A Nonlinear Model Predictive Control Strategy for Autonomous Racing of Scale Vehicles

TL;DR

This paper presents a nonlinear model predictive control strategy for small-scale autonomous racing cars, optimizing lap time and safety by considering vehicle dynamics, actuation limits, and obstacle avoidance, validated through Gazebo simulations.

Contribution

The paper introduces a novel NMPC approach tailored for small-scale racing cars that integrates obstacle avoidance and dynamic constraints, with open-source code for reproducibility.

Findings

Successful lap time minimization in simulations

Safe obstacle avoidance demonstrated

Open-source code enables replication

Abstract

A Nonlinear Model Predictive Control (NMPC) strategy aimed at controlling a small-scale car model for autonomous racing competitions is presented in this paper. The proposed control strategy is concerned with minimizing the lap time while keeping the vehicle within track boundaries. The optimization problem considers both the vehicle's actuation limits and the lateral and longitudinal forces acting on the car modeled through the Pacejka's magic formula and a simple drivetrain model. Furthermore, the approach allows to safely race on a track populated by static obstacles generating collision-free trajectories and tracking them while enhancing the lap timing performance. Gazebo simulations using the F1/10 simulator showcase the feasibility and validity of the proposed control strategy. The code is released as open-source making it possible to replicate the obtained results.