Abstract
Unmanned Aerial Vehicles (UAVs) are used in numerous applications including civil, military and rescue operations. The UAVs are expected to autonomously travel through a collision-free, shortest route from the start position to the goal position. In this paper, three optimization algorithms, Grey Wolf Optimization (GWO), Archimedes Optimization Algorithm (AOA), and Particle Swarm Optimization (PSO) are employed to find out an optimized flyable route in a three-dimensional environment for the UAV. From the simulated results, it is found that the GWO achieves the minimum cost and therefore, outperforms the other two algorithms. However, the minimum time required to obtain the path is produced by AOA which is also comparable with the time estimated by GWO. Furthermore, the GWO achieves 8.4% and 80.5% lower mean cost than AOA, and PSO, respectively. This validates that GWO performs remarkably in a long run and therefore, should be employed for this task.
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Funding
This work was supported by the grant funded by Jagadish Chandra Bose Research Organisation (JCBRO) (File No. Research/sponsor/ADHAAR_2021).
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Sreelakshmy, K., Gupta, H., Ansari, I.A., Sharma, S., Goyal, K.K., Verma, O.P. (2022). Metaheuristic Optimization for Three Dimensional Path Planning of UAV. In: Kumar, R., Ahn, C.W., Sharma, T.K., Verma, O.P., Agarwal, A. (eds) Soft Computing: Theories and Applications. Lecture Notes in Networks and Systems, vol 425. Springer, Singapore. https://doi.org/10.1007/978-981-19-0707-4_71
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DOI: https://doi.org/10.1007/978-981-19-0707-4_71
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