Thesis of Julien Damers

Lab-STICC UBO GDR MACS GDR Robotique ENSTA Bretagne DGA ROBEX Sperob Gth Rob


Grant : CIFRE with Kopadia
Start : May 2019.
Supervisers : Luc Jaulin and Simon Rohou
Title : Lie groups applied to localisation of mobile robots


Defense : 2022 July 6, 10 am, Brest, Amphi 1, ENSTA-Bretagne
Manuscript :
The defense starts at 10:00. Fifteen minutes before, open the Teams link :


Hélène Piet-Lahanier, Adjointe Scientifique ONERA, Rapporteur
Michel Kieffer, Professeur des Universités, Centrale-Supélec, Rapporteur
Nicolas Delanoue, Maître de conférence à l'Université d'Angers, Examinateur
Silvère Bonnabel, Professeur à l'école des Mines ParisTech, Examinateur
David Filliat, Professeur à l'ENSTA-Paris, Examinateur
Alexandre Chapoutot, Maître de conférence à l'ENSTA-Paris, Examinateur
Simon Rohou, Maître de conférence à l'ENSTA-Bretagne, co-encadrant
Luc Jaulin, Professeur des Universités, directeur de thèse
Thierry Grousset, PDG de Kopadia, invité

Rapport de Hélène Piet Lahanier :
Rapport de Michel Kieffer :

Rapport de soutenance :


With the development of offshore activities the costs of maintenance and monitoring of offshore plants in terms of crew members, boats, and money has greatly increased and is still growing dramatically. This encouraged the development of Autonomous Underwater Vehicles (AUV). These are still very expensive because of the numerous high-end sensors they need to embark to accomplish their missions. Thus their number is relatively low. Therefore research is made to develop low-cost AUVs that could be produced in a larger amount to perform the same missions. This thesis comes within the scope of this research field. One of the main problems when dealing with AUVs is the localisation of the vehicle which will be the one addressed throughout this work. To tackle it, we present a new guaranteed integration method, which is more robust to the uncertainties on the initial condition than the ones currently available, based on Lie symmetries. This method is first presented through different simple theoretical examples. We then apply it on a the localisation problem in a robotic context

Publications of Julien Damers

Julien Damers, Luc Jaulin, Simon Rohou. Lie symmetries applied to interval integration, Automatica (accepted).

Julien Damers, Luc Jaulin, Simon Rohou. Guaranteed interval integration for large initial boxes, SWIM 2019.

Séminaires satellites

La veille et l'après midi après la thèse, se tiendront les séminaires satellites. Les exposés sont ouverts à un large public et traitent de thèmes proches de ceux abordés par la thèse de Julien (calcul par intervalles, localisation, robotique sous-marine).

Mardi 5 juillet, 15:00. Nicolas Delanoue, Amphi 3.
Titre : Version tropicale du théorème de Putinar. Applications à l’optimisation globale.

Mercredi 6 juillet, 14:30. David Filliat, Amphi 3
Title: Improving data efficiency for machine learning in robotics.
Abstract : Machine learning is a key technology for robotics and autonomous systems, in the area of sensory processing and in the area of control. While learning on large datasets for perception lead to impressive applications, applications in the area of control still present many challenges as the system to control remains slow and brittle. Reducing the required number of interactions with the system is, therefore, a key aspect of the progress of machine learning applied to robotics. We will present different approaches that can be used for this purpose, in particular approaches for State Representation Learning that can help improve data efficiency in Reinforcement Learning.

Mercredi 6 juillet, 15:00. Hélène Piet Lahanier, Amphi 3
Title: Set theoretic approach for target localization and tracking using a fleet of drones
Abstract: Among the various applications for fleets of UAVs, searching and tracking mobile targets remains a challenging task. This talk presents a distributed set-membership estimation and control scheme. This scheme relies on the description of uncertainty and noise as bounded processes. Constraints on the field of view, as well as the presence of false targets, are taken into account. Each UAV maintains several set estimates: one for each detected and identified true target, one for detected but not yet identified targets, and one for not yet detected targets, which is also the subset of the state space still to be explored. These sets are updated by each UAV using the information coming from its sensors as well as received from its neighbors.
A distributed set-membership model predictive control approach is considered to compute the trajectories of UAVs. The control input minimizing a measure of the volume of the set-membership estimates predicted h steps ahead is then evaluated. Simulations of scenarios including the presence of false targets illustrate the ability of the proposed approach to efficiently search and track an unknown number of moving targets within some delimited search area.

Mercredi 6 juillet, 15:30. Michel Kieffer, Amphi 3
Title: Localization of partially hidden targets using a fleet of UAVs via robust bounded-error estimation
Abstract: In this talk, we address the cooperative search of static ground targets by a group of UAVs over some region of interest. The search strategy dependents on the availability and accuracy of the information collected. When a target is detected, a probabilistic description of the measurement noise is usually considered, as well as probabilities of false alarm and non-detection, which may prove difficult to characterize a priori.
An alternative modeling is introduced here. The ability to detect and identify a target depends deterministically on the point of view from which the target is observed. Introducing the notion of detectability sets for targets, we propose a robust distributed set-membership estimator to provide set estimates of target locations. The obtained set estimates are guaranteed to contain all target locations when the search is completed. The target search is again formulated as a multi-agent cooperative control problem where the control inputs are obtained using a Model Predictive Control (MPC) approach minimizing a measure of the set estimates representing the detection performance. The effectiveness of the proposed algorithm is illustrated by simulation.



Julien et Paul Antoine : les AUV retournent à Kopadia