DIY: Arduino folkrace robot - Revision history

Mol: /* Model Predictive Control Approach to Autonomous Race Driving for the F1/10 Platform */

2024-10-08T06:16:52Z

Model Predictive Control Approach to Autonomous Race Driving for the F1/10 Platform

← Older revision		Revision as of 09:16, 8 October 2024
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	<youtube>JpuX4b5ZsYE</youtube>		<youtube>JpuX4b5ZsYE</youtube>

			== Real-time Trajectory Optimization for Autonomous Vehicle Racing ==

			https://github.com/janismac/RacingTrajectoryOptimization

Mol: /* LTC21 Tutorial MPPI */

2024-10-08T06:06:40Z

LTC21 Tutorial MPPI

← Older revision		Revision as of 09:06, 8 October 2024
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	<youtube>19QLyMuQ_BE</youtube>		<youtube>19QLyMuQ_BE</youtube>


			=== 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.

			<youtube>JpuX4b5ZsYE</youtube>

Mol: /* SLAM model */

2024-10-08T05:57:37Z

SLAM model

← Older revision		Revision as of 08:57, 8 October 2024
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	* https://emanual.robotis.com/docs/en/platform/turtlebot3/appendix_lds_01/		* https://emanual.robotis.com/docs/en/platform/turtlebot3/appendix_lds_01/
	* https://www.andreasjakl.com/basics-of-ar-slam-simultaneous-localization-and-mapping/		* 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


			<youtube>19QLyMuQ_BE</youtube>

Mol: /* SLAM model */

2023-11-24T07:59:54Z

SLAM model

← Older revision		Revision as of 10:59, 24 November 2023
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	* Multiple sensors will likely be necessary.		* Multiple sensors will likely be necessary.
	* Kalman filtering		* Kalman filtering

			ORB-slam
			* https://webdiis.unizar.es/~raulmur/orbslam/

	References		References

Mol: /* SLAM model */

2023-11-24T07:59:26Z

SLAM model

← Older revision		Revision as of 10:59, 24 November 2023
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	* https://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM%20course.pdf		* 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://emanual.robotis.com/docs/en/platform/turtlebot3/appendix_lds_01/
			* https://www.andreasjakl.com/basics-of-ar-slam-simultaneous-localization-and-mapping/

Mol: /* SLAM model */

2023-11-24T07:58:59Z

SLAM model

← Older revision		Revision as of 10:58, 24 November 2023
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	* https://yoraish.com/projects/lidarbot/		* https://yoraish.com/projects/lidarbot/
	* https://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM%20course.pdf		* 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/

Mol: /* SLAM model */

2023-11-24T07:58:14Z

SLAM model

← Older revision		Revision as of 10:58, 24 November 2023
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	References		References
	* https://www.youtube.com/watch?v=saVZtgPyyJQ		* 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://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM%20course.pdf

Mol: Created page with "== Introduction == == Theory == === SLAM model === Simultaneous localization and mapping. * which sensors to use * Noice * Multiple sensors will likely be necessary. * Kalman filtering References * https://www.youtube.com/watch?v=saVZtgPyyJQ * https://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM%20course.pdf"

2023-11-24T07:55:18Z

Created page with "== Introduction == == Theory == === SLAM model === Simultaneous localization and mapping. * which sensors to use * Noice * Multiple sensors will likely be necessary. * Kalman filtering References * https://www.youtube.com/watch?v=saVZtgPyyJQ * https://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM%20course.pdf"

New page

== Introduction ==

== Theory ==

=== SLAM model ===

Simultaneous localization and mapping.
* which sensors to use
* Noice
* Multiple sensors will likely be necessary.
* Kalman filtering

References
* https://www.youtube.com/watch?v=saVZtgPyyJQ
* https://www.iri.upc.edu/people/jsola/JoanSola/objectes/curs_SLAM/SLAM2D/SLAM%20course.pdf