Abstract
Path planning is an important aspect of an autonomous Unmanned Ariel Vehicle (UAV). As finding the best path is a non-deterministic problem, meta-heuristic algorithms proved to be a better choice in recent years. Particle Swarm Optimization (PSO) is one of the widely applied meta-heuristic algorithms for non-deterministic problem due to simplicity and ease of implementation. However, the lack of diversity in the particles in PSO algorithm generates a low-quality path for UAV. In this paper, we presented a modified PSO algorithm called n-PSO. In the algorithm, a dynamic neighborhood approach is proposed to improve the diversity of the particles. The n-PSO algorithm is applied to UAV path planning and simulated in a 3D environment. We compared the algorithm with two widely used versions of PSO for UAV path planning. The proposed algorithm showed significant improvement in particles diversity that plays an important role to produce better UAV path. At the end, we presented a time cost analysis of the algorithm for UAV path planning.
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References
Zhao, Y., Zheng, Z., Liu, Y.: Survey on computational-intelligence-based UAV path planning. Knowl.-Based Syst. 158, 54–64 (2018)
Sebbane, Y.B.: Intelligent Autonomy of UAVs: Advanced Missions and Future Use. Chapman and Hall/CRC, London (2018)
Cheng, Z., Wang, E., Tang, Y., Wang, Y.: Real-time path planning strategy for UAV based on improved particle swarm optimization. JCP 9(1), 209–214 (2014)
Huang, C., Fei, J.: UAV path planning based on particle swarm optimization with global best path competition. Int. J. Pattern Recogn. Artif. Intell. 32(06), 1859008 (2018)
BoussaïD, I., Lepagnot, J., Siarry, P.: A survey on optimization metaheuristics. Inf. Sci. 237, 82–117 (2013)
Zhang, Y., Wang, S., Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Probl. Eng. 2015, 1–38 (2015)
Kennedy, J.: Swarm intelligence. In: Zomaya, A.Y. (ed.) Handbook of Nature-Inspired and Innovative Computing, pp. 187–219. Springer, Boston (2006). https://doi.org/10.1007/0-387-27705-6_6
Zafar, M.N., Mohanta, J.: Methodology for path planning and optimization of mobile robots: a review. Procedia Comput. Sci. 133, 141–152 (2018)
Pehlivanoğlu, Y.V.: A new particle swarm optimization method for the path planning of UAV in 3D environment. J. Aeronaut. Space Technol. 5(4), 1–14 (2012)
Sanchez-Garcia, J., Reina, D., Toral, S.: A distributed PSO-based exploration algorithm for a UAV network assisting a disaster scenario. Future Gener. Comput. Syst. 90, 129–148 (2019)
Tharwat, A., Elhoseny, M., Hassanien, A.E., Gabel, T., Kumar, A.: Intelligent Bézier curve-based path planning model using chaotic particle swarm optimization algorithm. Cluster Comput. 22(2), 4745–4766 (2018). https://doi.org/10.1007/s10586-018-2360-3
Roberge, V., Tarbouchi, M., Labonté, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Ind. Inform. 9(1), 132–141 (2013)
Higashi, N., Iba, H.: Particle swarm optimization with Gaussian mutation. In: Proceedings of the 2003 IEEE Swarm Intelligence Symposium (SIS 2003) (Cat. No. 03EX706), pp. 72–79. IEEE (2003)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No. 98TH8360), pp. 69–73. IEEE (1998)
Suganthan, P.N.: Particle swarm optimiser with neighbourhood operator. In: Proceedings of the 1999 Congress on Evolutionary Computation (CEC 1999) (Cat. No. 99TH8406), vol. 3, pp. 1958–1962. IEEE (1999)
Howden, D.J.: Bushfire surveillance using dynamic priority maps and swarming unmanned aerial vehicles. Ph.D. thesis, Ph.D. dissertation, Swinburne University of Technology, Hawthorn, Victoria (2013)
Khan, A.: Coordinated unmanned aerial vehicles for surveillance of targets. Ph.D. thesis, Queen Mary University of London (2015)
Ravankar, A., Ravankar, A., Kobayashi, Y., Hoshino, Y., Peng, C.C.: Path smoothing techniques in robot navigation: state-of-the-art, current and future challenges. Sensors 18(9), 3170 (2018)
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science (MHS 1995), pp. 39–43. IEEE (1995)
Olorunda, O., Engelbrecht, A.P.: Measuring exploration/exploitation in particle swarms using swarm diversity. In: 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 1128–1134. IEEE (2008)
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This work is supported by Key Lab of Information Network Security, Ministry of Public Security, Shanghai 201804, China.
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Nayeem, G.M., Fan, M., Li, S., Ahammad, K. (2020). A Modified Particle Swarm Optimization for Autonomous UAV Path Planning in 3D Environment. In: Bhuiyan, T., Rahman, M.M., Ali, M.A. (eds) Cyber Security and Computer Science. ICONCS 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 325. Springer, Cham. https://doi.org/10.1007/978-3-030-52856-0_14
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