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Online gain setting method for path tracking using CMA-ES: Application to off-road mobile robot control

Abstract : This paper proposes a new approach for online control law gains adaptation, through the use of neural networks and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, in order to optimize the behavior of the robot with respect to an objective function. The neural network considered takes as input the current observed state as well as its uncertainty, and provides as output the control law gains. It is trained, using the CMA-ES evolution strategy, on a simulator reproducing the vehicle dynamics. Then, it is tested in real conditions on an agricultural mobile robot at different speeds. The transferability of this method from simulation to a real system is demonstrated, as well as its robustness to environmental changes, such as GPS signal degradation or ground variation. As a result, path following errors are reduced, while ensuring tracking stability.
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https://hal-cea.archives-ouvertes.fr/cea-03314566
Contributor : Eric Lucet <>
Submitted on : Thursday, August 5, 2021 - 10:10:55 AM
Last modification on : Tuesday, September 7, 2021 - 3:44:09 PM

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  • HAL Id : cea-03314566, version 1

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Ashley Hill, Jean Laneurit, Roland Lenain, Eric Lucet. Online gain setting method for path tracking using CMA-ES: Application to off-road mobile robot control. IROS 2020, International Conference on Intelligent Robots and Systems, Oct 2020, Las Vegas, United States. ⟨cea-03314566⟩

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