Multi-UAV reconnaissance task allocation for heterogeneous targets using grouping ant colony optimization algorithm

The study on multiple unmanned aerial vehicles (UAVs) reconnaissance task allocation problem is an important research field, which is significant for both military and civilian applications. This problem has often been considered as a multiple traveling salesman problem where the targets are considered as points. In this paper, we present a novel mathematical model that classifies heterogeneous targets as point targets, line targets and area targets to improve the fidelity of the model. It is a complex combinatorial optimization problem, for which we can hardly get an optimal solution as the scale of the problem expands. A new heuristic algorithm called grouping ant colony optimization algorithm is proposed for this new model. Compared with traditional ant colony algorithm, pheromone is divided into membership pheromone and sequence pheromone corresponding to grouping and permutation characteristics of the model, respectively. Also, negative feedback mechanism is introduced to accelerate convergence speed of the algorithm. The simulation results demonstrate that the new algorithm can consider comprehensively the performance of different UAVs and the characteristic of heterogeneous targets. It outperforms existing methods reported in the literature in terms of optimality of the result, and the advantage gets more obvious with the scale of reconnaissance task allocation problem expanding.