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This project, funded by Gebert Rüf Stiftung, is supported by the following project partners: Sevensense Robotics AG; Wyss Zurich, NCCR Robotics; ESA Business Incubation Center; Venture Kick
Données de projet
Numéro du projet: GRS-031/18
Subside accordé: CHF 296'000
Durée: 02.2019 - 08.2020
Champs d'activité: Pilotprojekte, 1998 - 2018
Direction du projet
PhD Gregory Hitz
8092 Zürich (Schweiz)
Mobile robots have the potential to bring disruptive changes to our everyday lives. However, to be able to move autonomously not only behind closed doors of factories, they need to be able to interact with other moving systems and, more importantly, with people. They need to react appropriately and socially compliant, such that they do not interfere or scare people around them.
In this project we will develop a compliance-driven navigation framework that will allow robots to become compliant in dynamic environments. For we will look at the problem holistically, from the sensors that perceive the robot’s surroundings, to the control strategy that will guide it through public spaces.
Quelles sont les particularités de ce projet?
Mobile robots have brought significant increase in efficiency and effectiveness to several industries. For our everyday life, this also had an impact, even though it mostly happened behind the closed doors of ware houses and logistics centers. It is largely due to the efficiency that robots brought to ware houses, that we can conveniently order most products online and expect them at our door step the following day.
Within this project we will enable such mobile robotic systems to work safely in areas that are not completely inaccessible to humans. In fact, many industries and applications could benefit from the capabilities of mobile robots. Imagine robotic cleaning machines or delivery robots, that work seamlessly in crowded areas such as airports. However, to enable such machines to work safely and efficiently among humans, they need to become compliant with human interaction. They need to be able to move through crowded areas while not disturbing the people around them. Furthermore, they need to be able to navigate in complex environments and need to have an efficient and robust way to measure their own position in space. In this project visual Simultaneous Localization and Mapping (SLAM) technologies will be developed which give these robotic systems the flexibility they need.
The team has successfully completed the first phase of the project during which a suite of sensor systems has been selected and carefully evaluated. A prototype has been built up, such that it can be used as a testing platform during the project. With this prototype the team has been able to collect invaluable data sets, which will form a fundamental component for the development work in the second stage of the project.
The team is now working on the creation of a holistic approach to obstacle avoidance in crowded and dynamic spaces. In particular, the identification of moving objects and humans is essential for this. Once we are able to distinguish moving from static obstacles, we will be able to work on approaches to avoid them efficiently.
G. Cesari, G. Schildbach, A. Carvalho, and F. Borrelli, “Scenario model predictive control for lane change assistance and autonomous driving on highways”, Intelligent Transportation Systems Magazine, vol. 9, no. 3, pp. 23–35, 2017;
G. Hitz, E. Galceran, M.-E. Garneau, F. Pomerleau, and R. Siegwart, “Adaptive continuous- space informative path planning for online environmental monitoring”,Journal of Field Robotics, 2016.
Revue de presse
Personnes participant au projet
Dernière mise à jour de cette présentation du projet 15.01.2020