用神经网络实时计算拖车与障碍物距离，实现无地图安全导航。
Neural Distance-Guided Path Integral Control for Tractor-Trailer Navigation
- 用神经编码器直接从激光雷达数据估算全车身距离
- 在复杂农田环境中生成安全且动态可行的轨迹
- 适合农业机器人等需避障的柔性车辆系统
自主安全导航拖车系统需要精确、实时的碰撞避免和动态可行控制，尤其在杂乱复杂的农业环境中。由于其铰接式、可变形的几何结构和非线性动力学，传统方法往往简化车辆形状或依赖已知地图的预计算距离场，难以适用于动态、部分未知环境。为此，我们提出一种几何神经编码器，能快速准确地估算拖车整车与原始激光雷达感知之间的距离，实现无需地图的实时几何推理。这些学习到的距离被集成到模型预测路径积分（MPPI）控制器中，使系统能将真实铰接几何直接纳入代价评估，从而在复杂农业场景中实现更灵敏的导航。仿真结果表明，所提框架可在杂乱复杂环境中生成动态可行且安全的轨迹。
原文摘要 · Abstract (English)
Autonomous and safe navigation of tractor-trailer systems requires accurate, real-time collision avoidance and dynamically feasible control, particularly in cluttered and complex agricultural environments. This is challenging due to their articulated, deformable geometries and nonlinear dynamics. Traditional methods oversimplify vehicle geometry or rely on precomputed distance fields that assume a known map, limiting their applicability in dynamic, partially unknown environments. To address these limitations, we propose a geometric neural encoder that provides fast and accurate distance estimates between the full tractor-trailer body and raw LiDAR perception, enabling real-time, map-free geometric reasoning. These learned distances are integrated into a Model Predictive Path Integral (MPPI) controller, allowing the system to incorporate true articulated geometry directly into its cost evaluation and enabling more responsive navigation in challenging agricultural settings. Simulation results demonstrate that the proposed framework generates dynamically feasible and safe trajectories for navigating tractor-trailer systems in cluttered and complex environments.
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