The Franco-Australian IRP-ARS (Advanced Autonomy for Robotic Systems)

The Franco-Australian IRP-ARS (Advanced Autonomy for Robotic Systems) is managed by Professor Tarek Hamel (I3S UMR-CNRS 7271, Côte d’Azur University) in collaboration with Professor Robert Mahony  (Australian Centre for Robotic Vision, Australian National University) and started in 2020.

France
• Professor Tarek Hamel. I3S UMR-CNRS 7271, Côte d’Azur University (leader of the French part)CRAN UMR-CNRS 7039, Université de Lorraine and IRISA UMR -CNRS 6074, Rennes.

Australia
• Professor Robert Mahony. Australian Centre for Robotic Vision, Australian National University (leader of the Australian part), the University of Melbourne and Monash University.

control and perception algorithms ; Unmanned Robotic Systems ; high quality state estimation

MISSIONS AND RESEARCH THEMES

During the last two decades important progress has been made in the development of advanced methods for motion control of unmanned autonomous robotic systems.  The commercial landscape of these vehicles is characterized by a plethora of small start-up companies proposing an increasing variety of devices for various applications. This rapidly growing market has boosted research studies in the field of autonomous robotic systems, particularly from the Systems and Control community.

The objective of the IRP-ARS is to pool the top-level resources of research institutions from both France and Australia and build an excellence centre  to go beyond the state of the art in systems and control theory applied to Unmanned Robotic Systems. It will be built on existing collaborations and it will establish new ones between the top-level research entities.

Theoretical open problems with high impact in practical applications will be addressed to enable Unmanned Robotic Systems to operate more robustly and more reliably in complex and cluttered dynamic environments encountered in real-world applications. These aims will be achieved by meeting the following objectives:

  • Develop a unified control approach of a large class of Unmanned Robotic Systems (Airplanes, helicopters and other Vertical Take-Off and Landing (VTOL) vehicles, blimps, rockets, hydroplanes, ships and submarines are generally underactuated)
  • Develop a unifying scientific framework for the analysis and design of observers for general systems with symmetry of the system with high quality state estimation (position, velocity, orientation, etc.).

Exploit the latest advances in deep learning to develop ‘front-end’ sensor processing that directly integrates into globally well-defined control and perception algorithms for systems with symmetry.