Road Recognition and Stability Control for Unmanned Ground Vehicles on Complex Terrain

Abstract

The study of unmanned ground vehicles (UGVs) operating under unstructured roads is of great significance to intelligent transportation, agricultural development and military technology. In order to ensure reliable and stable operation of UGVs on unstructured terrain, it is necessary to identify the current road terrain and perform vehicle stability adjustment. Road terrain identification is a prerequisite for stability control. Most of the existing road terrain identification methods use a single vehicle sensor, which has the problem that complex algorithms need to be applied for data processing, which reduces the real-time performance. Moreover, the single sensor is weak in anti-interference and limited in recognizing the road. To address these problems, a method is proposed to collect vehicle motion data using on-board gyroscope sensors and velocity sensors. Back propagation (BP) neural network is used to identify the category of the road. For the problem that the conventional proportional-integral differential (PID) algorithm cannot be adapted to different road stability control, a multi-loop adaptive proportional-integral differential (PID) control system with the velocity loop as the outer loop and the torque (current) loop as the inner loop is proposed. In order to verify the feasibility and effectiveness of the method, experiments are conducted on a UGV using robot operating system (ROS), and the results verify the feasibility and superiority of the road identification and stability control method proposed in this paper. It provides a good theoretical basis and valuable technical guidance for the UGV operation and control on unstructured roads.

Keywords

• Adaptive algorithms
• BP neural networks
• complex terrain
• multi-loop PID control systems
• road identification