高速全地形自主性：在移动极限下的安全性保证
High-Speed, All-Terrain Autonomy: Ensuring Safety at the Limits of Mobility
AI总结：
本文提出一种新型局部轨迹规划器，通过能量约束实现高速越野车辆在复杂地形中的安全行驶，通过仿真和实验证明其在极端场景下的有效性。
摘要：
本文提出了一种新颖的局部轨迹规划器，能够控制自主越野车辆在崎岖地形上以高速行驶。当前自主车辆无法在高速下安全越野，因为现有方法要么无法预测和缓解由粗糙地形引起的翻车，要么不具实时可行性。为解决这一挑战，开发了一种新的模型预测控制（MPC）形式用于局部轨迹规划。推导了一个新的越野车辆在非平面粗糙地形的动力学模型用于预测。通过新的基于能量的约束实现了极端移动，包括轮胎脱离而不翻车。该形式通过并行化GPGPU计算实现了实时可行性。通过模拟试验和全尺寸物理实验研究了规划器提供安全极端轨迹的能力。结果表明，在多个挑战性场景中，相比最先进的基线方法，翻车更少，成功更多。
英文摘要：
A novel local trajectory planner, capable of controlling an autonomous off-road vehicle on rugged terrain at high-speed is presented. Autonomous vehicles are currently unable to safely operate off-road at high-speed, as current approaches either fail to predict and mitigate rollovers induced by rough terrain or are not real-time feasible. To address this challenge, a novel model predictive control (MPC) formulation is developed for local trajectory planning. A new dynamics model for off-road vehicles on rough, non-planar terrain is derived and used for prediction. Extreme mobility, including tire liftoff without rollover, is safely enabled through a new energy-based constraint. The formulation is analytically shown to mitigate rollover types ignored by many state-of-the-art methods, and real-time feasibility is achieved through parallelized GPGPU computation. The planner's ability to provide safe, extreme trajectories is studied through both simulated trials and full-scale physical experiments. The results demonstrate fewer rollovers and more successes compared to a state-of-the-art baseline across several challenging scenarios that push the vehicle to its mobility limits.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
