This article presents a review of the platform configuration and dynamic of obstacle-surmounting unmanned ground vehicles (UGVs). For now, unmanned systems have emerged as a result of the rapid advancement of artificial intelligence and modern manufacturing techniques both domestically and internationally. The research on unmanned systems has been improved a lot. The UGV platform can execute transportation, recurring, and military tasks independently. For the high-level self-control, adaption, and maneuverability abilities, the UGV platform has been applied in the military, industry, and other special fields widely. The UGV platform usually performs tasks in an unstructured environment, so the all-terrain performance becomes a key factor restricting their operating efficiency and reliability. A brief literature review of the UGV platform is carried out in this article.

An effective path planner is critical for autonomous traversal of unmanned ground vehicles (UGVs) in harsh environments. This paper describes a novel path planning method considering Bézier curves and a two-layer planning framework. In the two-layer framework, a road centerline (RCL) estimator located on the upper layer works as a global planner to obtain the local target for the bottom local planner. The RCL is estimated from a series of candidate Bézier curves based on a safety criterion. In the bottom layer, an optimal trajectory planner and a speed planner make up the local planner to obtain the desired steering turning angle and linear speed. The criteria for optimal trajectory selection are designed for comfortable driving. Road safety is considered in the speed planner for robust driving. Three sets of simulations are used to evaluate and quantify the relative performance of variations of our path planning algorithm. The proposed path planning method is implemented on a modified Polaris RZR 800 UGV, too. Two experiments based on this UGV are set up in the country road environment to demonstrate the viability of the proposed method.

Autonomous Unmanned Ground Vehicles (UGVs) require a reliable navigation system that works in all environments. However, indoor navigation remains a challenge because the existing satellite-based navigation systems such as the Global Positioning System (GPS) are mostly unavailable indoors. In this paper, a tightly-coupled integrated navigation system that integrates two dimensional (2D) Light Detection and Ranging (LiDAR), Inertial Navigation System (INS), and odometry is introduced. An efficient LiDAR-based line features detection/tracking algorithm is proposed to estimate the relative changes in orientation and displacement of the vehicle. Furthermore, an error model of INS/odometry system is derived. LiDAR-estimated orientation/position changes are fused by an Extended Kalman Filter (EKF) with those predicted by INS/odometry using the developed error model. Errors estimated by EKF are used to correct the position and orientation of the vehicle and to compensate for sensor errors. The proposed system is verified through simulation and real experiment on an UGV equipped with LiDAR, MEMS-based IMU, and encoder. Both simulation and experimental results showed that sensor errors are accurately estimated and the drifts of INS are significantly reduced leading to navigation performance of sub-metre accuracy.