Efficiency-first spraying mission arrangement optimization with multiple UAVs in heterogeneous farmland with varying pesticide requirements

Abstract

Combining multiple crop protection Unmanned Aerial Vehicles (UAVs) as a team for a scheduled spraying mission over farmland now is a common way to significantly increase efficiency. However, given some issues such as different configurations, irregular borders, and especially varying pesticide requirements, it is more important and more complex than other multi-Agent Systems (MASs) in common use. In this work, we focus on the mission arrangement of UAVs, which is the foundation of other high-level cooperations, systematically propose Efficiency-first Spraying Mission Arrangement Problem (ESMAP), and try to construct a united problem framework for the mission arrangement of crop protection UAVs. Besides, to characterise the differences in sub-areas, the varying pesticide requirement per unit is well considered based on Normalized Difference Vegetation Index (NDVI). Firstly, the mathematical model of multiple crop-protection UAVs is established and ESMAP is defined. Furthermore, an acquisition method of a farmland’s NDVI map is proposed, and the calculation method of pesticide volume based on NDVI is discussed. Secondly, an improved Genetic Algorithm (GA) is proposed to solve ESMAP, and a comparable combination algorithm is introduced. Numerical simulations for algorithm analysis are carried out within MATLAB, and it is determined that the proposed GA is more efficient and accurate than the latter. Finally, a mission arrangement tested with three UAVs was carried out to validate the effectiveness of the proposed GA in spraying operation. Test results illustrated that it performed well, which took only 90.6 % of the operation time taken by the combination algorithm.

Keywords

• Efficiency-first optimization
• Mission arrangement
• Multi-UAVs
• Heterogeneous farmland