arXiv:2506.22170cs.ROmath.OC2025-06

基于黎曼度量的路径规划算法,提升机器人在复杂表面的路径精度与平滑性。

RM-Dijkstra: A surface optimal path planning algorithm based on Riemannian metric

  • 构建新黎曼度量将曲面路径问题转化为平面几何问题
  • 仿真验证路径准确率与平滑性优于传统算法
  • 适合复杂地形下移动机器人路径规划使用

Dijkstra算法是经典的路径规划方法,在离散图空间中根据非负边权计算从源点到目标节点或所有其他节点的最短路径。尽管其应用潜力广泛,但其在移动机器人曲面路径规划中的应用仍较少被研究。本文提出一种基于黎曼度量模型的表面最优路径规划算法——RM-Dijkstra。通过在二维投影平面构造新的黎曼度量,将曲面最优路径规划问题转化为带有新黎曼度量的二维平面几何问题。该新度量由曲面上的标准欧氏度量诱导,反映机器人所处环境信息,并保证投影映射为等距浸入。一系列仿真测试表明,RM-Dijkstra算法不仅能有效解决曲面上的最优路径规划问题,且在路径精度和光滑性方面优于传统算法,尤其在复杂场景中表现更优。

原文摘要 · Abstract (English)

The Dijkstra algorithm is a classic path planning method, which operates in a discrete graph space to determine the shortest path from a specified source point to a target node or all other nodes based on non-negative edge weights. Numerous studies have focused on the Dijkstra algorithm due to its potential application. However, its application in surface path planning for mobile robots remains largely unexplored. In this letter, a surface optimal path planning algorithm called RM-Dijkstra is proposed, which is based on Riemannian metric model. By constructing a new Riemannian metric on the 2D projection plane, the surface optimal path planning problem is therefore transformed into a geometric problem on the 2D plane with new Riemannian metric. Induced by the standard Euclidean metric on surface, the constructed new metric reflects environmental information of the robot and ensures that the projection map is an isometric immersion. By conducting a series of simulation tests, the experimental results demonstrate that the RM-Dijkstra algorithm not only effectively solves the optimal path planning problem on surfaces, but also outperforms traditional path planning algorithms in terms of path accuracy and smoothness, particularly in complex scenarios.

路径规划黎曼几何机器人

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