融合地形与车辆动力学的实时路径规划，支持复杂路面自主导航。
Hybrid Terrain-Aware Path Planning: Integrating VD-RRT* Exploration and VD-D* Lite Repair
- 用土质模型和坡度惩罚构建可微分的地形代价场。
- 结合VD-RRT*探索与VD-D* Lite修复，实现毫秒级重规划。
- 适用于越野车、无人车等复杂地形自主系统。
在非结构化环境中运行的自主地面车辆需实时规划符合曲率约束的路径，并考虑空间变化的土壤强度与坡度风险。本文提出一种连续状态-代价度量，结合Bekker压陷模型与高程衍生的坡度和姿态惩罚。该地形代价场具有解析性、有界性和单调性，确保离散化良好且对传感器噪声稳定更新。代价场在网格上评估，使用精确转向原语：差速驱动的Dubins与Reeds-Shepp运动，以及阿克曼转向的时间参数化自行车弧。全局探索采用车辆动力学RRT*，局部修复由车辆动力学D* Lite管理，实现无需启发式平滑的毫秒级重规划。通过将地形-车辆模型与规划器解耦，该框架为可变形地形中的确定性、采样式或学习驱动规划提供通用基础。硬件试验在越野平台上验证了其在松软土壤与坡度过渡区的实时导航能力，支持复杂环境下的可靠自主运行。
原文摘要 · Abstract (English)
Autonomous ground vehicles operating off-road must plan curvature-feasible paths while accounting for spatially varying soil strength and slope hazards in real time. We present a continuous state--cost metric that combines a Bekker pressure--sinkage model with elevation-derived slope and attitude penalties. The resulting terrain cost field is analytic, bounded, and monotonic in soil modulus and slope, ensuring well-posed discretization and stable updates under sensor noise. This metric is evaluated on a lattice with exact steering primitives: Dubins and Reeds--Shepp motions for differential drive and time-parameterized bicycle arcs for Ackermann steering. Global exploration is performed using Vehicle-Dynamics RRT\(^{*}\), while local repair is managed by Vehicle-Dynamics D\(^{*}\) Lite, enabling millisecond-scale replanning without heuristic smoothing. By separating the terrain--vehicle model from the planner, the framework provides a reusable basis for deterministic, sampling-based, or learning-driven planning in deformable terrain. Hardware trials on an off-road platform demonstrate real-time navigation across soft soil and slope transitions, supporting reliable autonomy in unstructured environments.
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