The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Learning-Based Model Predictive Control for Autonomous Racing
Open accessAbstract
In this letter, we present a learning-based control approach for autonomous racing with an application to the AMZ Driverless race cargotthard. One major issue in autonomous racing is that accurate vehicle models that cover the entire performance envelope of a race car are highly nonlinear, complex, and complicated to identify, rendering them impractical for control. To address this issue, we employ a relatively simple nominal vehicle model, which is improved based on measurement data and tools from machine learning.The resulting formulation is an online learning data-driven model predictive controller, which uses Gaussian processes regression to take residual model uncertainty into account and achieve safe driving behavior. To improve the vehicle model online, we select from a constant in-flow of data points according to a criterion reflecting the information gain, and maintain a small dictionary of 300 data points. The framework is tested on the full-size AMZ Driverless race car, where it is able to improve the vehicle model and reduce lap times by$ {\mathbf{10}{\%}}$while maintaining safety of the vehicle.
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Citations
Cites
A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
Paden, B., Cap, M., Yong, S. Z., Yershov, D., & Frazzoli, E. (2016) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2016.2578706 · Cited by 2,585
Stanley: The robot that won the DARPA Grand Challenge
Thrun, S., Montemerlo, M., Dahlkamp, H., Stavens, D., Aron, A., Diebel, J., Fong, P., Gale, J., Halpenny, M., Hoffmann, G., et al. (2006) · Journal of Field Robotics · doi:10.1002/rob.20147 · Cited by 2,237
Sparse Gaussian Processes using Pseudo-inputs
Snelson, E., & Ghahramani, Z. (2005) · Cited by 1,415
Pacejka, H. B., & Bakker, E. (1992) · Vehicle System Dynamics · doi:10.1080/00423119208969994 · Cited by 1,316
Optimization‐based autonomous racing of 1:43 scale RC cars
Liniger, A., Domahidi, A., & Morari, M. (2015) · Optimal Control Applications and Methods · doi:10.1002/oca.2123 · Cited by 537
Self-citationCautious Model Predictive Control Using Gaussian Process Regression
Hewing, L., Kabzan, J., & Zeilinger, M. N. (2020) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000389524 · Cited by 469
Self-citationAggressive driving with model predictive path integral control
Williams, G., Drews, P., Goldfain, B., Rehg, J. M., & Theodorou, E. A. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487277 · Cited by 444
Zanelli, A., Domahidi, A., Jerez, J., & Morari, M. (2020) · International Journal of Control · doi:10.1080/00207179.2017.1316017 · Cited by 230
Learning-based Nonlinear Model Predictive Control to Improve Vision-based Mobile Robot Path Tracking
Ostafew, C. J., Schoellig, A. P., Barfoot, T. D., & Collier, J. (2016) · Journal of Field Robotics · doi:10.1002/rob.21587 · Cited by 196
Approximation Methods for Gaussian Process Regression
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Cited by
Learning-Based Model Predictive Control: Toward Safe Learning in Control
Hewing, L., Wabersich, K. P., Menner, M., & Zeilinger, M. N. (2020) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-090419-075625 · Cited by 835
Self-citationAutonomous 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-citationSalzmann, T., Kaufmann, E., Arrizabalaga, J., Pavone, M., Scaramuzza, D., & Ryll, M. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3246839 · Cited by 231
Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing
Brunnbauer, A., Berducci, L., Brandstatter, A., Lechner, M., Hasani, R., Rus, D., & Grosu, R. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9811650 · Cited by 36
Lessons for Robotics From the Control Architecture of the Octopus
Sivitilli, D. M., Smith, J. R., & Gire, D. H. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.862391 · Cited by 13
“The frequent sensing and re-planning allow the robot to react dynamically to changes in the environment, while still making more intelligent choices than a purely reactive system (Erez et al., 2011; Lenz et al., 2015; Zhang et al., 2016; Liu et al., 2017;Williams et al., 2017; Gillespie et al., 2018; Amos et al., 2019…”
Background
Metadata
- Authors
- Kabzan, J., Hewing, L., Liniger, A., & Zeilinger, M. N.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2019-10
- Volume
- 4
- Issue
- 4
- Pages
- 3363-3370
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2954115742
- Cited by
- 462
- References
- 18
- FWCI
- 18.31
- Type
- journal article
- Language
- en
- Open access
- green
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