Model- and Acceleration-based Pursuit Controller for High-Performance Autonomous Racing
OPEN ACCESS
Author / Producer
Date
2023
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
Abstract
Autonomous racing is a research field gaining large popularity, as it pushes autonomous driving algorithms to their limits and serves as a catalyst for general autonomous driving. For scaled autonomous racing platforms, the computational constraint and complexity often limit the use of Model Predictive Control (MPC). As a consequence, geometric controllers are the most frequently deployed controllers. They prove to be performant while yielding implementation and operational simplicity. Yet, they inherently lack the incorporation of model dynamics, thus limiting the race car to a velocity domain where tire slip can be neglected. This paper presents Model- and Acceleration-based Pursuit (MAP) a high-performance model-based trajectory tracking controller that preserves the simplicity of geometric approaches while leveraging tire dynamics. The proposed algorithm allows accurate tracking of a trajectory at unprecedented velocities compared to State-of-the-Art (SotA) geometric controllers. The MAP controller is experimentally validated and outperforms the reference geometric controller four-fold in terms of lateral tracking error, yielding a tracking error of 0.055 m at tested speeds up to 11 m/s on a scaled racecar. Code: https://github.com/ETH-PBL/MAP-Controller.
Permanent link
Publication status
published
Editor
Book title
2023 IEEE International Conference on Robotics and Automation (ICRA)
Journal / series
Volume
Pages / Article No.
5276 - 5283
Publisher
IEEE
Event
40th IEEE International Conference on Robotics and Automation (ICRA 2023)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Robotics (cs.RO); control; autonomous driving
Organisational unit
01225 - D-ITET Zentr. f. projektbasiertes Lernen / D-ITET Center for Project-Based Learning
Notes
Funding
Related publications and datasets
Is new version of: 10.48550/arXiv.2209.04346