Human-Machine Cooperative Control of UAV in Complex Indoor Environments

Unmanned aerial vehicle (UAV), as a current research hotspot, has a vast potential application prospect in military and civilian sectors, and it helps to deal with the coping ability of dangerous and complex scenes. Most of the UAVs based on autonomous control systems or manual control have poor adaptability to complex environments and weak anti-interference ability, which limits the application scope of UAVs. In this paper, authority allocation is applied to the control system of UAV, and the UAV system based on human-machine authority allocation is constructed by combining manual control with model predictive control (MPC). The performance of the system is verified through UAV simulation experiments. The experimental results show that human-machine collaborative control performs better than the manual control in terms of average completion time and collision rate, and the proposed system can improve the safety of the UAV control, and at the same time enhance the task completion efficiency of the UAV system.