传统地下移动装备自主导航主要依靠预先建立的静态地图做出全局路径规划，在遇到突然出现的障碍物时容易产生振荡的轨迹，导致行驶路线无法被执行。为解决上述问题，提出了在全局路径规划的基础上增加改进TEB（Time Elastic Band）局部路径规划，并对TEB算法增加曲率约束、急动度约束、末端平缓约束和能耗约束，以适应地下巷道环境。试验结果表明：改进TEB算法产生了适应度更高的轨迹，有效地缩短了路径长度，降低了速度的跳变；优化后的路径平滑性得到提高，与目标点的偏差减小，并在运行效率方面比传统TEB路径规划有所提高，改进前的平均路径代价值为23.09，改进后的平均路径代价值为10.19，总体代价值降低了55.87%。

With the rapid development of unmanned driving technology，the driverless vehicles on the road have been widely used，which has laid a solid foundation for the fully unmanned mine.Especially for un-derground operation equipment，the roadway environment has the characteristics of closedness，irregular driving area，and difficulty in environmental perception，which makes the mobile equipment of underground manual driving inefficient and frequent accidents.So in a chaotic，irregular and dynamic environment，a safe and efficient autonomous navigation system is essential.The traditional autonomous navigation of underground mobile equipment mainly relies on pre-established static maps to make global path planning，then directly hands over the global path to control model，which makes it impossible to update the map in time when encountering sudden obstacles，resulting in oscillating trajectories and crooked paths.In order to solve the above problems，this article proposed the improved TEB （Time Elastic Band） local path planning to quickly update the path by combining global planning and local planning on the basis of mapping and navigation.In order to adapt the underground roadway environment， add target point constraints，urgency constraints，end smoothing constraints and energy consumption constraints，the nonlinear optimization problem can be iteratively solved through the G2O graph optimization framework to obtain a suboptimal solution that meets the requirements，the programming speed is within 100 ms.By simulating the dual-lane collision-free，dual-lane oncoming traffic，dynamic crossing scene，according to the principle of underground driving，the improved TEB algorithm produces a more feasible trajectory，which effectively shortens the path length，reduces the number of turns and stops，especially the path smoothness at the corner，and the operating efficiency is higher than the traditional TEB path planning algorithm.The average path generation value before the improvement was 23.09，and the average path generation value after the improvement was 10.19，which decreased the overall generation value by 55.87%.Finally，the unmanned vehicle experimental platform is used to build random obstacles in the underground roadway scene according to the 9∶1 scale，and the feasibility of the algorithm is verified in the dynamic cross environment，which can satisfy the safe and efficient driving of underground mobile equipment in the roadway.