Navigation Algorithm Design for Future Swarm Missions (w/m/d)

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: