PhD Position in Online Reconfiguration and Coordination of Robot Systems in an Assembly Plant

Catholic University of Leuven

Belgium

 
For the Department of Mechanical Engineering, Robotics, Automation and Mechatronics (RAM) Division, we are looking for a doctoral researcher with a strong interest in distributed task scheduling, resource allocation and programming of robot systems. The focus is on (i) the online reconfiguration of all robots involved in a mission, and (ii) the coordination of the autonomous decision making over the participating robots. In other words, the "flock" of robots should be able to make progress without permanent connection to the assembly plant's "central" scheduling and dispatching. But the introduction of an assembly cell-centric "task scheduling holon" is an option. You will be part of the Robotics Research Group at the Department of Mechanical Engineering. 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. The Department 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 more recently RobMoSys), 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.

Project

This PhD project will be part of a larger Flanders Make SBO project, AssemblyRecon, that tackles current issues with reconfigurable assembly system (RAS) configurations. They typically result in a suboptimal space and capital expenditure utilization as (1) they are not continuously operating at full potential (e.g. volume changes due to product ramp-up, market fluctuations or end-of-life) and (2) they need to be re-engineered drastically and frequently (e.g. new products or volume changes). AssemblyRecon will focus on Reconfigurable Assembly Systems and tackle the following barriers, which hamper their industrial adoption:

  • Lack of a decision framework to decide on assembly plant reconfiguration at three levels: workstation level (1 day to 1 month), system architecture management level (1 day to 1 month) and on-line task execution level (1 to 10 seconds). The former two respond to production changes, such as product mix, variants or volume, while the latter responds to incoming assembly orders and possible execution disturbances, such as stock shortage, rush orders, quality issues and breakdowns.
  • Lack of a proof of concept in a relevant environment showcasing the potential of RAS in terms of optimization of capex & space utilization enabled by a novel decision framework and well-established technologies (regarding flexible flow systems, inline kitting and picking systems, industrial machine to machine communication protocols and new design concepts for flexible workstations).

In order to do so, the project will deliver both an Assembly Configuration Recommender (ACR) and an Assembly Execution System (AES), which will closely interact. The ACR will propose reconfiguration based on external triggers extracted from production changes using a two-step approach. In a first step initial configurations will be proposed based on historic data and configurations. To this end, a graph-based database approach will be realized, containing a.o. models describing the assembly system characteristics and capabilities. The ACR can call upon six different optimization modules in two categories (workstation and system architecture) to compute optimal assembly system configurations, applying (meta-) heuristic approaches smartly combined with generic local search engines. The AES will follow a distributed approach in which incoming assembly orders are translated in dynamic task execution commands for the respective hardware modules. Continuously, each module will indicate its availability and status. As each module is owner of its own time slots, fast local optimizations are possible in case of disturbances, such as breakdowns or stock shortages. The AES will continuously update stochastic models to estimate performance, such as task execution times, reduce uncertainty and predict the need for global reconfiguration requests towards the ACR. This PhD project will focus on the Assembly Execution System (AES) and, thus, the selected candidate will also have to closely work together with researchers developing the ACR.

Important research targets in this PhD project are (1) semantic modelling of task execution in an assembly context; (2) resource allocation (scheduling) based on the higher-level decisions (selected layout design, workstation and transportation options); (3) coping with external (e.g. stock shortage, rushorders, …) and internal (e.g. hardware failures, assembly failures, delays, …) disturbances during assembly execution, and design probabilistic models for performance estimation and prediction to improve the robustness of the AES to such disturbances.

Profile

A successful candidate has obtained a MSc degree in engineering (Mechanical, Mechatronics, Operations, Electrical, Computer Science) related to Robotics or Operations Engineering and has a strong background and interest to contribute to:

  • distributed task scheduling and resource allocation of assembly execution systems
  • embedded control systems, software engineering for robotics

Contributions to free and open source software projects (also beyond the topic of the project!) and hands-on experience with robot platforms and sensor systems (vision, force …) are both a plus. If applicable, please list them clearly in your application or send us your portfolio.

Please, refrain from sending in your file if your ambition is to tackle the problem with big data and/or learning approaches, because this project emphasizes explicit formal knowledge representation and reasoning.

Similarly, if you think ROS is the (magic) solution, you'd better not apply.

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:

  • be an independent, out of the box thinker, but a team player at the same time
  • have a very good knowledge of English (spoken and written)
  • be able to work independently, accurately and methodically
  • present research findings at national and international conferences
  • publish research findings in international journals

Offer

The successful candidate will receive:

  • a fully funded doctoral scholarship for one year, renewable for a total duration of up to four years
  • multiple benefits (health insurance, access to university infrastructure and sports facilities, etc.)
  • the opportunity to participate in research collaborations and international conferences

A start date in the course of 2020, preferably in or before September 2020, is to be agreed upon.

Interested?

Please use the online application tool to submit your application. Include:

- an academic cv with photo

- a pdf of your diplomas and transcript of course work and grades

- statement of research interests and career goals (max. 2 pages)

- sample of technical writing (publication or thesis)

- contact details of at least two referees

Deadline: June 15, 2020. Note: the position might be filled in earlier if an excellent candidate is found!

For applications, please use the online application tool. For more information, send an e-mail to rob.hr@kuleuven.be 

You can apply for this job no later than June 15, 2020 via the
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.
  • Employment percentage: Voltijds
  • Location: Leuven
  • Apply before: June 15, 2020
  • Tags: Ingenieurswetenschappen


In your application, please refer to Polytechnicpositions.com

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