In general, grid map based path planning algorithms are employed in the robotics arena. The algorithm uses a grid map to represent environmental information, standardized. Compared with feature maps and topological maps, the algorithm realizes the construction of environmental maps in a more direct way, and has the characteristics of fast, simple and efficient.The integration and prediction of terrain is an unavoidable problem and the traditional raster map prediction method is based on the research of the terrain data itself, and lacks dynamic supplement for the path planning process. When the environmental data changes, the classification algorithm can only be re-executed, and the past data is completely discarded. Since the planned path is unlikely to change, the terrain tends to be stable. To solve this problem, this paper proposes a concept of C(circular)-terrain band following path nodes and terrain construction and prediction methods. The C-Terrain method first obtains an ordered set of passing points at the initial moment, based on the complete path planning. Then an ordered sequence of influence function values is obtained, which depends on the selection of the terrain band and the adjustment of related parameters. Finally, regression methods such as machine learning are used to complete the prediction of the path and location terrain, and the unknown path and terrain are predicted. The experimental results prove the accuracy and practical value of the C-T method.

中文翻译：

---

基于C地形路径的四足机器人网格地图构建与地形预测

通常，机器人领域采用基于网格图的路径规划算法。该算法使用网格图来表示环境信息，标准化。与特征图和拓扑图相比，该算法以更直接的方式实现了环境图的构建，具有快速、简单、高效的特点。 地形的整合与预测是传统栅格地图预测方法无法回避的问题基于地形数据本身的研究，缺乏对路径规划过程的动态补充。当环境数据发生变化时，只能重新执行分类算法，彻底丢弃过去的数据。由于规划的路径不太可能改变，地形趋于稳定。为了解决这个问题，本文提出了C(circular)-地形带跟随路径节点的概念以及地形构建和预测方法。C-Terrain 方法首先基于完整的路径规划获得初始时刻的有序通过点集。然后得到一个有序的影响函数值序列，这取决于地形波段的选择和相关参数的调整。最后利用机器学习等回归方法完成路径和位置地形的预测，对未知路径和地形进行预测。实验结果证明了CT方法的准确性和实用价值。基于完整的路径规划。然后得到一个有序的影响函数值序列，这取决于地形波段的选择和相关参数的调整。最后利用机器学习等回归方法完成路径和位置地形的预测，对未知路径和地形进行预测。实验结果证明了CT方法的准确性和实用价值。基于完整的路径规划。然后得到一个有序的影响函数值序列，这取决于地形波段的选择和相关参数的调整。最后利用机器学习等回归方法完成路径和位置地形的预测，对未知路径和地形进行预测。实验结果证明了CT方法的准确性和实用价值。