摘要：

针对无人车在潮滩这类泥泞崎岖地形中无法高效抵达目标点的问题, 提出一种基于A*算法的改进算法TA*(Tidal-A*), 从而为无人车规划最优路径。结合潮滩环境特点, 使用路径土壤含水量(SMC)、路径高度起伏、路径长度共同衡量生成路径的质量。针对环境信息难以直接获取的问题, 使用无人机搭载高光谱传感器、激光雷达扫描目标区域, 提出结合光谱预处理与Pearson相关系数的组合降维方法, 以训练SMC反演模型。针对传统A*算法只能依据路径长度搜索路径的问题, 在为3条约束分别设计代价函数的基础上, 提出综合多约束的代价函数。针对传统A*算法无法依据需求改变路径的问题, 设计系数组合控制代价函数中各约束的占比, 同时解决不同约束间数量级不统一的问题。针对传统A*算法可能会忽略更优解的问题, 改进启发函数的计算范围, 使算法能以路径的冗余换取其他约束的优化。仿真结果表明, 使用该算法训练模型, 决定系数R²为0.784, 相对分析误差(RPD)为2.151, 相比直接反演方法分别提升了38%、33.8%。相比传统算法, TA*算法生成路径的长度、SMC值、高度起伏最高分别减少3.4%、5.1%、18.7%。

关键词: A*算法, 路径规划, 遥感, 土壤含水量反演, 高光谱图像, 数字高程模型

Abstract:

To address the issue of unmanned vehicles being unable to efficiently reach target points in muddy and rugged terrains such as tidal flats, an improved algorithm based on the A* algorithm, Tidal-A* (TA*), is proposed to plan optimal paths for unmanned vehicles. Considering the characteristics of tidal flat environments, the quality of the generated paths is jointly evaluated using Soil Moisture Content (SMC) along the path, path height fluctuation, and path length. To address the difficulty of directly obtaining environmental information, a drone equipped with a hyperspectral sensor and LiDAR is used to scan the target area. A dimensionality reduction method combining spectral preprocessing and the Pearson correlation coefficient is proposed to train the SMC inversion model. In response to the limitations of the traditional A* algorithm, which only searches for paths based on path length, a cost function that integrates multiple constraints is proposed based on the design of the cost functions for three individual constraints. To address the issue that the traditional A* algorithm cannot change the path according to requirements, a coefficient combination is designed to control the proportion of each constraint in the cost function while solving the problem of inconsistent orders of magnitude between different constraints. To address the potential issue that the traditional A* algorithm may overlook better solutions, the calculation range of the heuristic function is improved, allowing the algorithm to trade off-path redundancy for the optimization of other constraints. The simulation results show that when using this algorithm to train the model, the determination coefficient R² is 0.784, and the Ratio of Standard Deviation (RPD) is 2.151, which are 38% and 33.8% higher, respectively, than those of the direct inversion methods. Compared to those generated by traditional algorithms, the length, SMC value, and height fluctuation of the paths generated by the TA* algorithm are reduced by 3.4%, 5.1%, and 18.7%, respectively.

Key words: A* algorithm, path planning, remote sensing, Soil Moisture Content (SMC) inversion, hyperspectral image, digital elevation model