Local NMPC on Global Optimised Path for Autonomous Racing

TL;DR

This paper introduces a control strategy for autonomous racing cars that combines global path optimization with local NMPC to improve racing performance and safety on pre-mapped tracks.

Contribution

It proposes a novel integration of global optimal racing line computation with a local NMPC that considers multiple objectives for autonomous racing.

Findings

Effective racing line optimization considering track boundaries

Successful implementation of local NMPC with multiple objectives

Improved racing performance and safety measures

Abstract

The paper presents a strategy for the control of anautonomous racing car on a pre-mapped track. Using a dynamic model of the vehicle, the optimal racing line is computed, taking track boundaries into account. With the optimal racing line as areference, a local nonlinear model predictive controller (NMPC) is proposed, which takes into account multiple local objectives like making more progress along the race line, avoiding collision with opponent vehicles, and use of drafting to achieve more progress.