Reconnaissance and photography are important applications of UAVs. The terrain coverage task is performed by the fix-wing UAVs flying along the circuits in this paper. Compared to the rotor UAV, the fix-wing UAV has a lower maneuverability, and its field of view is related to its flight direction. To avoid the frequent change of flight altitude, each UAV is assigned to fly at a flight level. In this cooperative terrain coverage task, the route of each UAV needs to be optimized, and the optimization objective is to minimize the sum of circuits length while satisfying the requirement on the coverage rate. To solve this problem, a random addition and deletion-based (A&D-based) algorithm is proposed, in which three key issues are addressed, i.e., initialization of feasible circuits, update of circuit and calculation of the shortest route between two waypoints. Besides, a hierarchical online route planning algorithm is developed to cope with the change of no-fly zone and realize the local adjustment. The strategy of determining the start and end points of online route planning is presented, and the idea of transition point is introduced to obtain the shortest route while maintaining the coverage rate. Simulation results demonstrate that the A&D-based algorithm is effective in generating the circuit for each UAV, and the performance is better than the popular algorithms. The factors affecting the performance of algorithm are also analyzed. The hierarchical local adjustment strategy is valid in modifying the circuit in each flight level when facing different situations.

Drone has become more and more popular in various civil applications due to the open of the low-altitude airspace and its easy operation. Unlike the common search and track task for the target, the new search and lock (SAL) task is focused on in this article. In this task, multiple drones first try to detect the moving ground target cooperatively. Then, they must lock the target by covering all the surrounding area of it at a low flight altitude, which indicates that the target can be watched clearly in all directions from then on. The SAL task can be applied in the panorama shot for the moving ground target. First, the low-altitude urban airspace is discretized into cubes, based on which the flight rules of drone are defined. The field of view (FOV) of drone is modeled considering the flight altitude and the block of buildings. For the cooperation among multiple drones, the constraints on the patten of waypoint, the communication distance, and the collision avoidance are all included. The goal in the search phase is to cover more area, which have not been visited recently to increase the probability of detecting the target, and it is expected to lock the target as soon as possible in the lock phase. A new swarm-based imitative learning optimization (SBILO) algorithm is proposed to determine the waypoint of drone in the search phase. To have a quick response to the escape behavior of the target in the lock phase, the waypoint of drone is generated in a distributed way to cover more surrounding area of the target and lock it gradually. The case of losing the target in the FOV of all drones is also addressed by covering more possible places where the target may appear. Simulation results demonstrate that the SAL task can be performed efficiently by the drones with the flight routes obtained by the proposed SBILO algorithm and the distributed asynchronous decision-make (DADM) approach.

There have been many applications of drones in urban environments, such as delivery, rescue, and surveillance. In a persistent surveillance task, the drones sometimes cannot complete it independently when some regions are required to be covered on the ground. For this purpose, unmanned aerial vehicles and unmanned ground vehicles (UAVs & UGVs) system is introduced to perform such a task in this article, and the goal is to generate the circular paths for the drones and the UGVs, respectively, to minimize their travel time of realizing a complete coverage. First, the cooperative path planning problem of UAVs & UGVs is formulated into a large-scale 0-1 optimization problem, in which the on-off states of the discrete points are to be optimized. Second, a hybrid algorithm integrating the estimation of distribution algorithm (EDA) and the genetic algorithm (GA) algorithm is proposed to solve the problem. The advantages of EDA and GA in the global and local search are fully taken considering the demands in different phases of the iterative process. A simple sweep-based approach is employed to determine the optimal sequence of passing the open points. Then, an online local adjustment strategy is also applied to address the changes of the requirements on covering the ground area. Simulation results demonstrate that the UAVs & UGVs system can enhance the efficiency of the task. The hybrid EDA-GA algorithm can greatly improve the performance of EDA and GA in terms of the quality and the stability of solutions. The online adjustment strategy is effective to maintain a complete coverage while minimizing the impact on the circular paths.