Laser tracking leader-follower automatic cooperative navigation system for UAVs

Currently, small payload and short endurance are the main problems of a single UAV in agricultural applications, especially in large-scale farmland. It is one of the important methods to solve the above problems of UAVs by improving operation efficiency through multi-UAV cooperative navigation. This study proposed a laser tracking leader-follower automatic cooperative navigation system for multi-UAVs. The leader in the cluster fires a laser beam to irradiate the follower, and the follower performs a visual tracking flight according to the light spot at the relative position of the laser tracking device. Based on the existing kernel correlation filter (KCF) tracking algorithm, an improved KCF real-time spot tracking method was proposed. Compared with the traditional KCF tracking algorithm, the recognition and tracking rate of the optimized algorithm was increased from 70% to 95% in indoor environment, and was increased from 20% to 90% in outdoor environment. The navigation control method was studied from two aspects: the distance coordinate transformation model based on micro-gyroscope and navigation control strategy. The error of spot position was reduced from the maximum (3.12, −3.66) cm to (0.14, 0.12) cm by correcting the deviation distance of the spot at different angles through a coordinate correction algorithm. An image coordinate conversion model was established for a complementary metal-oxide-semiconductor (CMOS) camera and laser receiving device at different mounting distances. The laser receiving device was divided into four regions, S0-S3, and the speed of the four regions is calculated using an uncontrollable discrete Kalman filter. The outdoor flight experiments of two UAVs were carried out outdoors using this system. The experiment results show that the average flight error of the two UAVs on the X-axis is 5.2 cm, and the coefficient of variation is 0.0181. The average flight error on the Z-axis is 7.3 cm, and the coefficient of variation is 0.0414. This study demonstrated the possibility and adaptability of the developed system to achieve multi-UAVs cooperative navigation.