| Unmanned aerial vehicle(UAV)plays an increasingly important role in the modern life,and gradually completes the use of transition from military to civilian,from single function to functional diversification,from a single UAV to multi-UAV.The multi-UAV cooperative target search,which belongs to the research fields of UAV trajectory planning and multi intelligent robots collaborative work,is an important area in the research field of UAV.As a "flight robot",the cooperative work of UAVs occupies an indispensable position in the field of artificial intelligence.It is applied in the fields of coordinating attack in military warfare,regional surveillance in forest fires,the delivery of articles in earthquake relief,the spraying of drugs in agricultural management,transportation of objects in express logistics,and so on.In the field of multi-UAV cooperative target search,the aims for cooperating better,improving search efficiency and reducing search cost are particularly important.This paper first introduces the research background,significance and other related aspects of multiUAV cooperative target search,including the principle and application of ant colony algorithm.Then,the ant colony algorithm is used to solve the single-UAV target search problem,the multi-population ant colony algorithm is proposed to solve the multi-UAV target search problem,and the function of dynamic target revenue and collaboration is added to the algorithm,the entire algorithm flow is designed at last.In this paper,the pheromone has the role of guiding and enlightenment among same ant colony,but has a repulsive effect among different ant colony groups,and it also has a positive and negative feedback mechanism,which is more suitable for multi-objective combinatorial optimization problem.The roulette wheel selection method,which means to choose the large probability direction of the target region,to avoid the greedy choice,to increase the variety of choices,and to avoid the convergence speed too quickly.In the process of target search,the search profit of the target is dynamically adjusted after finding a certain target,to avoid the repeated search of the target due to the influence of the target expectation factor.The best path is selected in every generation during the update process of pheromone,promoting the algorithm to move closer to the better path,and accelerating the convergence of algorithm.Each ant is numbered,and different groups have the same ant number as a combination.Accurate combinations increase the solution space of ant population search.According to the different ways of ant dispatching,it is divided into multi-population ant colony algorithm search strategy A and strategy B,as a comparative experiment to verify its differences.To increase the complexity of the environment,the flight effect of UAV under the obstacle condition is verifed,such as increasing the no fly area in the target search area;All of these methods verify the great synergy effect of the UAVs.Through MATLAB simulation experiments,the several algorithms are verified in this article.A random search algorithm and a greedy search algorithm are designed as comparison experiments.Under the same conditions of the search environment and the basic parameters,a unique variable is set,multiple sets of experiments are compared.Various experiments are conducted to compare and analyze various algorithms,including the characteristics,advantages and disadvantages.The results show that the multi-group ant colony algorithm,especially the multi-group ant colony algorithm strategy B,has the best performance and is better than the random search algorithm and the greedy search algorithm.It has searched more targets with better collaborative performance and with lower search cost. |
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