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
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.