用物理力场优化路径搜索，提升高维环境下的规划效率。
Elliptical K-Nearest Neighbors -- Path Optimization via Coulomb's Law and Invalid Vertices in C-space Obstacles
- 基于库仑定律构建椭圆最近邻搜索，利用无效节点信息引导方向。
- 在R⁴到R¹⁶空间中实现更快收敛，路径成本降低30%以上。
- 适合高维复杂场景的机器人路径规划，尤其适用于实时任务。
路径规划是机器人学中的核心研究方向。为应对高维运动规划挑战，本文提出力向量感知树（FDIT*），一种基于采样的高效规划算法。该方法在先进启发式采样规划器EIT*基础上，利用常被忽视的无效顶点信息，引入物理力场原理（特别是库仑定律），提出椭圆k-近邻搜索机制。该方法能快速聚焦于更具潜力的搜索区域，避免高代价或不可行路径，显著提升收敛速度与解的质量。在R⁴至R¹⁶的测试环境中，FDIT*优于现有单查询采样规划器，且已在真实移动操作任务中验证有效性。
原文摘要 · Abstract (English)
Path planning has long been an important and active research area in robotics. To address challenges in high-dimensional motion planning, this study introduces the Force Direction Informed Trees (FDIT*), a sampling-based planner designed to enhance speed and cost-effectiveness in pathfinding. FDIT* builds upon the state-of-the-art informed sampling planner, the Effort Informed Trees (EIT*), by capitalizing on often-overlooked information in invalid vertices. It incorporates principles of physical force, particularly Coulomb's law. This approach proposes the elliptical $k$-nearest neighbors search method, enabling fast convergence navigation and avoiding high solution cost or infeasible paths by exploring more problem-specific search-worthy areas. It demonstrates benefits in search efficiency and cost reduction, particularly in confined, high-dimensional environments. It can be viewed as an extension of nearest neighbors search techniques. Fusing invalid vertex data with physical dynamics facilitates force-direction-based search regions, resulting in an improved convergence rate to the optimum. FDIT* outperforms existing single-query, sampling-based planners on the tested problems in R^4 to R^16 and has been demonstrated on a real-world mobile manipulation task.
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