Description du sujet de thèse

Multi-robot systems are increasingly being deployed in applications such as surveillance, environmental monitoring, and industrial automation. Most of the current works focus on
homogeneous multi-robot systems collaboratively carrying out a single simple task such as coverage, exploration, object pushing, etc. [1]. However, achieving seamless
collaboration among heterogeneous robots with diverse capabilities performing complex missions remains a challenge. These missions often consist of multi-objective tasks
(e.g., in an agricultural setting: foraging, transportation, inspection, etc.) requiring different tools or capabilities in expansive and dynamically changing environments, necessitating
advanced interaction models.


Envisaged activities
Interaction paradigms observed in social and biological networks such as, e.g., stubbornness, clustering, or inhibitory interactions, have the potential to improve the coordination of
heterogeneous robots by enabling them to exhibit adaptive, resilient, and complex behaviors to execute multi-task missions in dynamically changing environments.
This PhD aims to enhance the coordination of heterogeneous multi-robot systems by leveraging the dynamic models used to describe complex interactions in social
and biological networks. The plan of this PhD is to adapt the latter to multi-robot systems with sensing and actuation constraints in order to resiliently perform multi-
objective tasks in dynamically changing environments.
For this purpose the goal is to directly model the interactions among the robots based on nonlinear agreement protocols, opinion dynamics and multi-layer graphs,
taking into account the different capabilities of the agents and the multi-objective tasks. Current works on opinion dynamics consider only static and unconstrained agents where the
model of the opinion dynamics are used to explain different phenomena occurring in social or biological networks. This PhD will investigate instead how the inclusion of an opinion
layer in the interactions of multiple heterogeneous mobile robots with sensing and actuation constraints can lead to complex and resilient behaviors in the context of multi-objective
missions. In a second time, and due to the complexity of the system, approaches based on reinforcement learning may be considered to increase the adaptability and robustness of
the swarm to dynamic conditions.
Experimental validation: the control methods and algorithms will be validated and tested on tasks progressively advancing from simpler missions such as forming clusters and
simultaneously aggregating at different locations of interest, to a long-term dynamical mission that needs reconfiguration of the robotic team. The experimental validation will utilize
the facilities of the Rainbow team, including two indoor and multi-robot arenas and several mobile robots (e.g. a team of Crazyflie mini drones).

Contexte de travail

The PhD will be carried out in the Rainbow team at IRISA/Inria, Rennes


- The Ph.D. position is full-time for 3 years (standard duration in France). The position will be paid according to the French salary regulations for PhD students.
- We do high quality and impactful research in robotics, publishing on the major journals and conferences.
- We often collaborate with other top researchers in europe and worldwide.
- You will have access to a well established laboratory including: two flying arenas equipped with motion tracking system, several quadrotors, and a few fully-actuated manipulators; one robotic manipulation lab equipped with several robotic arms, like the Franka Emika Panda.
- You will be part of an international and friendly team. We organize several events, from after works, to multi-day lab retreat.
- Regular visits and talks by internationally known researchers from top research labs.




The expected competences are:

- M.Sc. degree in mechatronics, robotics, engineering, computer science (or related fields)
- Excellent written and spoken English skills
- Good experience in C/C++ , ROS, Matlab/Simulink, CAD
- Good experience with numerical trajectory optimization tools for robotics (e.g., use of CaSaDi, Acado, Autodiff, Crocoddyl, etc.)
- Scientific curiosity, large autonomy and ability to work independently
- Experience with robotic systems and/or aerial robots is a plus

Le poste se situe dans un secteur relevant de la protection du potentiel scientifique et technique (PPST), et nécessite donc, conformément à la réglementation, que votre arrivée soit autorisée par l'autorité compétente du MESR.

Contraintes et risques

nothing to report