Phd position f - m hardware acceleration for unmanned aerial vehicle control algorithms h/f

A propos d'Inria

Inria est l'institut national de recherche dédié aux sciences et technologies du numérique. Il emploie 2600 personnes. Ses 215 équipes-projets agiles, en général communes avec des partenaires académiques, impliquent plus de 3900 scientifiques pour relever les défis du numérique, souvent à l'interface d'autres disciplines. L'institut fait appel à de nombreux talents dans plus d'une quarantaine de métiers différents. 900 personnels d'appui à la recherche et à l'innovation contribuent à faire émerger et grandir des projets scientifiques ou entrepreneuriaux qui impactent le monde. Inria travaille avec de nombreuses entreprises et a accompagné la création de plus de 200 start-up. L'institut s'eorce ainsi de répondre aux enjeux de la transformation numérique de la science, de la société et de l'économie. PhD Position F/M Hardware Acceleration for Unmanned Aerial Vehicle Control Algorithms
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : CDD

Niveau de diplôme exigé : Bac +5 ou équivalent

Fonction : Doctorant

A propos du centre ou de la direction fonctionnelle

The Inria Rennes - Bretagne Atlantique Centre is one of Inria's eight centres and has more than thirty research teams. The Inria Center is a major and recognized player in the field of digital sciences. IT is at the heart of a rich R&D and innovation ecosystem : highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.

Contexte et atouts du poste

Disclaimer

A PhDis nota continuation of coursework or a natural next step after a Master's degree. A PhD is a long-term, research-focusedcommitmentthat requires deepcuriosity,self-motivation, resilience, and a certaindegree ofautonomy.

By research, we meancreating new knowledge, not just applying existing theories. Your task is to discover, design, or prove something that no one has done before-work that will become what future students study.

If you are mainly looking for structured classes, predefined assignments, or a repeat of your Master's experience, you willlikely find this path unfulfilling. We welcome applications from candidates who are excited by uncertainty, driven to ask original questions, and eager to shape the frontier of their field.

Context & background :

Robots are physical agents that interact with their physical environment. Accordingly, their sensorimotor capabilities are essential and largely determine the activities that robots can perform. In recent years, great progress has been made in sensory capabilities thanks to significant advances in machine learning and dedicated hardware. In contrast, much less progress has been made in motor skills. Examples of promising approaches in the current scientific literature are Model Predictive Control (MPC) [1] and Model Predictive Path Integral (MPPI) control [2], where control actions are optimized over a finite time horizon, considering the time evolution of robot dynamics to optimize a given cost or reward function that describes the robot motion. Such algorithms are particularly suited foroptimizing control trajectories andplanning horizonsin real timedue to their ability to handle dynamic environments.

From a control perspective, planning a horizon that is as long as possibleto manage complex trajectories while considering the environment is essential. Additionally, maintaininga high control frequencyis crucial to meet thereal-timedemands imposed by real-world physics and, if necessary, to adjust the sequence of movements. In theresource-constrained context ofsmall-scale UAVs, such control algorithms are crucial as they enableoptimal trajectory generation and real-time decision-makingin complex, dynamic, and uncertain environments. However, particularly forbattery-powered UAVs, achieving a high control frequency while planning for a long horizon is difficult due to limited computational power and energy constraints [3], and conventional GPU acceleration often requires excessive energy consumption.

In recent years, hardware acceleration[4] has become increasingly popular, usingdedicated platformssuch asFPGAs(Field Programmable Gate Arrays) andASICs(Application-specific Integrated Circuits), increasing energy efficiency byorders of magnitude[5]. However, dedicated hardware acceleration for small-scale UAV control has not been proposed.

The PhD is in collaboration between the computer architecture team () and the robotics team () at Inria Centre at Rennes University.

Contact people :

Marcello Traiola, Tommaso Belvedere, Marco Tognon,

[1] E. F. Camacho and C. Bordons, Model Predictive control. in Advanced Textbooks in Control and Signal Processing. London : Springer, 2007. doi : 10.1-5.
[2] G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, Aggressive driving with model predictive path integral control, in 2016 IEEE International Conference on Robotics and Automation (ICRA), May 2016, pp. 1433-1440. doi : 10.1109/ICRA.2.
[3] K. Nguyen, S. Schoedel, A. Alavilli, B. Plancher, and Z. Manchester, TinyMPC : Model-Predictive Control on Resource-Constrained Microcontrollers, in 2024 IEEE International Conference on Robotics and Automation (ICRA), May 2024, pp. 1-7. doi : 10.1109/ICRA57147.2.
[4] W. J. Dally, Y. Turakhia, and S. Han, Domain-specific hardware accelerators, Commun ACM, vol. 63, no. 7, pp. 48-57, Jun. 2020, doi : 10.1145/3361682
[5] J. L. Hennessy and D. A. Patterson, A new golden age for computer architecture, Commun ACM, vol. 62, no. 2, pp. 48-60, Jan. 2019, doi : 10.1145/3282307.

Mission confiée

This Ph.D. thesis aims to usealgorithm-specific custom hardware accelerationto implementefficient real-time control for UAVswithlong prediction horizonsandhigh control frequencies. The structure of the control algorithms is complex and sensitive to numerical errors or reduced arithmetic precision. Thus, applying ahardware-algorithm Co-designapproach is necessary, i.e., adaptingthecontrol algorithms to the hardwareanddesigning the hardwareto suit the control algorithms optimally.

Principales activités

After a detailed study of UAV state-of-the-art control algorithms, the student will identify HW acceleration opportunities, such asparallelization, pipelining, anddata specialization. The student will applyco-designapproaches to realize efficient accelerators, utilizing the control algorithms' properties to improve the hardware while adjusting the algorithms to the hardware's characteristics.Simulationswill BE carried out to validate the proposed approaches and prepare the finalintegration in the UAV platform, which is already available to the RAINBOW team.

Compétences

Required technical skills :
- Good knowledge ofcomputer architecturesandembedded systems
- HW design : VHDL/Verilog basics, HW synthesis flow
- Programmingknowledge (C/C++, python)
- Experience in HW/SW co-design and robotics is a plus

Candidates must have a Master's degree (or equivalent) inComputer Engineeringorrelated areas relevant to the PhD topic

Languages : proficiency in written English and fluency in spoken English are required.

Relational skills :thecandidatewillworkin a research team, where regular meetings will BE set up.The candidatehas to BE able to present the progress oftheir work in a clear and detailed manner.

Othervalues appreciated areopen-mindedness, strong integration skills, and team spirit.

Most importantly, we seek highly motivated candidates.

Avantages
- - Subsidized meals
- Partial reimbursement of public transport costs
- Possibility of teleworking (90 days per year) and flexible organization of working hours
- Partial payment of insurance costs

Rémunération

monthly gross salary 2200 Euros