发表机构
Center for Autonomous Robotic Systems, Khalifa University; Institute of Industrial and Control Engineering, Universitat Politècnica de Catalunya（哈利法大学自主机器人系统中心; 加泰罗尼亚理工大学工业与控制工程研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出RIT*规划框架，以黎曼原语替代批量知情搜索的欧几里得原语，结合CARM优化度量，在高维各向异性运动规划中显著降低成本，性能优于BIT*等基准方法。
AI 中文摘要
我们提出了黎曼知情树（RIT*），这是一种规划框架，它将批量知情搜索中的欧几里得原语替换为对应的黎曼原语。RIT*构建了更紧致、成本一致的知情集，在各向异性距离度量下执行近邻搜索，并通过级联方案高效评估边成本。我们进一步引入了碰撞自适应度量优化（CARM），该方法从碰撞反馈中在线学习障碍物邻近成本场，减少了实际场景中对先验度量设计的依赖。在从2维到14维的各类环境中开展的实验表明，RIT*在低维与空间恒定度量场景中具备竞争力，且在高维配置空间的空间变化度量场景中能生成显著更低成本的解。性能增益随各向异性程度和维度提升而扩大，在3维各向异性基准测试中，其初始成本中位数较BIT*提升最高达13.0%；在6自由度操作场景中，最终成本中位数较BIT*提升最高达9.0%；在14自由度双臂规划问题中，其成本提升幅度为24.8%至63.5%，而欧几里得知情基准在此类场景中表现退化。相关视频与代码可在此处获取：this https URL
英文摘要
We present Riemannian Informed Trees (RIT*), a planning framework that replaces Euclidean primitives in batch-informed search with their Riemannian counterparts. RIT* constructs a tighter, cost-consistent informed set, performs a nearest-neighbour search under an anisotropic distance metric, and evaluates edge costs efficiently via a cascading scheme. We further introduce a Collision-Adaptive Metric Refinement (CARM), which learns an obstacle-proximity cost field online from collision feedback, reducing the reliance on prior metric design in practical settings. Experiments across environments from 2-D to 14-D show that RIT* is competitive in low-dimensional and spatially constant-metric settings and produces substantially lower-cost solutions when the metric varies spatially in high-dimensional configuration spaces. Performance gains scale with anisotropy and dimension, reaching up to 13.0% improvement in median initial cost over BIT* in the 3-D anisotropic benchmark, up to 9.0% in median final cost over BIT* in 6-DOF manipulation, and 24.8-63.5% in a 14-DOF bimanual planning problem, where Euclidean-informed baselines degrade. Videos and code can be found here: https://muhayyuddin.github.io/ritstar/
CommentsAccepted in Robotics and Automation Letters
