高精度实时规划让无人越野车高速行驶不翻车
High-Speed, All-Terrain Autonomy: Ensuring Safety at the Limits of Mobility
- 基于新型动力学模型与能量约束的MPC规划器
- 实测在极限地形下翻车率更低，成功率更高
- 适合需要高速越野的自动驾驶系统研发者
提出一种新型局部轨迹规划方法，可实现无人越野车在复杂非平面地形上的高速安全行驶。现有方法或无法预测和防止崎岖地形引发的翻滚，或不具备实时可行性。本文构建了适用于粗糙非平面地形的车辆动力学模型，并设计基于能量的约束以安全实现轮胎离地等极端机动。理论分析表明该方法能缓解多种被现有先进方法忽略的翻滚类型，通过并行化GPGPU计算实现实时性。在模拟与全尺寸物理实验中验证，相比当前最优基线，在多个推至车辆性能极限的挑战场景中均表现出更少翻车和更高成功率。
原文摘要 · Abstract (English)
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.
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