High-Speed, All-Terrain Autonomy: Ensuring Safety at the Limits of Mobility

Abstract

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.

Figures (16)

• Figure 1: Off-road vehicles experience significant vertical dynamics (evidenced by the extreme bend of the fiberglass flagpole) and even tire liftoff (shown in close-up view) when traveling over the rugged terrain considered in this work, even at moderate speed (5). Since these dynamics contribute to rollover, navigation, especially at high speeds, is dangerous. This work demonstrates a method to mitigate the risk of rollover through advanced control design.
• Figure 2: The extended single-track vehicle dynamics model, a state-of-the-art baseline
• Figure 3: The single rigid body vehicle dynamics model, a higher-fidelity alternative to the extended single-track model
• Figure 4: Relative height used to compute the Energy Stability Margin (ESM)
• Figure 5: Normalized soft constraint formulation: The additional cost rate added by incremental violation of a soft constraint is shown and compared to the equivalent additional cost of violating a hard constraint.