DIY: Arduino folkrace robot: Difference between revisions
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=== Model Predictive Control Approach to Autonomous Race Driving for the F1/10 Platform === | |||
Originally presented at IFAC WC 2020, A. Tatulea-Codrean, T. Mariani, S. Engell. | |||
Abstract: | |||
This paper addresses the challenges of developing an embedded non-linear model predictive control (NMPC) solution for the optimal driving of miniature scale autonomous vehicles (AVs). The NMPC approach lends itself perfectly to driving applications, provided that a system for localization and tracking of the vehicle is available. An important challenge in the implementation results from the need to accurately steer the vehicle at high speeds, which requires fast actuation. In this paper we present a solution to this problem, which employs an artificial neural network (ANN) controller trained with rigorous NMPC input-output data. We discuss the development process, from modelling until the realization of the ANN controller within the operating system of the AV. The procedure is demonstrated within the virtual environment of the popular F1/10 race car, an AV platform widely used in AI and autonomous driving challenges. The results contain both NMPC and ANN-based simulations for different race tracks and for different driving strategies. The main focus of this work lies in the formulation of the optimal driving control problem and the training method of the ANN. Our approach uses a standardization of the driving problem, which enables us to abstractize optimal driving and to simplify it for the learning process. We show how driving patterns can be learned accurately on a reduced set of training data and that they can subsequently be extended to new and more challenging driving situations. | |||
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Revision as of 08:06, 8 October 2024
Introduction
Theory
SLAM model
Simultaneous localization and mapping.
- which sensors to use
- Noice
- Multiple sensors will likely be necessary.
- Kalman filtering
ORB-slam
References
- https://www.youtube.com/watch?v=saVZtgPyyJQ
- https://www.youtube.com/@yoraish/videos
- https://yoraish.com/projects/lidarbot/
- https://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM%20course.pdf
- https://emanual.robotis.com/docs/en/platform/turtlebot3/appendix_lds_01/
- https://www.andreasjakl.com/basics-of-ar-slam-simultaneous-localization-and-mapping/
LTC21 Tutorial MPPI
Telluride Neuromorphic Workshop tutorial For Model Predictive Path Integral Control method of G. Williams, A. Aldrich, and E. A. Theodorou, “Model Predictive Path Integral Control: From Theory to Parallel Computation,” J. Guid. Control Dyn., vol. 40, no. 2, pp. 344–357, Feb. 2017, doi: 10.2514/1.G001921. [Online]. Available: https://doi.org/10.2514/1.G001921
Model Predictive Control Approach to Autonomous Race Driving for the F1/10 Platform
Originally presented at IFAC WC 2020, A. Tatulea-Codrean, T. Mariani, S. Engell.
Abstract: This paper addresses the challenges of developing an embedded non-linear model predictive control (NMPC) solution for the optimal driving of miniature scale autonomous vehicles (AVs). The NMPC approach lends itself perfectly to driving applications, provided that a system for localization and tracking of the vehicle is available. An important challenge in the implementation results from the need to accurately steer the vehicle at high speeds, which requires fast actuation. In this paper we present a solution to this problem, which employs an artificial neural network (ANN) controller trained with rigorous NMPC input-output data. We discuss the development process, from modelling until the realization of the ANN controller within the operating system of the AV. The procedure is demonstrated within the virtual environment of the popular F1/10 race car, an AV platform widely used in AI and autonomous driving challenges. The results contain both NMPC and ANN-based simulations for different race tracks and for different driving strategies. The main focus of this work lies in the formulation of the optimal driving control problem and the training method of the ANN. Our approach uses a standardization of the driving problem, which enables us to abstractize optimal driving and to simplify it for the learning process. We show how driving patterns can be learned accurately on a reduced set of training data and that they can subsequently be extended to new and more challenging driving situations.
