ROS Implementation of Planning and Robust Control Strategies for Autonomous Vehicles
Abstract
:1. Introduction
- Designing the ROS2 architecture: The initial focus is on developing the ROS2 architecture, taking into account technical implementation considerations. ROS2 serves as a crucial tool for testing of control algorithms. Although there has been some progress in implementing an autonomous vehicle architecture in ROS2, accomplishing this objective has proven challenging due to the variety of tasks involved. The framework will be utilized by colleagues to implement decision making and different vehicle lateral dynamic control algorithms.
- Estimating the lateral dynamics parameters of the new vehicle: With the introduction of a new vehicle, it is necessary to identify its model and estimate the lateral dynamics parameters. This step involves conducting multiple experiments, processing collected data, and addressing the mechanical aspects of the vehicle. Additionally, a realistic simulation is designed to test the controllers prior to implementation.
- Control of the lateral dynamics using a robust controller and planning algorithms: The final objective is to control the lateral dynamics of the vehicle utilizing a robust controller and planning algorithms. This controller can be easily tuned and demonstrates good performance for linear systems. It also accounts for uncertainties and disturbances affecting the system. Furthermore, since the algorithm considers various system parameters, the LPV tool is tested to enhance vehicle tracking performance.
2. Experimental Setup
Autonomous Vehicle ROS Implementation Framework
3. Control Theory
3.1. Definition of Norm
3.2. Control Problem Formulation
- : Robustness required with max module margin;
- : Tracking speed and rejecting disturbances;
- : Steady-state tracking error;
- : Actuator constraints based on ;
- : Actuator bandwidth;
- : Attenuated noises on controlled input.
3.3. LPV System Modeling Formulation
3.3.1. Affine Parameter Dependence
3.3.2. Polytopic Modeling Approach
4. Estimation of the Bicycle Lateral Model Parameters
4.1. Mathematical Modeling
4.2. System Identification
5. Lateral Control Design
5.1. Look-Ahead Reference Generator
5.2. Controller
5.3. Controller
6. Simulation and Experimental Results
6.1. Simulation of LTI/ Controller
6.2. Simulation of LPV/ Controller
6.3. Experimental Results of LTI/
6.4. Experimental Results of LPV/
6.5. Experimental Comparison between LTI and Polytopic LPV
7. Conclusions
- We introduced the ROS2 environment, achieved through the design and development of a versatile architecture that breaks down the vehicle software controller into distinct levels. Each of these levels was addressed and adjusted independently.
- We introduced a concise discussion of the lateral dynamic model of the system, highlighting the impact of on its linear mode. Additionally, a parameter estimation-based prediction error method was presented, accompanied by realistic outcomes.
- The system is categorized as LTI or LPV depending on the variation of . For each scenario, a robust controller was designed and practically implemented using the previously described ROS2 architecture. In terms of experimental comparison, this study reveals the limitations of the LTI/ approach compared to the LPV/ approach, particularly when dealing with velocities higher than the designed operating point.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Type | Functionality | |
---|---|---|
1 | Swicth | Switching car power ON/OFF. |
2 | 8 mm Qualisys super-spherical | Captured by Vicon tracker. |
3 | Arduino RP 2040 | Microcontroller of the vehicle. |
4 | Spur gears | Increase torque provided by BLDC. |
5 | Elastic wheel | 2 rear wheels of the vehicle. |
6 | ACCU NI-MH 3000 | Supply battery power. |
7 | MG996R servo motor | Steering actuator. |
8 | Elastic wheel | 2 Front wheels of the vehicle. |
9 | BLDC–A2212/13T | Throttle actuator. |
Parameter | Value | S.I Units |
---|---|---|
m | Kg | |
Kg·m | ||
m | ||
m |
Parameter | Value |
---|---|
2 | |
(rad/s) | 3.14 |
1 | |
(rad/s) | 31.4 |
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Hachem, M.; Borrell, A.M.; Sename, O.; Atoui, H.; Morato, M. ROS Implementation of Planning and Robust Control Strategies for Autonomous Vehicles. Electronics 2023, 12, 3680. https://doi.org/10.3390/electronics12173680
Hachem M, Borrell AM, Sename O, Atoui H, Morato M. ROS Implementation of Planning and Robust Control Strategies for Autonomous Vehicles. Electronics. 2023; 12(17):3680. https://doi.org/10.3390/electronics12173680
Chicago/Turabian StyleHachem, Mohamad, Ariel M. Borrell, Olivier Sename, Hussam Atoui, and Marcelo Morato. 2023. "ROS Implementation of Planning and Robust Control Strategies for Autonomous Vehicles" Electronics 12, no. 17: 3680. https://doi.org/10.3390/electronics12173680
APA StyleHachem, M., Borrell, A. M., Sename, O., Atoui, H., & Morato, M. (2023). ROS Implementation of Planning and Robust Control Strategies for Autonomous Vehicles. Electronics, 12(17), 3680. https://doi.org/10.3390/electronics12173680