ref
- John A. Sauter, Robert Matthews, H. Van Dyke Parunak, and Sven A. Brueckner. 2005. Performance of digital pheromones for swarming vehicle control. In Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems. ACM, New York, NY, 903--910. DOI:http://dx.doi.org/10.1145/1082473.1082610Google Scholar
- P. B. Sujit and Debasish Ghose. 2011. Self assessment-based decision making for multiagent cooperative search. IEEE Trans. Automat. Sci. Eng. 8, 4 (Oct. 2011), 705--719. DOI:http://dx.doi.org/10.1109/TASE.2011.2155058Google ScholarCross Ref
- Jinwen Hu, Lihua Xie, Jun Xu, and Zhao Xu. 2014. Multi-agent cooperative target search. Sensors 14, 6 (June 2014), 9408--9428, DOI:http://dx.doi.org/10.3390/s140609408Google ScholarCross Ref
- Xiaoting Ji, Xiangke Wang, Yifeng Niu, and Lincheng Shen. 2015. Cooperative search by multiple unmanned aerial vehicles in a nonconvex environment. Math. Prob. Eng. 196730 (2015). DOI:http://dx.doi.org/10.1155/2015/196730Google Scholar
- Mario G. C. A. Cimino, Alessandro Lazzeri, and Gigliola Vaglini. 2015. In Proceedings of the 6th International Conference on Information, Intelligence, Systems and Applications (IISA’15). IEEE. Piscataway, NJ, 1--6. DOI:http://dx.doi.org/10.1109/IISA.2015.7387990Google Scholar
- Jinwen Hu, Lihua Xie, Kai-Yew Lum, and Jun Xu. 2013. Multiagent information fusion and cooperative control in target search. IEEE Trans. Contr. Syst. Technol. 21, 4 (July 2013), 1223--1235. DOI:http://dx.doi.org/10.1109/TCST.2012.2198650Google ScholarCross Ref
- Rutger Claes, Tom Holvoet, and Danny Weyns. 2011. A decentralized approach for anticipatory vehicle routing using delegate multiagent systems. IEEE Trans. Intell. Transport. Syst. 12, 2 (June 2011), 364--373. DOI:http://dx.doi.org/10.1109/TITS.2011.2105867Google ScholarDigital Library
- Min Zhang, Pengfei Tian, and Xin Chen. 2016. UAV guidance law for circumnavigating and tracking ground target and its stability proof. Acta Aeronaut. Astronaut. Sinica 37, 11 (Nov. 2016). 3424--3434. DOI:http://dx.doi.org/10.7527/S1000-6893.2016.0002Google Scholar
- Graham C. Goodwin, Maria M. Seron, and Jose A. De Dona. 2005. Constrained Control and Estimation: An Optimisation Approach. Springer, London, UK.Google Scholar
- Roni Stern, Scott Kiesel, Rami Puzis, Ariel Felner, and Wheeler Ruml. 2015. Max is more than min: Solving maximization problems with heuristic search. In Proceedings of the 24th International Joint Conference on Artificial Intelligence. AAAI Press, Palo Alto, CA, 4324--4330Google Scholar
- Lester E. Dubins. 1957. On curves of minimal length with a constraint on average curvature, and with prescribed initial and terminal positions and tangents. Amer. J. Math. 79, 3 (July 1957), 497--516. DOI:http://dx.doi.org/10.2307/2372560Google ScholarCross Ref
- Randal W. Bread and Timothy W. McLain. 2012. Small Unmanned Aircraft: Theory and Practice. Princeton University Press, Princeton, NJ.Google Scholar
- Averill M. Law. 2015. Simulation Modeling and Analysis (5th. ed.). McGraw-Hill, New York, NY.Google Scholar
- Alexander I. J. Forrester, András Sóbester, and Andy J. Keane. 2008. Engineering Design via Surrogate Modelling: A Practical Guide. Wiley, West Sussex, UK.Google Scholar
- Injong Rhee, Minsu Shin, Seongik Hong, Kyunghan Lee, Seong Joon Kim, and Song Chong. 2011. On the levy-walk nature of human mobility. IEEE/ACM Trans. Netw. 19, 3 (2011), 630--643. DOI:http://dx.doi.org/10.1109/TNET.2011.2120618Google ScholarDigital Library
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- UAVs vs. Pirates: An Anticipatory Swarm Monitoring Method Using an Adaptive Pheromone Map
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