The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Optimization‐based autonomous racing of 1:43 scale RC cars
Open accessAbstract
Summary: This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two‐level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real‐time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high‐level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real‐time feasibility and high performance of optimization‐based approaches for autonomous racing. Copyright © 2014 John Wiley & Sons, Ltd.
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Junior: The Stanford entry in the Urban Challenge
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Planning and Decision-Making for Autonomous Vehicles
Schwarting, W., Alonso-Mora, J., & Rus, D. (2018) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105157 · Cited by 972
Learning-Based Model Predictive Control for Autonomous Racing
Kabzan, J., Hewing, L., Liniger, A., & Zeilinger, M. N. (2019) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2019.2926677 · Cited by 462
Self-citationSafety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
Zeng, J., Zhang, B., & Sreenath, K. (2021) · 2021 American Control Conference (ACC) · doi:10.23919/acc50511.2021.9483029 · Cited by 390
Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing
Betz, J., Zheng, H., Liniger, A., Rosolia, U., Karle, P., Behl, M., Krovi, V., & Mangharam, R. (2022) · IEEE Open Journal of Intelligent Transportation Systems · doi:10.1109/ojits.2022.3181510 · Cited by 322
Self-citationWabersich, K. P., Taylor, A. J., Choi, J. J., Sreenath, K., Tomlin, C. J., Ames, A. D., & Zeilinger, M. N. (2023) · IEEE Control Systems · doi:10.1109/mcs.2023.3291885 · Cited by 132
Metadata
- Authors
- Liniger, A., Domahidi, A., & Morari, M.
- Venue
- Optimal Control Applications and Methods
- Publisher
- Wiley
- Published
- 2014-07-17
- Volume
- 36
- Issue
- 5
- Pages
- 628-647
- ISSN
- 0143-2087 (ISSN-L)
- 1099-1514
- OpenAlex
- W1578293866
- Cited by
- 537
- References
- 27
- FWCI
- 16.34
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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