Multi-UAV Coverage Path Planning and Control for Reconnaissance Task

In order to enhance the efficiency when multiple unmanned aerial vehicles (UAVs) carry out reconnaissance task in complex environments, a reconnaissance scheme that integrates task assignment, coverage path planning and trajectory tracking control is designed. Firstly, the task area is divided by using the divide areas based on robots initial positions (DARP) algorithm. Then, an improved spanning tree coverage (STC) algorithm with priority is designed to plan coverage path in each task area, and the Minimum Snap method is used to optimize and smooth the trajectory. Finally, the trajectory tracking controller based on nonlinear model predictive control (NMPC) is designed. The simulation results show that, compared with traditional STC algorithm, the improved STC method reduces the number of path turns by 37.7% and 30.2% respectively for a single UAV and multi-UAV. The NMPC controller can ensure that each UAV is able to track the pre-planned trajectory and avoid obstacles when facing external disturbances and unknown obstacles, and keeps the position tracking error within 0.5 meters. The designed scheme can ensure that each UAV successfully completes the reconnaissance task in the complex scenarios.