LunarCobot

The reference mission planned for LunarCobot is intended to provide robotic assistance in analyzing sites of interest as a precursor to the arrival of crewed missions. Such missions are inherently demanding and may require different capabilities that, due to system limitations, will not always be available on the same system. For this purpose, a heterogeneous robot team consisting of different robots with diverse capabilities can be deployed.

The DFKI’s focus here is on the further development of subcomponents for spaceflight that can be used for such cooperation between robotic systems. One area of focus is the further development of a scalable quasi-direct-drive motor for space applications, along with the associated motor electronics. Such a highly dynamic motor can be used for legged robots as a particularly flexible component of a robot team, or for a dynamic manipulator arm as a functional component of a robot for tooling tasks.

Another area is the further development and TRL advancement of a multifunctional interface for planetary applications. This interface can connect modules and components, transmit electrical power, and enable data exchange. This allows for simplified integration and reconfiguration of systems while simultaneously increasing their flexibility and adaptability to changing missions. 

Another focus of LunarCobot is hybrid tether technology, which enables data and power transmission over longer distances and is being developed by the project partner, the University of Stuttgart. This is particularly important for powering systems in permanently shaded areas. One example of the implementation of these technologies is the Nanokhod microrover, developed by the partner. Other mobile rovers deployed by DFKI—such as Coyote III and the Hunter SE—which are capable of traversing greater distances and performing tasks such as manipulation, together with Nanokhod, form a network of heterogeneous systems. Using the developed multifunctional interface, the flexibility and potential implementation of various mission scenarios can be demonstrated here.

Mapping of the newly explored regions, particularly the shaded areas, is enabled by the sensors and the environmental representation software on board the rover. Based on these maps, regions of particular interest can be selected for close-range inspection.

To improve collaboration within the deployed robotic systems as a team, contextual learning is to be integrated into the task negotiation and coordination processes. Hardware states, environmental conditions, and team composition can be encoded as dynamic contexts. In this way, the robots can adapt in real time to (re)configurations.