TY - GEN
T1 - Metaheuristic Optimization for Three Dimensional Path Planning of UAV
AU - Sreelakshmy, K.
AU - Gupta, Himanshu
AU - Ansari, Irshad Ahmad
AU - Sharma, Sachin
AU - Goyal, Kapil Kumar
AU - Verma, Om Prakash
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85132008314
UR - https://www.scopus.com/pages/publications/85132008314#tab=citedBy
U2 - 10.1007/978-981-19-0707-4_71
DO - 10.1007/978-981-19-0707-4_71
M3 - Conference contribution
AN - SCOPUS:85132008314
SN - 9789811907067
T3 - Lecture Notes in Networks and Systems
SP - 791
EP - 802
BT - Soft Computing
A2 - Kumar, Rajesh
A2 - Ahn, Chang Wook
A2 - Sharma, Tarun K.
A2 - Verma, Om Prakash
A2 - Agarwal, Anand
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on Soft Computing: Theories and Applications, SoCTA 2021
Y2 - 17 December 2021 through 19 December 2021
ER -