复杂环境下无人地面车辆的安全关键框架：一种数据驱动的差异感知方法
A Safety-Critical Framework for UGVs in Complex Environments: A Data-Driven Discrepancy-Aware Approach
AI总结：
针对UGV在未知障碍与建模不确定性下的安全导航问题，提出数据驱动的差异感知多层规划控制框架，通过共形预测构建鲁棒控制器与碰撞缓冲，经多场景实验验证有效且可拓展为驾驶辅助系统。
摘要：
本研究提出了一种新颖的数据驱动多层规划与控制框架，用于一类无人地面车辆（UGV）在存在未知静态障碍物和加性建模不确定性的情况下实现安全导航。该框架的基础是一款新颖的鲁棒模型预测规划器，旨在根据占据栅格地图生成最优无碰撞轨迹；以及配套的辅助控制器，经增强后可对从学习数据中提取的模型不确定性提供鲁棒性。\n 为解决建模差异问题，我们利用闭环跟踪误差作为训练数据，识别真实模型与标称降阶模型之间的匹配（输入差异）和不匹配模型残差。借助共形预测（conformal prediction），我们提取未知模型残差的概率上界，用于构建鲁棒化辅助控制器。此外，我们还确定了增强策略下的最大跟踪差异（也称为鲁棒控制不变管），并将其制定为碰撞缓冲区。利用基于激光雷达（LiDAR）的占据地图表征环境，我们构建了融合这些碰撞缓冲区的差异感知代价地图。随后将该地图集成到基于采样的模型预测路径规划器中，生成最优且安全的轨迹，在存在模型失配的情况下，增强型辅助控制器可对其进行鲁棒跟踪。\n 我们在四种不同的车辆-地形配置下，通过杂乱环境中的自主高速轨迹跟踪实验验证了该框架的有效性。我们还将其重新构建为驾驶员辅助程序，可根据用户操纵杆指令提供避碰修正，展示了该框架的多功能性。
英文摘要：
This work presents a novel data-driven multi-layered planning and control framework for the safe navigation of a class of unmanned ground vehicles (UGVs) in the presence of unknown stationary obstacles and additive modeling uncertainties. The foundation of this framework is a novel robust model predictive planner, designed to generate optimal collision-free trajectories given an occupancy grid map, and a paired ancillary controller, augmented to provide robustness against model uncertainties extracted from learning data. To tackle modeling discrepancies, we identify both matched (input discrepancies) and unmatched model residuals between the true and the nominal reduced-order models using closed-loop tracking errors as training data. Utilizing conformal prediction, we extract probabilistic upper bounds for the unknown model residuals, which serve to construct a robustifying ancillary controller. Further, we also determine maximum tracking discrepancies, also known as the robust control invariance tube, under the augmented policy, formulating them as collision buffers. Employing a LiDAR-based occupancy map to characterize the environment, we construct a discrepancy-aware cost map that incorporates these collision buffers. This map is then integrated into a sampling-based model predictive path planner that generates optimal and safe trajectories that can be robustly tracked by the augmented ancillary controller in the presence of model mismatches. The effectiveness of the framework is experimentally validated for autonomous high-speed trajectory tracking in a cluttered environment with four different vehicle-terrain configurations. We also showcase the framework's versatility by reformulating it as a driver-assist program, providing collision avoidance corrections based on user joystick commands.
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