Ability-Restricted Indoor Reconnaissance Task Planning for Multiple UAVs

For indoor multi-task planning problems of small unmanned aerial vehicles (UAVs) with different abilities, task assignment and path planning play a crucial role. The multi-dimensional requirements of reconnaissance tasks bring great difficulties to the task execution of multi-UAV cooperation. Meanwhile, the complex internal environment of buildings has a great impact on the path planning of UAVs. In this paper, the ability-restricted indoor reconnaissance task-planning (ARIRTP) problem is solved by a bi-level problem-solving framework. In the upper level, an iterative search algorithm is used to solve the task assignment problem. According to the characteristics of the problem, a solution-space compression mechanism (SSCM) is proposed to exclude solutions that do not satisfy the task requirements. In the lower level, based on a topological map, the nearest neighbor (NN) algorithm is used to quickly construct the path sequence of a UAV. Finally, the genetic algorithm (GA) and simulated annealing (SA) algorithm are applied to the upper level of the framework as iterative search algorithms, which produces two hybrid algorithms named the GA-NN and SA-NN, respectively. ARIRTP instances of different scales are designed to verify the effectiveness of the SSCM and the performance of the GA-NN and SA-NN methods. It is demonstrated that the SSCM can significantly compress the solution space and effectively improve the performance of the algorithms. The proposed bi-level problem-solving framework provides a methodology for the cooperation of multi-UAV to perform reconnaissance tasks in indoor environments. The experimental results show that the GA-NN and SA-NN methods can quickly and efficiently solve the ARIRTP problem. The performance of the GA-NN method is similar to that of the SA-NN method. The GA-NN method runs slightly faster. In large-scale instances, the performance of the SA-NN method is slightly better than that of the GA-NN method.