In this paper, the autonomous navigation of six-crawler machine is studied, and a visual tracking control method based on machine vision for fuzzy proportional–integral–derivative control of six-crawler machine is proposed. The steering principle of the six-crawler machine and the matching relationship between the steering angle and the speed of each crawler are introduced, and the control system is described in detail. Besides, the mathematical model for the unsteady steering is introduced to analyze the influence of deflection angle on the steering trajectory of the six-crawler machine. The image processing algorithm is programmed by LabVIEW software. After the image is fitted by graying, binary, filtering, edge detection, and least square method, the navigation line-fitting curve is obtained. The fuzzy proportional–integral–derivative control algorithm is programmed in the control system to control the six-crawler machine to drive along the navigation line. In order to obtain reasonable control parameters, a virtual prototype model of a six-crawler machine is established. In the CoLink module, the control algorithm of a six-crawler machine is established, and the co-simulation is carried out. By analyzing the simulation results, the control parameters of the fuzzy proportional–integral–derivative controller of the six-crawler machine are established. In order to verify the control effect of the visual tracking control system of the six-crawler machine, a physical prototype of the six-crawler machine is constructed and tested. The results show that the visual tracking control system of the six-crawler machine can complete the preset functions.

Road roughness is a key factor affecting vehicle ride comfort and occupant comfort. It is the basis for road grade evaluation and also determines the dynamic load of the vehicle. Measuring road unevenness is often used to evaluate the quality of road construction. However, with the development of smart cars, in order to adjust the speed and suspension parameters in real time according to the road roughness, to improve the ride comfort, the method for measuring the road unevenness in real time becomes New research hotspots. This paper firstly introduces the indicators and standards for evaluating road roughness, and then summarizes the research progress and direction of road roughness from both direct measurement and indirect identification. Among them, the methods of non-direct identification of road roughness are highlighted, which are classified into laser sensor based recognition, vibration model based recognition and neural network based recognition.