A Multilevel Optimization Framework for Off-Road Path Planning Incorporating Multiscale Geomechanical Constraints and Integrated Soil Modeling
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摘要: 越野路径规划在应急救援等特殊场景任务中具有重要战略价值，但现有模型在复杂地形中存在通行性模型精度不足与求解效率低下的问题.为此，基于地质力学约束和融合土壤建模的思想，构建了通行性评估支撑的越野路径规划模型.首先，引入土壤湿度-硬度经验模型（SMSP Ⅱ）预测土壤额定圆锥系数（rating cone index，RCI），结合车辆额定圆锥指数（vehicle cone index，VCI），构建多要素约束且融合土壤通行性指标的通行度栅格图；在此基础上，提出一种融合分治策略与启发式搜索的改进NSGA-Ⅱ算法，并与Dijkstra算法集成，构建了多级混合路径优化框架.研究结果表明，论文提出的模型在路径可行性保持不变的前提下，计算效率提升约45%，路径通行度提升2%，路径长度缩短约2.1%.研究验证了论文提出的技术方法体系在复杂地质环境下越野路径规划的优越性能.Abstract: Off-road path planning plays a strategically important role in special mission scenarios such as emergency rescue. However, existing algorithmic models often suffer from low accuracy in passability modeling and poor computational efficiency in complex terrains. To improve planning accuracy and efficiency, this study introduces the Soil Moisture-Strength Prediction model (SMSP Ⅱ) to estimate the rated cone index (RCI) of soil, and combines it with the vehicle cone index (VCI) to construct a traversability grid map that integrates multiple constraints and soil passability indicators. On this basis, an improved NSGA-Ⅱ algorithm is proposed, incorporating a divide-and-conquer strategy and heuristic search, and is further integrated with the Dijkstra algorithm to build a multi-level hybrid path optimization framework. Experimental results demonstrate that the proposed method improves computational efficiency by approximately 45%, enhances path passability by 2%, and reduces path length by about 2.1%, while maintaining path feasibility. The findings verify the superior performance of the proposed technical framework for off-road path planning in complex geological environments.
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图 1 越野机动环境构建数据
地形、地表覆盖、断裂构造数据来源于遥感数据解译，土壤数据来自SoilGrids2.0平台的空间分辨率为250 m的数据集.b图土壤数据中：CH.高塑性黏土；CL.低塑性黏土；MH.高塑性粉土；SC.黏土质砂；SC-SM.含粉土的黏土质砂；SM.粉土质砂
Fig. 1. Off-road mobility environment construction data
图 6 全局路径规划
a图为Dijkstra算法在90m空间分辨率通行度栅格图中全局路径规划结果；b图蓝色线代表全局规划路径，红色点代表在全局规划路径上的分段点.
Fig. 6. Large-scale global route planning
图 7 不同算法路径对比
a图为A*、Dijkstra、改进PRM 3种不同改进混合算法全局路径规划结果；b图为3种算法在采取分段策略后第一段路径规划结果，红色框区域为b图路径规划区域
Fig. 7. Path comparison of different algorithms
表 1 多项式插值与指数衰减插值模型效果对比
Table 1. Performance comparison between polynomial inter-polation and exponential decay interpolation models
模型 MSE R2 三次多项式 0.000 196 0.998 4 指数衰减 0.000 103 0.999 1 
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表 2 地质灾害胁迫缓冲区量化
Table 2. Quantitative analysis of geological hazard threat buffer zones
缓冲距离(m) 通行度系数 0~300 0.10 300~600 0.25 600~900 0.50 900~1 200 0.75 1 200以上 1.00
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表 3 土壤类型通行量化
Table 3. Quantitative analysis of soil type traversability
USCS分类 通行评级 通行度系数 CH 差(Poor) 0.50 CL 中等(Fair) 0.65 CL⁃ML 中等(Fair) 0.60 MH 差(poor) 0.55 ML 中等(Fair) 0.70 SC 良好(Good) 0.85 SC⁃SM 中等(Fair) 0.80 SM 中等(Fair) 0.75 SP 良好(Good) 0.95 SP⁃SM 中等(Fair) 0.90 注：本研究首次将USCS工程分类与RCI阈值联合用于越野通行评级; CH.高塑性黏土; CL.低塑性黏土; CL⁃ML.低塑性粉质黏土; MH.高塑性粉土; ML.低塑性粉土; SC.黏土质砂; SC⁃SM.含粉土的黏土质砂; SM.粉土质砂; SP.级配不良砂; SP⁃SM.含粉土的级配不良砂.
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表 4 栅格合并算法
Table 4. Raster merging process workflow
算法1 栅格合并算法过程 输入：栅格文件 步骤1读取文件、计算output_rows、output_cols 步骤2 for i in长度out_rows按行遍历 for j in长度out_clos按列遍历 Windows获取，规则判断： 如果不可通行栅格占优，合并栅格不可通行 否则，可通行栅格平均值赋值给中心点 步骤3 Return output文件
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表 5 算法参数
Table 5. Algorithm parameters
参数名称 参数值 population 200 generations 300 Crossover_rate 0.5 Mutation_rate 0.5 resolution 30 obstacle_penalty 106
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表 6 A*与Dijkstra算法全局规划性能对比
Table 6. Performance comparison between A* and Dijkstra algorithms for global path planning
算法 路径栅格数 总距离(km) 平均通
行度算法平均时间(s) A*算法 211 22.18 0.642 4.210 7 Dijkstra算法 211 22.36 0.771 0.544 2
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表 7 全局规划与分段规划对比
Table 7. Performance comparison between global and segmented planning approaches
路径方法 搜索栅格数 平均通行度 路径距离(km) 全局规划 91 351 0.629 19.647 分段规划 50 305 0.642 19.234
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表 8 不同算法性能对比
Table 8. Algorithm performance comparison
算法 搜索栅格数 平均通行度 路径距离(m) A*+改进NSGA⁃Ⅱ 50 305 0.642 19 234.00 Dijkstra+改进NSGA⁃Ⅱ 165 967 0.663 19 516.46 PRM+改进NSGA⁃Ⅱ - 0.635 19 521.17
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