In order to solve the problem of low safety and efficiency of underground mine vehicles, a path planning method for underground mine vehicles based on an improved A star (A*) and fuzzy control Dynamic Window Approach (DWA) is proposed. Firstly, the environmental information is analyzed, and the proportion of obstacles is introduced into the A* algorithm to optimize the heuristic function to improve its search efficiency in different environments. Secondly, for the global path, the key node selection strategy is proposed, the node selection method is optimized, and the redundant nodes are deleted. The path is optimized by using a 3 times Clamped-B spline to improve the smoothness of the path. Then, based on the principle of fuzzy control, fuzzy rules are established, and a DWA fuzzy controller is designed to improve the environmental adaptability of the DWA algorithm. Finally, on the basis of the global path, the key node is used as the local target point of the DWA, and the fusion of the improved A* and DWA algorithm is realized. Finally, experiments are conducted to verify the effectiveness and feasibility of the proposed path-planning method. The average deviation of the path is controlled at ±0.109 m, which basically meets the path planning needs of underground mine vehicles.

In order to make up for the shortcomings of independent sensors and provide more reliable estimation, a multi-sensor fusion framework for simultaneous localization and mapping is proposed in this paper. Firstly, the light detection and ranging (LiDAR) point cloud is screened in the front-end processing to eliminate abnormal points and improve the positioning and mapping accuracy. Secondly, for the problem of false detection when the LiDAR is surrounded by repeated structures, the intensity value of the laser point cloud is used as the screening condition to screen out robust visual features with high distance confidence, for the purpose of softening. Then, the initial factor, registration factor, inertial measurement units (IMU) factor and loop factor are inserted into the factor graph. A factor graph optimization algorithm based on a Bayesian tree is used for incremental optimization estimation to realize the data fusion. The algorithm was tested in campus and real road environments. The experimental results show that the proposed algorithm can realize state estimation and map construction with high accuracy and strong robustness.

Aiming at the problem that, under certain extreme conditions, relying on tire force or tire angular velocity to represent the longitudinal velocity of the unmanned vehicle will fail, this paper proposes a longitudinal velocity estimation method that fuses LiDAR and inertial measurement unit (IMU). First, in order to improve the accuracy of LiDAR odometry, IMU information is introduced in the process of eliminating point cloud motion distortion. Then, the statistical characteristic of the system noise is tracked by an adaptive noise estimator, which reduces the model error and suppresses the filtering divergence, thereby improving the robustness and filtering accuracy of the algorithm. Next, in order to further improve the estimation accuracy of longitudinal velocity, time-series analysis is used to predict longitudinal acceleration, which improves the accuracy of the prediction step in the unscented Kalman filter (UKF). Finally, the feasibility of the estimation method is verified by simulation experiments and real-vehicle experiments. In the simulation experiments, medium- and high-velocity conditions are tested. In high-velocity conditions (0–30 m/s), the average error is 1.573 m/s; in the experiment, the average error is 0.113 m/s.