Task Assignment Algorithm for Unmanned Systems Based on Step Clustering Ant Colony

The task assignment of unmanned systems is widely used and has been studied extensively. However, when there are a large number of target points in the task area, it is often a difficult problem to carry out reasonable task allocation. In view of the above situation, this paper proposes a task assignment based on step clustering ant colony algorithm. Firstly, by analyzing a large number of task scenarios in the case of target points, the task scenarios are transformed into NP-hard problems, and the mathematical model is established. Secondly, the k-means algorithm is used to cluster the target to be distributed, and the “elbow method” is used to determine the optimal clustering K value, the task area is divided into “target block”, and the problem dimension is reduced. After that, ant colony algorithm is used to solve the TSP problem for the target points in the “target block”, and the task assignment for a large number of targets is finally completed. Finally, simulation experiment is conducted to verify the effectiveness of the algorithm. The results show that the ant colony algorithm based on step clustering can effectively assign tasks with a large number of target points in the task area.