College of Mechanical Engineering, Xi'an University of Science and Technology
Shaanxi College of Communications Technology

针对矿用车辆在狭窄、弯曲及有未知障碍物的井下巷道中的路径规划效率低的问题，提出了一种融合简化可视图(SVG)和A*算法的全局路径规划算法DVGA*。在构建真实环境点云地图基础上，连接车辆在不同视点下的可视切点，动态生成SVG；将可视切点依次存入OPEN表作为节点，根据A*算法估价函数选取路径最短情况下的节点加入CLOSED表，得到最优路径点并存储路径，同时删除OPEN表中的其余节点，循环此过程，直到OPEN表中出现终点；最后利用路径平滑算法进一步减少路径节点数量，从而提高路径规划效率。实验结果表明，与完整可视图+A*算法、SVG+A*算法及SVGCA*算法对比，DVGA*算法对复杂长距离路径的规划时间最短，平均路径长度分别缩短了10.79 % ，6.26% 和2.86%，具有更强的适应性和更高的规划成功率。井下试验结果表明：在巷道宽度变换区域和躲避静态障碍物时，相比SVGCA*算法，DVGA*算法规划的路径更加平滑；躲避动态障碍物时，DVGA*算法能够及时进行路径纠正，保证了路径规划的时效性和稳定性；在复杂多变的巷道环境中，DVGA*算法的规划时间和路径长度相比SVGCA*算法分别减少了11.51%和1.54%，具有更高的环境适应性和稳定性。

To address the low path planning efficiency of mining vehicles in narrow, winding underground tunnels with unknown obstacles, a global path planning algorithm, DVGA*, was proposed, integrating simplified visibility graphs (SVG) and the A* algorithm. Based on the construction of a point cloud map of the real environment, the algorithm connected the vehicle's visual tangent points from different viewpoints to dynamically generate the SVG. The visual tangent points were sequentially stored in the OPEN list as nodes, and nodes were selected for the CLOSED list based on the A* algorithm's evaluation function to ensure the shortest path. This process continued until the endpoint appeared in the OPEN list, resulting in the optimal path points being stored while the remaining nodes in the OPEN list were deleted. Finally, a path smoothing algorithm was utilized to further reduce the number of path nodes, thereby enhancing path planning efficiency. Experimental results indicated that compared to the Complete Visibility Graph + A* algorithm, SVG + A* algorithm, and SVGCA* algorithm, the DVGA* algorithm had the shortest planning time for complex long-distance paths, with average path lengths reduced by 10.79%, 6.26%, and 2.86%, respectively, demonstrating stronger adaptability and higher planning success rates. Results from underground tests showed that in areas with variable tunnel widths and while avoiding static obstacles, the path planned by DVGA* was smoother compared to that of the SVGCA* algorithm. When avoiding dynamic obstacles, DVGA* was able to promptly correct the path, ensuring timely and stable path planning. In complex and variable tunnel environments, the planning time and path length of DVGA* were reduced by 11.51% and 1.54%, respectively, compared to SVGCA*, indicating higher environmental adaptability and stability.

陕西省创新人才推进计划项目（2021TD-27）。

张传伟，芦思颜，秦沛霖，等. 融合简化可视图和A*算法的矿用车辆全局路径规划算法[J]. 工矿自动化，2024，50（10）：12-20.

ZHANG Chuanwei, LU Siyan, QIN Peilin, et al. Global path planning algorithm for mining vehicles integrating simplified visibility graph and A* algorithm[J]. Journal of Mine Automation，2024，50（10）：12-20.