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A Performance Study of Bio-Inspired algorithms in Autonomous Landing of Unmanned Aerial Vehicle

Procedia Computer Science 2020年 171卷 1449-1458页

作者： Harun Surej Ilango Ramanathan R. Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham– 641112 India

The complete operational automation of fixed wing Unmanned Aerial Vehicle (UAV) involves the autonomous operations across take-off, cruising and landing. Among all these stages the landing stage is the most crucial one. During landing, it is important for the UAV to maintain a constant speed and glide slope to ensure the stability and a successful touchdown on the runway. Also, it is important for a UAV to estimate the accurate point of landing in a minimal amount of time. Embedding Bio-inspiring algorithms in UAV control systems helps in accurate estimation of the landing point in a minimal amount of time. In this research work, the Bio-inspired optimization algorithms bats optimization algorithm, Moth Flame optimization algorithm and Artificial Bee Colony algorithm are used in determining the coordinates (points) of the computed path and to determine the optimal point of landing which ensures the above said parameters are within the operational limits of the UAV. The objective of this research work is to determine the path from the computed points and to find the optimal landing point in a minimal amount of time. The difference between the original points of the actual path and the derived computed points of the estimated path is measured as the error rate. The performance of the algorithms is analyzed in terms of two trade-off parameters, the time taken to compute the landing point and the accuracy in predicting the landing point. The empirical results show that the Moth Flame optimization algorithm takes less time to compute the optimal point with minimal error among the three optimization algorithms taken up for the study.

关键词： Fixed-wing Unmanned Aerial Vehicles (UAVs) Bio-inspired optimization algorithms bats optimization algorithm Moth Flame optimization algorithm Artificial Bee Colony algorithm

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