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You will be part of the Robotics Research Group at the Department of Mechanical Engineering, Division of Production engineering, Machine design and Automation (PMA).
The group has pioneered robotics research in Europe since the mid-1970s and was among the first to develop active force feedback for assembly operations. Already in 1980 it developed learning insertion algorithms based on stochastic automata. It has covered virtually all aspects of sensor-based robotics, from the high-level task specification down to low-level sensor-based control, and applied the research results in a variety of industrial applications. In the last decade the group shifted its attention towards service robots (behaviour-based mobile manipulation, shared control, learning control), medical robotics (natural interfaces, haptic bilateral control), industrial robot assistants, and active sensing. PMA has created several spin-off companies that are active in robotics-related activities, has initiated several free and open-source software projects in robotics (Orocos, KDL, iTaSC, eTaSL, …), and has participated in a large number of EU projects in robotics, mostly oriented towards control and software development, with a focus on model-driven engineering techniques. More information is available through the link below.
The selected candidate will be involved in the HYSLAM project, a four-year strategic research project. The project focuses on adding semantics to SLAM in order to cope with dynamic environments and to improve reliability to a level ready for industrial uptake. The project will achieve this goal through developing mechanisms that continuously update maps and by making the navigation more robust to changes in the environment. To this end, semantic information will be included in the map representation of the environment, which can then be used to decide which objects in the environment are likely to change and which ones are not. The developed algorithms will be validated on two use cases: one use case in which a mobile robot navigates in an industrial indoor environment, and one use case in which a drone flies in an outdoor environment. For both use cases, a simulation framework and an experimental platform will be developed.
For this vacancy, the selected candidate will focus on the models and underlying data structures for the semantic maps as well as the tooling to efficiently work with them. This includes working on graph-based databases and algorithms to represent domain-specific knowledge about the robots, their sensors, the environment, the task, and relations between them. The use cases of the project are defining the scope for these domain-specific models. The candidate will then develop tooling that supports project partners but also robots in creating, modifying, and querying knowledge in these semantic world models. Since such queries will require the execution of algorithms on the graph structures, the final task of the candidate is to find efficient algorithms and to deploy them in the developed infrastructure, e.g., in the form of microservices in a database.
A successful candidate has obtained a MSc degree in engineering related to Robotics or Computer Science, and has a background and interest to contribute to
Software development will be part of your core research. The following elements will be considered in the evaluation:
In your motivation letter or extended CV description, please consider to mention your previous experiences and skills which may help to make relevant contributions to the project.
The selected candidate is furthermore expected to:
Hands-on experience with databases, robot platforms, or sensor systems (cameras, Lidar,...) are a plus.
The successful candidate will receive:
A start date in the course of 2019 is to be agreed upon.