用遗传编程优化路径规划，提升复杂环境下的搜索效率与精度
Genetic Informed Trees (GIT*): Path Planning via Reinforced Genetic Programming Heuristics
- 结合环境数据与强化遗传编程生成启发式函数
- 在R^4至R^16空间中优于现有单查询采样规划器
- 适合高维空间路径规划与真实机器人任务应用
最优路径规划需在起始点与目标点间找到满足目标的可行状态序列。该过程依赖启发式函数引导搜索方向。尽管强大启发式可提升搜索效率与解质量，但现有方法常忽略环境数据，并因信息关系复杂而简化函数结构。本文提出遗传知情树（GIT*），通过融合障碍物排斥力、顶点动态重要性等更广泛的环境数据，改进启发式函数以提供更好引导。同时引入强化遗传编程（RGP），将遗传编程与奖励反馈结合，对生成启发式的基因型进行变异，充分利用多类型数据，在限定时间内提升计算效率与解质量。对比实验表明，GIT*在R^4至R^16问题上超越现有单查询采样规划器，并在真实移动操作任务中验证。实验视频见：https://youtu.be/URjXbc_BiYg
原文摘要 · Abstract (English)
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
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