Optimization Of Pesticide Spraying Tasks Via Multi-UAVs Using Genetic Algorithm

Unmanned aerial vehicles(UAVs)have been widely applied in precision seeding,vegetation testing,pesticide spraying,and other agricultural aviation operations.Using UAVs to spray pesticides has become an important way in the agricultural plant protection process.Thus,with the minimum of human intervention,using UAVs to complete agricultural aviation operations tasks autonomously has attracted widespread attention.The one key factors of affecting the pesticide spraying effects is the tasks allocation.However,the research of UAVs tasks allocation is still in the stage of development,there are many key theories and techniques to be solved.Therefore,the optimization of multi-UAV pesticide spraying assignments allocation has very important theory and practical significance.For the multi-UAVs pesticide spraying assignments allocation problem described in the thesis,we construct a model of team orienteering problem(TOP).We first introduce each UAV’s kinematic constraint and extend the Euclidean distance between fields to the Dubins path distance.We then analyze the two factors affecting the pesticide spraying effects,which are the type of pesticides and the temperature during the pesticide spraying.The time window of the pesticide spraying is dynamically generated according to the temperature and is introduced to the pesticide spraying efficacy function.Finally,according to the extensions,we propose a TOP with variable time windows and variable profits(DTOP-VTW-VP)model.We propose the genetic algorithm(GA)to solve the above model and give the methods of encoding,crossover,and mutation in the algorithm.We analyze and summarize the factors of affecting assignments allocation that are Dubins curve path,time windows and variable profits.And the optimal allocation schemes of multi-UAVs pesticide spraying assignments allocation problem is analyzed by simulation experiments.Based on this research,the experimental results show that this model and its solution method have clear advantages over the common manual allocation strategy,and can provide the same results as with the enumeration method in small-scale scenarios.In addition,the results also show that the algorithm parameter can affect the solution,and we provide the optimal parameters configuration for the algorithm.