In complex terrain, such as uneven roads or irregular terrain, two-wheeled heavy-duty self-balancing vehicles are easily affected by external interference factors, causing rollover or rendering the vehicle unable to move, which poses a greater challenge to its stability control. Therefore, it is necessary to establish a kinematic model of a two-wheeled vehicle and design a control system to study its driving stability. This paper aims to study the stability control system of a two-wheeled self-balancing vehicle under complex terrain. First, a self-balancing vehicle modeling method based on complex terrain is designed. By analyzing the motion characteristics of the self-balancing vehicle, a kinematic model suitable for complex terrain is established, which provides a basis for subsequent control algorithms. Secondly, a precise control system is designed for different terrain conditions, and parameters such as vehicle attitude, speed and acceleration are adjusted through the Proportional–Integral–Derivative (PID) control algorithm to achieve the smooth operation of the self-balancing vehicle in complex terrain. In addition, a vehicle-mounted camera is used to capture terrain images in real time, and different terrains are accurately identified through the terrain recognition algorithm based on deep learning, thereby determining the friction coefficient and effectively improving the stability of the self-balancing vehicle on complex terrain. The experimental results show that the designed control system can enable the self-balancing two-wheeled vehicle to achieve stable balance control in different terrains, and has good applicability and stability.

This paper focuses on the challenge of autonomous landing of unmanned aerial vehicles (UAVs) on unknown moving unmanned ground vehicles (UGVs) in GPS-denied environments. We present a newly integrated UWB-Vision framework for autonomous approach and landing of UAVs in GPS-Denied environments. Our framework combines ultra-wideband (UWB) relative localization and Apriltag localization techniques. UWB technology is utilized to measure the relative distance between the UAV and UGV, while the inertial measurement unit (IMU) estimates the target's displacement for precise and real-time position estimation. In addition, Apriltag positioning is integrated into the framework to enhance landing accuracy. Experimental results validate the effectiveness and correctness of our proposed approach. This research contributes to the advancement of autonomous UAV landing in GPS-denied environments.