A Multi-Step CNN-Based Estimation of Aircraft Landing Gear Angles

This paper presents a method for measuring aircraft landing gear angles based on a monocular camera and the CAD aircraft model. Condition monitoring of the aircraft landing gear is a prerequisite for the safe landing of the aircraft. Traditional manual observation has an intense subjectivity. In recent years, target detection models dependent on deep learning and pose estimation methods relying on a single RGB image have made significant progress. Based on these advanced algorithms, this paper proposes a method for measuring the actual angles of landing gears in two-dimensional images. A single RGB image of an aircraft is inputted to the target detection module to obtain the key points of landing gears. The vector field network votes the key points of the fuselage after extraction and scale normalization of the pixels inside the aircraft prediction box. Knowing the pixel position of the key points and the constraints on the aircraft, the angle between the landing gear and fuselage plane can be calculated even without depth information. The vector field loss function is improved based on the distance between pixels and key points, and synthetic datasets of aircraft with different angle landing gears are created to verify the validity of the proposed algorithm. The experimental results show that the mean error of the proposed algorithm for the landing gears is less than 5 degrees on the light-varying dataset.

Friction analysis of aircraft landing gears due to landing impact

In this work, a six degrees of freedom heave-pitch mathematical model has been developed for an aircraft with main and nose oleo-pneumatic landing gear. Nonlinearities in stiffness, damping, and bending characteristics of landing gears and tires are incorporated in the model. Friction is an incidental and inevitable reaction that sticks along with the strut motion during the event of ground contact. The friction generated in the landing gear is the sum of the contribution from bearings and seals fitted in the landing gear. This study has focused on investigating the amount of frictional resistance gained by the struts while an aircraft is landing at various sink rates. The strut vertical forces, seal friction forces, and bearing friction forces generated in the main and nose landing gear during touchdown have been presented in this work. This preliminary estimation of friction forces for a range of sink rates aids the designer in developing optimal geometric or strut parameters in the design stage. This work also helps to calculate total landing loads for the certification of the landing gear.

Semi-Active Control for a Helicopter with Multiple Landing Gears Equipped with Magnetorheological Dampers

Due to their extensive use in various applications, helicopters need to be able to land in a variety of conditions. Typically, a helicopter landing gear system with skids or passive wheel-dampers is designed based on only one critical touchdown condition. Thus, this helicopter landing gear system may not perform well in different landing conditions. A landing gear system with magnetorheological (MR) dampers would be a promising candidate to solve this problem. However, a semi-active controller must be designed to determine the electrical current applied to the MR damper to directly manage the damping force. This paper presents a new skyhook controller, called the skyhook extended controller, for a helicopter with multiple landing gears equipped with MR dampers to reduce the helicopter’s acceleration at the center of gravity in off-normal landing attitude conditions. A 9-DOF simulation model of a helicopter with multiple MR landing gears was built using RECURDYN. To verify the effectiveness of the proposed controller, co-simulations were executed with RECURDYN and MATLAB in different initial pitch and roll angles at touchdown. The main simulation results show that the proposed controller can greatly decrease the peak and rms acceleration of the helicopter’s center of gravity compared to a traditional skyhook controller and passive damper.

Stabilization System for UAV Landing on Rough Ground by Adaptive 3D Sensing and High-Speed Landing Gear Adjustment

This paper proposes a real-time landing gear control system based on adaptive and high-speed 3D sensing to enable the safe landing of unmanned aerial vehicles (UAVs) on rough ground. The proposed system controls the measurement area on the ground according to the position and attitude of the UAV and enables high-speed 3D sensing of the focused areas in which the landing gears are expected to contact the ground. Furthermore, the spatio-temporal resolution of the measurement can be improved by focusing a measurement area and the proposed system can recognize the detailed shape of the ground and the dynamics. These detailed measurement results are used to control the lengths of the landing gears at high speed, and it is ensured that all the landing gears contact the ground simultaneously to reduce the instability at touchdown. In the experiment setup, the proposed system realized high-speed sensing for heights of contact points of two landing gears at a rate of 100 Hz and almost simultaneous contact on ground within 36 ms.