I’m Ashley Hill, a brit who has spent his life in france. Currently a PhD student in Artificial Intelligence applied to Mobile Robots. You can usually find me playing around with Machine Learning, Robotics, Electronics, Astronomy, and Programming.
MSci in Machine Learning, Information and Content, 2018
Magistaire in Computer Science, 2018
BSc in Computer Science, 2016
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.