煤矿机械

煤矿井下异构动态障碍物分布存在不确定性及巷道约束较多，无人电车的安全地图构建需要预先人工测定后导入，不断后期修正，易导致全局路径侵入高危区域、启发函数引导偏离实际通行代价及局部轨迹存在振荡，致使传统的A^*算法规划结果在安全性、平滑性与实时性方面表现欠佳。为此，提出一种煤矿复杂场景下矿用无人电车路径规划改进算法。首先，改进设计融合安全势场等级的栅格地图，计算改进后的斥力函数并归一化取整，自动获得安全等级函数，引导路径远离高风险区域；然后，采用改进A^*算法规划全局路径，引入动态权重平衡实际代价与启发式估计贡献，求出综合代价最小的最优路径；最后，结合局部感知信息，利用自适应概率路线图算法与三次样条插值优化轨迹，生成满足避障与运动平稳性约束的局部路径，实现矿用无人电车安全、平滑、高效运行。实验结果表明，所提方法能为矿用无人电车规划出更短的行驶路径，同时在多种障碍物布局下始终保持与障碍物之间的安全间距，且生成轨迹平滑、转折点少，显著提升了电车在煤矿复杂环境中的运行稳定性与整体运输效率。

The distribution of heterogeneous dynamic obstacles in underground coal mine is uncertainty and many constraints of roadway, the construction of safety maps for unmanned electric vehicles requires manual measurement in advance and continuous post correction, which can easily lead to global path intrusion into high-risk areas, the heuristic function guidance deviates from the actual travel cost, and local trajectory oscillations exist, resulting in poor performance of traditional A^* algorithm planning results in terms of safety, smoothness, and real-time performance. Therefore, an improved path planning algorithm for mining unmanned electric vehicles in complex coal mine scenarios was proposed. Firstly,improved the design of grid map that integrates safety potential field levels, calculated the improved repulsion function and normalize it to obtain the safety level function automatically, and guided the path away from high-risk areas; then, used the improved A^* algorithm to plan the global path, and dynamic weight balancing was introduced to balance the actual cost and heuristic estimation contribution, in order to find the optimal path with the minimum comprehensive cost; finally, combined with local perception information and utilizing adaptive probability roadmap algorithm and cubic spline interpolation to optimize trajectories, a local path that satisfies obstacle avoidance and motion smoothness constraints was generated, achieving safe, smooth, and efficient operation of mining unmanned electric vehicles. The experimental results show that the proposed method can plan shorter travel paths for mining unmanned electric vehicles, while maintaining a safe distance between them under various obstacle layouts. The generated trajectory is smooth and has fewer turning points,significantly improving the operational stability and overall transportation efficiency of electric vehicles in complex coal mine environments.