TU Delft

PhD: Learning of socially compliant motion planning for autonomous vehicles

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Faculty Mechanical, Maritime and Materials Engineering

The 3mE Faculty trains committed engineering students, PhD candidates and post-doctoral researchers in groundbreaking scientific research in the fields of mechanical, maritime and materials engineering. 3mE is the epitome of a dynamic, innovative faculty, with a European scope that contributes demonstrable economic and social benefits.

The main focus of the Cognitive Robotics department is the development of intelligent robots and vehicles that will advance mobility, productivity and quality of life. Our mission is to bring robotic solutions to human-inhabited environments, focusing on research in the areas of machine perception, motion planning and control, machine learning, automatic control and physical interaction of intelligent machines with humans. We combine fundamental research with work on physical demonstrators in areas such as self-driving vehicles, collaborative industrial robots, mobile manipulators and haptic interfaces. Strong collaborations exist with cross-faculty institutes TU Delft Robotics Institute and TU Delft Transport Institute), our national robotic ecosystem (RoboValley, Holland Robotics) and international industry and academia.

Functie omschrijving

We are looking for an ambitious PhD candidate who would like to develop novel methods for safe and socially compliant autonomous navigation in crowded urban canals, with a combination of machine learning (learning from historical data, reinforcement learning) and trajectory optimization approaches.

You will join a team of researchers within the context of the project "Sustainable Transportation and Logistics over Water: Electrification, Automation and Optimization (TRiLOGy)" funded by the Dutch Research Council (NWO). In this project, we will investigate (i) fleet management decisions at the high level (1 PhD position supervised by Assist. Prof. B. Atasoy) and (ii) autonomous navigation methodologies for autonomous vessels in urban canals (1 PhD position supervised by Assist. Prof. J. Alonso-Mora). You will be responsible for the latter, autonomous navigation.

The objective of the autonomous navigation part is to develop autonomy tools for navigation in inland waterways, among other manned and unmanned vessels. The main challenge to ensure safe and efficient navigation of autonomous vessels in urban waters is that of generating safe trajectories that (i) take into account the complex dynamics of the vessel, (ii) coordinate with other traffic participants and (iii) show socially-compliant behavior based on past experiece and historical data. In TRiLOGy we will rely on historical data from manned vessels and machine learning strategies (supervised learning, reinforcement learning, multi-agent reinforcement learning) to improve the performance of the motion planning system (trajectory optimization) and produce feasible human-like motions for the autonomous vessel. The developed motion planners will closely interact with the perception modules of the autonomous vessel. A typical scenario is that of crowded canals and intersections, where efficient navigation can be achieved with tight coordination among the interacting participants.

The autonomous navigation methods that will be developed in this project will be tested and verified through their application to autonomous vessels in the ResearchLab Autonomous Shipping (RAS). You will also interact with our industrial partners (Zoev City, Municipality of Amsterdam, Flying Fish and DEMCON Unmanned Systems), with the Amsterdam Institute of Advanced Metropolitan Solutions (AMS) and with MIT researchers working on the AMS Roboat project.

The PhD candidate will be embedded within the Autonomous Multi-robots Lab of the Department Cognitive Robocs at TU Delft. For more information of our ongoing research see https://www.autonomousrobots.nl.

Functie eisen

The candidate has a very good MSc degree in Robotics, Computer Science, Systems and Control, Electrical/Mechanical Engineering, Applied Mathematics, or a related field. The candidate must have strong analytical skills and must be able to work at the intersection of several research domains. Good programming skills and experience with Python/C++ and ROS are of foremost importance to implement the learning methods and the proposed designs on real ASVs. A very good command of the English language is required, as well as excellent communication skills. Candidates having exhibited their ability to perform research in machine learning, control, optimization, perception and/or robotics are especially encouraged to apply.

Arbeidsvoorwaarden

TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.

As a PhD candidate you will be enrolled in the TU Delft Graduate School. TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit www.tudelft.nl/phd for more information.

Informeren en solliciteren

If you have specific questions about this position, please contact Assist. Prof. J. Alonso-Mora (j.alonsomora@tudelft.nl, +31 152785489) Always specify the vacancy number in the email subject. Please do not send application emails to these email addresses but use the specified address (application-3mE@tudelft.nl).

To apply, please send via e-mail:

• a letter of motivation explaining why you are the right candidate for this project,

• a detailed CV,

• a complete record of Bachelor and Master courses (including grades),

• your Master Thesis (at least as draft),

• any publications, and a list of projects you have worked on with brief descriptions of your contributions (max 2 pages),

• the names and contact addresses of two or three references.

All these items should be combined in one PDF document. Applications should be submitted at the earliest convenience to application-3mE@tudelft.nl. When applying for this position, always refer to the vacancy number 3mE20-44. The review of applications will start on June 1st and continue until the position is filled. The intended starting date is fall 2020 (flexible).

Acquisitie naar aanleiding van deze vacature wordt niet op prijs gesteld.

Uren per week: 36-40 uur per week

Salaris: € 2.325,- tot € 2.972,- per maand

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