Guaranteed Self-Triggered Control of Disturbed Robotic Systems
Abstract
Nowadays, most of autonomous robotic systems are battery-powered and are connected to the network by short bandwidth communication protocols. The use of energy-hungry sensors, such as GNSS locators or light sensors, raises energy management issues for these systems. The main idea behind this work is therefore to reduce the number of measurement samples while guaranteeing the system’s stability despite uncertainties and disturbances. Such a strategy is known as self-triggered control, which allows the system to self-select the relevant asynchronous instants at which a new measurement will be triggered, thus guaranteeing its safety.
Location
Moliets (40), France
Centre de SĂ©minaires, Moliets, Landes 40660
Authors
Doctor in Control and Robotics
My research focuses on the efficient navigation of UAVs in constrained environments. I recently joined the LEASARD CominLabs project at LS2N Nantes, in which I investigate SLAM and navigation algorithms with event-based cameras to improve drone autonomy in cluttered environments. This work is supervised by Isabelle Fantoni. My Ph.D. thesis work, led at ICube Strasbourg and GIPSA-lab Grenoble, has been dedicated to the event-driven navigation of a drone in dark environments, as a part of the dark-NAV ANR project. This thesis has been supervised by Nicolas Marchand and Sylvain Durand. Through my research work, I developed research interests in aerial robotics, robust control and set theory. As a graduate of an engineering school (INSA Strasbourg), I have developed a taste for control theory, which I try to deploy as much as possible on real control systems.