基于遥感数据的潮滩区域路径规划算法研究OA

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.