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The mechanical properties of sandy road are quite different from those of hard surface road. For vehicle control systems such as EMS (engine management system), TCU (transmission control unit) and ABS (antilock brake system), the strategies and parameters set for solid surface road are not optimal for driving on sandy road. It is an effective way to improve the mobility of all-terrain vehicles by identifying sandy road online and shifting the control strategies and parameters of control systems to sandy sets. In this paper, a sandy road identification algorithm for SUVs is proposed. Firstly, the vehicle signals, such as engine torque and speed, gear position, wheel and vehicle speed, are acquired from EMS, TCU and ESP (electronic stability program) through CAN (controller area network) bus respectively. Based on the information and longitudinal force equilibrium equation, the travelling resistance of vehicle is estimated.

Unmanned Ground Vehicle (UGV) has a wide range of applications in the military, agriculture, firefighting and other fields. Path planning, as a key aspect of autonomous driving technology, plays an essential role for UGV to accomplish the established driving tasks. At present, there are many global path planning algorithms in grid maps on unstructured roads, while general grid maps do not consider the specific elevation or ground type difference of each grid, and unstructured roads are generally considered as flat and open roads. On the contrary, the unmanned off-road is always a bumpy road with undulating terrain, and meanwhile, the landform is complex and the types of features are diverse. In order to ensure the safety and improve the efficiency of autonomous driving of UGV in off-road environment, this paper proposes a global off-road path planning method for UGV based on the raw image of remote sensing map. Firstly, the raw image is gridded.