Victor Boutin

Controlling an aerial robot by human semaphore gestures using a bio-inspired neural network (PhD, 12/2016-12/2019)

Contrôle d'un robot volant opéré par des mouvements sémaphores humains grâce à un réseau de neurones bio-inspirés

Aix-Marseille University DOC2AMU is an innovative H2020-MSCA-COFUND
Aerial Robots, Vision, Neural Networks, Bio-Inspired Computer Vision, Gaze orientation, learning
Thesis director
Dr. Laurent PERRINET, Director's research unit: Institut de Neurosciences de la Timone (INT)
Thesis co-supervisition
Dr. Franck RUFFIER Co-director's research unit: Institut des Sciences du Mouvement (ISM)

Description of the PHD thesis project

Robotics is a rapidly evolving technology that allows for fast, low-risk and low-cost tasks with a worldwide market of over 80 billion dollars over the next few years. In particular, aerial robots, also known as drones, provide a breakthrough to easily image and access all sorts of terrains and situations and are useful for instance in surveillance and forensics, emergency industrial inspection or a search and rescue operation. A major difficulty for their global acceptance is the difficulty for controlling their flight and interacting with them.

Indeed, aerial robots are generally operated using a (central) ground station which is not compatible with the time pressure required by emergency conditions, for instance when rescuing a person out of reach with the ground station. This PhD project aims at concealing such obstacles and construct an aerial robot which is able to be autonomously and interactively controlled by simple human gestures, for instance that of a rescuer. The main scientific challenges are (i) to embed in the aerial robot all the electronics for the visual system from the retina to the control signals to the propellers, (ii) to very quickly recognize a variety of simple gestures on-board using a neuromimetic architecture and (iii) to make the robot react in real time to these gestures. As such, this project is inter-disciplinary by positively combining advanced algorithms from event-based bio-inspired computer vision and the latest technology in aerial robots.

  1. R. Benosman , S.-H. Leng , C. Clercq , C. Bartolozzi & M. Srinivasan (2012) “Asynchronous frameless event-based optical flow”, Neural Networks - Elsevier

  2. S.-C. Liu & T. Delbruck (2010) “Neuromorphic sensory systems”, Current opinion in neurobiology - Elsevier

  3. J. Nagi, A. Giusti, G. A. Di Caro, L. M. Gambardella (2014) “HRI in the Sky, Controlling UAVs using Face Poses and Hand Gestures”, HRI

3I dimensions and other aspects of the project

The present PhD proposal is at the crossroad between various disciplines. It first concerns biology and neuroscience because its event-based approach is strongly inspired from the neuronal network observed in animals such as insects to primates and used for navigation, obstacle avoidance, and sensori-motor control. It is also covering electronics, aerial robotics and signal processing as the main project achievement is to create a working spike-based electronic architecture able to recognize body movement, and to use it to control the robot. Such an oucome will have beneficial outcomes with respects to the SRI-S3 regional strategy, in particular with respect to “risks, security and safety”.

This project is a partnership between two different doctoral schools based in Marseille: the EDSMH at ISM for the robotic part, and the EDSVS at INT concerning visual processing and spike-based processing methods. This partnership will provide the ESR with the best resources to achieve his goals. In particular, the ISM owns a brand new flying arena (funded by Robotex project, equipped with high-tech motion capture tools (Vicon) and the INT has a entire technological platform dedicated for high-performance computing and measurement tool prototyping.

Combining neuroscience and robotics to design novel electronic architectures is an innovative and a valuable approach in Robotics. The doctoral student selected for this project will acquire experience in bio-inspired hardware architectures, which is going to be valuable in his career as there is a need to adapt actual electronic architecture to for instance spike-based visual processing.

List of publications

  • Julien Dupeyroux, Victor Boutin, Julien R Serres, Laurent U Perrinet, Stéphane Viollet. M2APix: a bio-inspired auto-adaptive visual sensor for robust ground height estimation, URL URL2 . In ISCAS2018, IEEE International Symposium on Circuits and Systems, 2018 abstract.

  • Victor Boutin, Franck Ruffier, Laurent Perrinet. Efficient learning of sparse image representations using homeostatic regulation, URL . In SPARS2017, Lisbon, 2017 abstract.

  • Victor Boutin, Franck Ruffier, Laurent Perrinet. Efficient learning of sparse image representations using homeostatic regulation, URL . In NeuroFrance 2017, International Conference from the Société des Neurosciences, Bordeaux, France, 2017 abstract.

  • Victor Boutin, Angelo Franciosini, Franck Ruffier, Laurent Perrinet. From biological vision to unsupervised hierarchical sparse coding, URL URL2 . In iTwist, 2018, 2018 abstract.

This work was supported by the Doc2Amu project which received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 713750. Projet cofinancé par le Conseil Régional Provence-Alpes-Côte d’Azur.Projet cofinancé par le Conseil Régional Provence-Alpes-Côte d’Azur, la commission européenne et les Investissements d'Avenir.

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