EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 388 (2023) MATEC Web Conf., 388 (2023) 04018 References
Open Access
Issue
MATEC Web Conf.
Volume 388, 2023
2023 RAPDASA-RobMech-PRASA-AMI Conference Advanced Manufacturing Beyond Borders - The 24th Annual International RAPDASA Conference joined by RobMech, PRASA and AMI, hosted by CSIR and CUT
Article Number 04018
Number of page(s) 18
Section Robotics and Mechatronics
DOI https://doi.org/10.1051/matecconf/202338804018
Published online 15 December 2023
  1. https://www.formulastudent.de/about/concept/ [Google Scholar]
  2. https://f1tenth.org/build [Google Scholar]
  3. A. Liniger, A. Domahidi, M. Morari, Optimization-Based Autonomous Racing of 1:43 Scale RC Cars. Optimal Control Applications and Methods, vol 36, 628–647. (2015) [CrossRef] [Google Scholar]
  4. P. Polack, F. Altché, B. Novel, A. de La Fortelle, The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?, in Proceedings of the IEEE Intelligent Vehicles Symposium, IEEE, 812–818. (2017) [Google Scholar]
  5. V. Fors, B. Olofsson, L. Nielsen, Formulation and interpretation of optimal braking and steering patterns towards autonomous safety-critical manoeuvres. Vehicle System Dynamics, vol 57, 1–1, (2018) [Google Scholar]
  6. T. Novi, A. Liniger, R. Capitani, C. Annicchiarico, Real-time control for at-limit handling driving on a predefined path. Vehicle System Dynamics. vol 58. 1–30. (2019) [Google Scholar]
  7. J. Kong, M. Pfeiffer, G. Schildbach, F. Borrelli, Kinematic and dynamic vehicle models for autonomous driving control design, in Proceedings of the IEEE Intelligent Vehicles Symposium, IEEE, 1094–1099. (2015) [Google Scholar]
  8. M. Ahlberg, Optimization Based Trajectory Planning for Autonomous Racing, Masters Thesis (2018) [Google Scholar]
  9. C. Samak, T. Samak, S. Kandhasamy, Autonomous Racing using a Hybrid ImitationReinforcement Learning Architecture, arXiveprints (2021) [Google Scholar]
  10. A. Heilmeier, A. Wischnewski, L. Hermansdorfer, J. Betz, M. Lienkamp, B. Lohmann, Minimum curvature trajectory planning and control for an autonomous race car. Vehicle System Dynamics, vol 58. 1–31, (2020) [Google Scholar]
  11. José L. Vázquez and Marius Brühlmeier and Alexander Liniger and Alisa Rupenyan and John Lygeros. Optimization-Based Hierarchical Motion Planning for Autonomous Racing, in Proceedings of the IEEE International Conference on Intelligent Robots and Systems, IEEE, 2397–2403. (2020) [Google Scholar]
  12. T. Sjolin and A. Sundberg, Trajectory planning and control of an autonomous race vehicle, Masters Thesis, 2021. [Google Scholar]
  13. A. Rucco, G. Notarstefano, J. Hauser, An Efficient Minimum-Time Trajectory Generation Strategy for Two-Track Car Vehicles, IEEE Transactions on Control Systems Technology. vol 23. (2015) [Google Scholar]
  14. Liniger, A. Path Planning and Control for Autonomous Racing, Doctoral Thesis (2018) [Google Scholar]
  15. H.B. Pacejka and E. Bakker, The magic formula tyre model, Vehicle system dynamics, vol 21, pp. 1–18. (1992) [CrossRef] [Google Scholar]
  16. M.L. Bynum, G. Hackebeil, W. Hart, C. Laird, B. Nicholson, J. Siirola, J. Watson, D. Woodruff. Pyomo - Optimization Modeling in Python. Third Edition vol. 67. Springer (2021) [CrossRef] [Google Scholar]
  17. W. Hart, J. Watson, D. Woodruff, Pyomo: modeling and solving mathematical programs in Python, Mathematical Programming Computation, vol 3, 219–260. (2011) [CrossRef] [Google Scholar]
  18. A. W. Achter, L. T. Biegle, On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Mathematical programming, vol 106, 25–57 (2006) [CrossRef] [Google Scholar]
  19. K. Vivek, M.A. Sheta, V. Gumtapure, A Comparative Study of Stanley, LQR and MPC Controllers for Path Tracking Application, in Proceedings of the IEEE International Conference on Intelligent Systems and Green Technology, 67–674. (2019) [Google Scholar]
  20. M. Ester, H.P. Kriegel, J. Sander, X. Xu et al. A density-based algorithm for discovering clusters in large spatial databases with noise, in Proceedings of the International Conference on Knowledge Discovery and Data Mining, vol. 96, 226–231 (1996) [Google Scholar]
  21. M. O’Kelly, et al., F1TENTH: An Open-source Evaluation Environment for Continuous Control and Reinforcement Learning, in Proceedings of the NeurIPS 2019 Competition and Demonstration Track, 77–89. (2020) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.