loading
Papers

Research.Publish.Connect.

Paper

Neuroevolution with CMA-ES for Real-time Gain Tuning of a Car-like Robot Controller

Topics: Engineering Applications on Intelligent Control Systems and Optimization; Evolutionary Computation and Control; Genetic Algorithms; Machine Learning in Control Applications; Mobile Robots and Autonomous Systems; Neural Networks Based Control Systems; Optimization Algorithms; Vehicle Control Applications

Authors: Ashley Hill 1 ; Eric Lucet 1 and Roland Lenain 2

Affiliations: 1 CEA, LIST, Interactive Robotics Laboratory, Gif-sur-Yvette, F-91191 and France ; 2 Université Clermont Auvergne, Irstea, UR TSCF, Centre de Clermont-Ferrand, F-63178 Aubière and France

ISBN: 978-989-758-380-3

Keyword(s): Neuroevolution, Machine Learning, Neural Network, Evolution Strategies, Gradient-free Optimization, Robotics, Mobile Robot, Control Theory, Gain Tuning, Adaptive Control.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computation and Control ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Mobile Robots and Autonomous Systems ; Neural Networks Based Control Systems ; Optimization Algorithms ; Robotics and Automation ; Soft Computing ; Vehicle Control Applications

Abstract: This paper proposes a method for dynamically varying the gains of a mobile robot controller that takes into account, not only errors to the reference trajectory but also the uncertainty in the localisation. To do this, the covariance matrix of a state observer is used to indicate the precision of the perception. CMA-ES, an evolutionary algorithm is used to train a neural network that is capable of adapting the robot’s behaviour in real-time. Using a car-like vehicle model in simulation. Promising results show significant trajectory following performances improvements thanks to control gains fluctuations by using this new method. Simulations demonstrate the capability of the system to control the robot in complex environments, in which classical static controllers could not guarantee a stable behaviour.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.227.2.246

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Hill, A.; Lucet, E. and Lenain, R. (2019). Neuroevolution with CMA-ES for Real-time Gain Tuning of a Car-like Robot Controller.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, pages 311-319. DOI: 10.5220/0007927103110319

@conference{icinco19,
author={Ashley Hill. and Eric Lucet. and Roland Lenain.},
title={Neuroevolution with CMA-ES for Real-time Gain Tuning of a Car-like Robot Controller},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={311-319},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007927103110319},
isbn={978-989-758-380-3},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Neuroevolution with CMA-ES for Real-time Gain Tuning of a Car-like Robot Controller
SN - 978-989-758-380-3
AU - Hill, A.
AU - Lucet, E.
AU - Lenain, R.
PY - 2019
SP - 311
EP - 319
DO - 10.5220/0007927103110319

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.