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The towbarless aircraft taxiing system (TLATS) consists of the towbarless towing vehicle (TLTV) and the aircraft. The tractor realizes the towing work by fixing the nose wheel. During the towing process, the tractor driver may cause the aircraft to collide with an obstacle because of the blind spot of vision leading to the accident. The special characteristics of aircraft do not allow us to modify the structure of the aircraft to achieve collision avoidance. In this paper, three degrees of freedom (DOE) kinematic model of the tractor system is established for each of the two cases of pushing and pulling the aircraft, and the relationship between the coordinates of each danger point and the relatively articulated angle of the TLATS and the velocity of the midpoint of the rear axle is derived.

In order to study the tire friction characteristics under wet skid surface, the “pseudo” hydrodynamic pressure bearing effect is used to be equivalent to the hydrodynamics of water film, and an advanced Lugre tire hydroplaning dynamic model is developed by combining the arbitrary pressure distribution function. The water hydroplaning dynamic tests were carried out for 285/70R19.5 tire under wet of different water film thickness and dry conditions, and the parameters of the advanced Lugre tire dynamic model were identified. The results show that the tire water-skiing model proposed in this paper can effectively simulate the friction characteristics of tires under different water film thicknesses. Under dry conditions, 0.5mm water film and 1mm water film road conditions, the relative errors of the maximum tire friction coefficient between the tested and advanced Lugre tire model are 1.11%, 0.12% and 0.16%, respectively.

When the aircraft towing operations are carried out in narrow areas such as the hangars or parking aprons, it has a high safety risk for aircraft that the wingtips may collide with the surrounding aircraft or the airport facility. A real-time trajectory prediction method for the towbarless aircraft taxiing system (TLATS) is proposed to evaluate the collision risk based on image recognition. The Yolov7 module is utilized to detect objects and extract the corresponding features. By obtaining information about the configuration of the airplane wing and obstacles in a narrow region, a Long Short-Term Memory (LSTM) encoder-decoder model is utilized to predict future motion trends. In addition, a video dataset containing the motions of various airplane wings in real traction scenarios is constructed for training and testing.