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

TL;DR

This paper introduces a real-time, model predictive control-based local trajectory planner for high-speed off-road autonomous vehicles, ensuring safety and mobility at the limits of terrain ruggedness.

Contribution

It develops a new dynamics model and energy-based constraints for off-road vehicles, enabling safe, high-speed navigation over rough terrain with real-time computation.

Findings

Fewer rollovers in challenging off-road scenarios

Successful high-speed navigation on rugged terrain

Real-time trajectory planning demonstrated through physical experiments

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.