基于改进人工势场法和自适应神经网络的共轴双旋翼无人机避障飞行控制

We propose a method based on a flight situation diagram and an improved artificial potential field algorithm for obstacle avoidance of coaxial rotor unmanned aerial vehicle ( CR-UAV) flying in unknown and dangerous environments. First, a flight situation diagram is used to model obstacle information that considers the constraint conditions of flight control of CR-UAVs. By using this obstacle information, the CR-UAV can effectively avoid obstacles, avoid the problem of falling into a local minimum, and the control and obstacle avoidance abilities of CR-UAV are significantly improved. Second, the CR-UAV adopts the unknown parameter adaptive control, which is based on radial basis function neural network (RBFNN) approximation, to approximate estimation and realtime compensation of disturbances for obstacle avoidance. Attitude tracking is realized using the method of state error feedback, which can facilitate attitude stability in obstacle avoidance flight. Finally, we carry out some simulation experiments. The results of our simulation experiments show that the CR-UAV has a good ability for attitude stability in obstacle avoidance flight.