Optimal path planning involves finding a feasible state sequence between a start and a goal that optimizes an objective. This process relies on heuristic functions to guide the search direction. While a robust function can improve search efficiency and solution quality, current methods often overlook available environmental data and simplify the function structure due to the complexity of information relationships. This study introduces Genetic Informed Trees (GIT*), which improves upon Effort Informed Trees (EIT*) by integrating a wider array of environmental data, such as repulsive forces from obstacles and the dynamic importance of vertices, to refine heuristic functions for better guidance. Furthermore, we integrated reinforced genetic programming (RGP), which combines genetic programming with reward system feedback to mutate genotype-generative heuristic functions for GIT*. RGP leverages a multitude of data types, thereby improving computational efficiency and solution quality within a set timeframe. Comparative analyses demonstrate that GIT* surpasses existing single-query, sampling-based planners in problems ranging from R^4 to R^16 and was tested on a real-world mobile manipulation task. A video showcasing our experimental results is available at https://youtu.be/URjXbc_BiYg

翻译：最优路径规划旨在寻找起点与目标点之间满足约束条件的状态序列，并使其优化某一目标函数。该过程依赖于启发式函数来引导搜索方向。虽然鲁棒的启发式函数能够提升搜索效率和解的质量，但现有方法常因信息关联的复杂性而忽略可用的环境数据，并简化函数结构。本研究提出遗传信息树（GIT*），该方法在努力信息树（EIT*）的基础上进行改进，通过整合更广泛的环境数据（如障碍物的排斥力与顶点的动态重要性）来优化启发式函数，从而提供更有效的引导。此外，我们引入了强化遗传编程（RGP），该方法将遗传编程与奖励系统反馈相结合，为GIT*生成可突变的基因型启发式函数。RGP能够利用多种数据类型，从而在设定时间范围内提升计算效率和解的质量。对比分析表明，在R^4至R^16维度的问题中，GIT*均优于现有的单次查询采样式规划器，并已在真实世界移动操作任务中通过测试。展示实验结果的视频可在 https://youtu.be/URjXbc_BiYg 观看。