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Today satellites are mostly monolithic systems and offer hardly any facilities to facilitate servicing or maintenance. Consequently, no servicer satellites are present as well. In this work, supported by the German Space Agency, we propose a modular concept for satellites based on standardized building blocks. These can be replaced in orbit requiring significantly less manipulation skills than ordinary repair missions would need. The building blocks could be replaced using robot satellites instead of sending astronauts on maintenance missions as has been performed few times in the past. The possible benefits are numerous: the concept enables cheaper and faster development of satellites, it enables repair missions extending the life expectancy of satellites by replacing damaged blocks or those run out of fuel, and finally old satellites can be refitted for new missions, reducing space debris and the cost of launching new systems. Developing our concept, we faced the same challenges known from modular robots on earth: interfaces had to be developed for connecting blocks, a distributed software architectures was needed, and algorithms were necessary which calculate suitable configurations of blocks according to given constraints. In this paper we will present solutions from which a concept of a modular satellite system emerges which is strongly inspired by earthbound heterogeneous robotic systems. We will complete the paper with thoughts on the servicing itself and on setting up maintenance infrastructures in the earth orbit.

This paper proposes a SLAM algorithm based on FastSLAM 2.0 that maps features representing regions with a semantic type, topological properties, and an approximative geometric extent. The resulting maps enable spatial reasoning on a semantic level and provide abstract information allowing efficient semantic planning and a convenient interface for human-machine interaction. We present novel region features and an algorithm for estimating the feature parameters from uncertain measurements. In particular, we provide a means of estimating parameters even if the region feature is considerably larger than the robot's sensor range. Finally, we adapt the FastSLAM 2.0 algorithm to map the proposed features and show simulation-based results illustrating the capabilities of the proposed algorithm.

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