随着移动机器人的广泛应用，路径规划效率已成为影响其作业性能的关键因素。针对移动机器人在动态的障碍物空间内路径规划的效率问题，本文提出一种基于改进采样和局部规划的批处理通知树算法(Batch Informed Trees，BIT^*)。采用Kennard-Stone算法实现均匀采样，通过改进采样分布降低采样点的聚集性，提升全局探索能力。寻路阶段引入射线检测来控制搜索树扩展，在生成新边时主动避开障碍物，从而减少了低效扩展对路径规划的影响。在搜索树进行搜索的过程中进行路径快速优化，通过后处理策略有效减少了路径的转折点并优化全局路径。针对动态障碍物，本文设计了分层的路径规划方案。上层基于改进BIT^*算法（K-BIT^*）生成参考路径；下层则引入改进的Informed RRT^*算法对动态障碍物进行局部重规划。在MATLAB仿真环境下构建障碍物空间，采用K-BIT^*、BIT^*、D-BIT^*和Informed RRT^*算法进行全局路径规划，并在动态环境下对比K-BIT^*、D-BIT^*和DGF-APF之间的动态规划效率。结果表明，K-BIT^*算法路径长度更短，规划时间较D-BIT^*减少13.7%，路径点比D-BIT^*少9.7%；在动态环境下，K-BIT^*相较D-BIT^*，经过局部规划的路径缩短了0.368m，且标准差小于D-BIT^*。验证了K-BIT^*算法在动态障碍物空间的路径规划效率。

With the widespread application of mobile robots, path planning efficiency has become a key factor influencing their operational performance. To address the path planning efficiency challenge for mobile robots in dynamic environments, this paper proposes an improved Batch Informed Trees (BIT^*) algorithm based on enhanced sampling and local planning. The Kennard-Stone algorithm is employed to achieve uniform sampling. By refining the sampling distribution to reduce point clustering, the algorithm's global exploration capability is enhanced. During the pathfinding phase, ray detection is introduced to control the expansion of the search tree, actively avoiding obstacles when generating new edges, thereby reducing the impact of inefficient expansions on path planning. Rapid path optimization is performed during the search tree exploration. Post-processing strategies are applied to effectively minimize the number of turns and optimize the global path. For dynamic obstacles, a hierarchical path planning framework is designed. The upper layer generates reference paths based on the improved BIT^* algorithm (K-BIT^*), while the lower layer employs an enhanced Informed RRT^* algorithm for local replanning around dynamic obstacles. A simulation environment with obstacle spaces is constructed in MATLAB. Global path planning is conducted using K-BIT^*, BIT^*, D-BIT^*, and Informed RRT^* algorithms. In dynamic environments, the dynamic planning efficiency of K-BIT^* is compared with that of D-BIT^* and DGF-APF. The results demonstrate that the K-BIT^* algorithm achieves shorter path lengths compared to D-BIT^*, along with a 13.7% reduction in planning time and 9.7% fewer waypoints. In dynamic environments, the locally replanned path of K-BIT^* is 0.368 m shorter than that of D-BIT^*, with a lower standard deviation. These findings validate the efficiency of the K-BIT^* algorithm for path planning in dynamic obstacle spaces.