Student Communications Engineering or similar (f/m/x) - Navigation algorithm design for future swarm missions

Location

Weßling | Germany

Job description

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Course paper / final thesis

Navigation algorithm design for future swarm missions

Robotic swarm exploring gas sources in a conceptual Mars exploration mission (Credits: DLR)

Starting date

1 March 2024

Duration of contract

up to 6 months

Remuneration

up to German TVöD 5

Type of employment

Full-time

A swarm of autonomous rovers can rapidly explore a vast extraterrestrial area. Compared to a single rover, a swarm can make simultaneous observations at different locations and avoids a single point of failure, which leads to a paradigm shift in future space missions. The movement strategy of rovers, assigned to autonomous exploration missions, are constrained by multiple objectives. Of course, the high-level goal is to collect information about the environment to be explored. Besides, the swarm needs to estimate the locations of itself and numerous objects, without the support of an external navigation system like GNSS. Further, the rovers’ trajectories are constrained by terrain conditions. The swarm’s unique capability of formation optimization has to account for all these objectives (and more), in order to successfully accomplish the exploration mission.

Formation control of a large swarm remained an open problem due to its high degree of freedom. In this thesis a framework for multi-objective swarm formation control should be develop, implemented and tested in simulations. The resulting algorithm is preferably decentralized, scalable, robust, and with low complexity and high precision. To this end, modern methods from the field of artificial intelligence like reinforcement learning may be applied.

Within this master thesis you are expected to contribute in:

• Understanding multi-objective optimization and multi-agent learning
• Designing decentralized formation optimization algorithms, potentially with artificial intelligence (AI), to account for multiple objectives in an exploration

• excellent knowledge of estimation, tracking, machine learning
• strong mathematics/signal-processing background
• excellent programming skills in MATLAB, Python etc.
• self-motivated working
• a prerequisite for employment at DLR is security clearance in accordance with the German Security Clearance Act (SÜG) and the willingness to undergo a security clearance in accordance with §8 ff SÜG
• experience in machine learning technics like deep reinforcement learning is an advantage

Look forward to a fulfilling job with an employer who appreciates your commitment and supports your personal and professional development. Our unique infrastructure offers you a working environment in which you have unparalleled scope to develop your creative ideas and accomplish your professional objectives. Our human resources policy places great value on a healthy family and work-life-balance as well as equal opportunities for persons of all genders (f/m/x). Individuals with disabilities will be given preferential consideration in the event their qualifications are equivalent to those of other candidates.

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