AIR COMBAT WITH PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM
Keywords:
Air Combat Manoeuvring, Path Planning, Artificial Intelligence, Particle Swarm Optimization, Genetic AlgorithmAbstract
The future of aircrafts is in unmanned aerial vehicles (UAVs), and any improvement in UAVs will play an important role, especially when it comes to intelligence and capabilities for air combat manoeuvring. The ultimate goal in such work is to bring computers to the level of a pilot’s intelligence capability in air combat. In order to achieve this goal, operations research is required. The present study is based on the fight or flight situation in air combat manoeuvring and aims to improve unmanned aircrafts and better understand the difficulties of modelling intelligence. Since the project’s focus is on the problem of path planning for moving targets and enemy situations, particle swarm optimization and genetic algorithms are modelled and tested against each other in a dog fight scenario. Also, multiple targets and enemies’ scenarios are developed to compare them against each other. Moreover, imperfect information affect and dynamic environment are evaluated in this research and required actions and options are analysed. Overall, this research aims to show the importance of artificial intelligence, articulate the role of the operations research and assess the implementation of intelligence through certain heuristics.
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[2] M. Hoffman, "UAV pilot career field could save $1.5B," Airforcetimes, 1 March 2009. [Online]. Available: http://www.airforcetimes.com/article/20090301/NEWS/903010326/UAV-pilot-career-field-could-save-1-5B. [Accessed 12 July 2013].
[3] A. Y. Javaid, W. Sun, V. K. Devabhaktuni and m. Alam, "Cyber Security Threat Analysis and Modeling of an Unmanned Aerial Vehicle System," in 2012 IEEE Conference on Technologies for Homeland Security, 2012.
[4] J. Luo, "Some New Optimal Control Problems in UAV Cooperative Control with Information Flow Constraints," in American Control Conference 2003, 2003.
[5] Y. V. Pehlivanoğlu, "A New Particle Swarm Optimization Method for the Path Planning of UAV in 3D Environment," Havacılık ve uzay teknolojileri dergisi, vol. 5, no. 4, pp. 1-14, 2012.
[6] T. Furukawa, F. Bourgault, B. Lavis and H. F. Durrant-Whyte, "Recursive Bayesian Search-and-Tracking Using Coordinated UAVs for Lost Targets," in Proceedings of the 2006 IEEE International Conference on Robotics and Automation, Orlando, 2006.
[7] R. D. Myungsoo Jun, "Path Planning for Unmanned Aerial Vehicles in Uncertain and Adversarial Environments," Cooperative Systems, vol. 1, pp. 95-110, 2003.
[8] J. Kim, "Discrete approximations to continuous shortest-path: application to minimum-risk path planning for groups of UAVs," Decision and Control, vol. 2, pp. 1734-1740, 2003.
[9] C. Sabo and K. Cohen, "SMART Heuristic for Pickup and Delivery Problem (PDP) with Cooperative UAVs," in Infotech@Aerospace, 2011.
[10] A. Richards, J. Bellingham, M. Tillerson and J. How, "Coordination and Control of Multiple UAVs," in AIAA Guidance, Navigation, and Control Conference and Exhibit, 2002.
[11] D. E. Goldberg and K. Sastry, Genetic Algorithm, New York: Springer-Verlag New York Incorporated, 2002.
[12] H. Zhu, Z. M. and R. Alkins, "Group Role Assignment via a Kuhn–Munkres Algorithm-Based Solution," IEEE Transactions On Systems, Man, And Cybernetics—Part A: Systems And Humans, vol. 42, no. 3, pp. 739-750, 2012.
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