Model Predictive Control for Aggressive Driving Over Uneven Terrain

Abstract

Terrain traversability in unstructured off-road autonomy has traditionally relied on semantic classification, resource-intensive dynamics models, or purely geometry-based methods to predict vehicle-terrain interactions.While inconsequential at low speeds, uneven terrain subjects our full-scale system to safety-critical challenges at operating speeds of 7-10 m/s.This study focuses particularly on uneven terrain such as hills, banks, and ditches.These common high-risk geometries are capable of disabling the vehicle and causing severe passenger injuries if poorly traversed.We introduce a physics-based framework for identifying traversability constraints on terrain dynamics.Using this framework, we derive two fundamental constraints, each with a focus on mitigating rollover and ditchcrossing failures while being fully parallelizable in the samplebased Model Predictive Control (MPC) framework.In addition, we present the design of our planning and control system, which implements our parallelized constraints in MPC and utilizes a low-level controller to meet the demands of our aggressive driving without prior information about the environment and its dynamics.Through real-world experimentation and traversal of hills and ditches, we demonstrate that our approach captures fundamental elements of safe and aggressive autonomy over uneven terrain.Our approach improves upon geometry-based methods by completing comprehensive off-road courses up to 22% faster while maintaining safe operation.

Topics & Concepts