Kineto-Dynamical Planning and Accurate Execution of Minimum-Time Maneuvers on Three-Dimensional Circuits

TL;DR

This paper introduces an artificial race driver that learns vehicle dynamics and plans minimum-time maneuvers on 3D circuits using a novel kineto-dynamical model and nonlinear model predictive control, achieving near-optimal lap times.

Contribution

It presents a new kineto-dynamical vehicle model integrated with economic nonlinear MPC for precise, real-time trajectory planning in 3D racing environments.

Findings

ARD achieves lap times close to offline optimal control solutions.

The kineto-dynamical model outperforms existing benchmarks.

The system demonstrates effective re-planning under execution errors.

Abstract

Online planning and execution of minimum-time maneuvers on three-dimensional (3D) circuits is an open challenge in autonomous vehicle racing. In this paper, we present an artificial race driver (ARD) to learn the vehicle dynamics, plan and execute minimum-time maneuvers on a 3D track. ARD integrates a novel kineto-dynamical (KD) vehicle model for trajectory planning with economic nonlinear model predictive control (E-NMPC). We use a high-fidelity vehicle simulator (VS) to compare the closed-loop ARD results with a minimum-lap-time optimal control problem (MLT-VS), solved offline with the same VS. Our ARD sets lap times close to the MLT-VS, and the new KD model outperforms a literature benchmark. Finally, we study the vehicle trajectories, to assess the re-planning capabilities of ARD under execution errors. A video with the main results is available as supplementary material.