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面向未知环境探索的分层架构路径规划算法

Hierarchical Path Planning Algorithm for Exploring Unknown Environments

  • 摘要: 为实现无人平台在未知环境下的高效路径探索, 该文提出了一种以“感知-规划-控制”分层架构为基础的路径规划算法。在感知层通过 Cartographer 建图算法实时构建未知环境的二维栅格地图。在规划层, 通过 Canny 边缘检测、基于密度的聚类算法、效能函数评估选择最佳探索目标点, 并在规划的效能函数中引入探索方向延续性概念, 克服了传统路径规划反复探索已知环境的难题。在控制层, 通过概率路线图算法规划从当前位姿到目标点的最短路径, 并通过纯跟踪算法和向量直方图算法实现了路径无碰撞跟踪。3 种典型环境下的仿真实验表明, 所提出的算法在不同环境下均具有较高的探索效率和完成度。

     

    Abstract: To realize efficient path exploration of unmanned platforms in unknown environments, a path planning algorithm based on a hierarchical architecture of “perception planning control” is studied in this work. Real-time construction of two-dimensional grid maps of unknown environments using the Cartographer mapping algorithm at the perception layer. At the planning level, the optimal exploration target point is selected by Canny edge detection, density-based clustering algorithm, and performance function evaluation. Specifically, the concept of continuity of exploration direction is introduced into the efficiency function of planning, overcoming the drawbacks of traditional path planning that repeatedly explores known environments. At the control layer, the shortest path from the current pose to the target point is planned using probability roadmap algorithm, and collision-free tracking of the path is achieved through pure tracking algorithm and vector histogram algorithm. The effectiveness of the algorithm was verified through simulation in three typical environments, and the results showed that the proposed algorithm can achieve higher exploration efficiency and completeness in different environments.

     

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