Improved GWO Algorithm for UAV Path Planning on Crop Pest Monitoring.

Qun Ding; Xiaolong Xu

doi:10.9781/ijimai.2022.07.002

Authors

Qun Ding Nanjing University of Posts and Telecommunications
Xiaolong Xu Nanjing University of Posts and Telecommunications

DOI:

https://doi.org/10.9781/ijimai.2022.07.002

Keywords:

Grey Wolf Optimization, Planning, Pest Management, Simulated Annealing, Unmanned Aerial Vehicle

Supporting Agencies

We would like to thank the editors and reviewers in advance for their comments in helping us improve the quality of this paper. This work was supported by the National Natural Science Foundation of China under Grant 62072255.

Abstract

Agricultural information monitoring is the monitoring of the agricultural production process, and its task is to monitor the growth process of major crops systematically. When assessing the pest situation of crops in this process, the traditional satellite monitoring method has the defects of poor real-time and high operating cost, whereas the pest monitoring through Unmanned Aerial Vehicles (UAVs) effectively solves the above problems, so this method is widely used. An important key issue involved in monitoring technology is path planning. In this paper, we proposed an Improved Grey Wolf Optimization algorithm, IGWO, to realize the flight path planning of UAV in crop pest monitoring. A map environment model is simulated, and information traversal is performed, then the search of feasible paths for UAV flight is carried out by the Grey Wolf Optimization algorithm (GWO). However, the algorithm search process has the defect of falling into local optimum which leading to path planning failure. To avoid such a situation, we introduced the probabilistic leap mechanism of the Simulated Annealing algorithm (SA). Besides, the convergence factor is modified with an exponential decay mode for improving the convergence rate of the algorithm. Compared with the GWO algorithm, IGWO has the 8.3%, 16.7%, 28.6% and 39.6% lower total cost of path distance on map models with precision of 15, 20, 25 and 30 respectively, and also has better path planning results in contrast to other swarm intelligence algorithms.

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