Teams of mobile robots will play a crucial role in future missions to explore the surfaces of extraterrestrial bodies. Setting up infrastructure and taking scientific samples are expensive tasks when operating in distant, challenging, and unknown environments. In contrast to current single-robot space missions, future heterogeneous robotic teams will increase efficiency via enhanced autonomy and parallelization, improve robustness via functional redundancy, as well as benefit from complementary capabilities of the individual robots. In this article, we present our heterogeneous robotic team, consisting of flying and driving robots that we plan to deploy on scientific sampling demonstration missions at a Moon-analogue site on Mt. Etna, Sicily, Italy in 2021 as part of the ARCHES project. We describe the robots' individual capabilities and their roles in two mission scenarios. We then present components and experiments on important tasks therein: automated task planning, high-level mission control, spectral rock analysis, radio-based localization, collaborative multi-robot 6D SLAM in Moon-analogue and Marslike scenarios, and demonstrations of autonomous sample return.

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Robotic tasks are becoming increasingly complex, and with this also the robotic systems. This requires new tools to manage this complexity and to orchestrate the systems to fulfill demanding autonomous tasks. For this purpose, we developed a new graphical tool targeting at the creation and execution of robotic tasks, called RAFCON. These tasks are described in hierarchical state machines supporting concurrency. A formal notation of this concept is given. The tool provides many debugging mechanisms and a GUI with a graphical editor, allowing for intuitive visual programming and fast prototyping. The application of RAFCON for an autonomous mobile robot in the SpaceBotCamp competition has already proved to be successful.

Many future application scenarios of robotics envision robotic agents to be in close physical interaction with humans: On the factory floor, robotic agents shall support their human co-workers with the dull and health threatening parts of their jobs. In their homes, robotic agents shall enable people to stay independent, even if they have disabilities that require physical help in their daily life-a pressing need for our aging societies. A key requirement for such robotic agents is that they are safety-aware, that is, that they know when actions may hurt or threaten humans and actively refrain from performing them. Safe robot control systems are a current research focus in control theory. The control system designs, however, are a bit paranoid: programmers build "software fences" around people, effectively preventing physical interactions. To physically interact in a competent manner robotic agents have to reason about the task context, the human, and her intentions. In this paper, we propose to extend cognition-enabled robot control by introducing humans, physical interaction events, and safe movements as first class objects into the plan language. We show the power of the safety-aware control approach in a real-world scenario with a leading-edge autonomous manipulation platform. Finally, we share our experimental recordings through an online knowledge processing system, and invite the reader to explore the data with queries based on the concepts discussed in this paper.

In this paper we present an experimental study on real-time collision avoidance with potential fields that are based on 3D point cloud data and processed on the Graphics Processing Unit (GPU). The virtual forces from the potential fields serve two purposes. First, they are used for changing the reference trajectory, second they are projected to and applied on torque control level for generating according nullspace behavior together with a Cartesian impedance main control loop. The GPU algorithm creates a map representation that is quickly accessible. I addition outliers and the robot structure are efficiently removed from the data, and the resolution of the representation can be easily adjusted. Based on the 3D robot representation and the remaining 3D environment data, the virtual forces that are fed to the trajectory planning and torque controller are calculated. The algorithm is experimentally verified with a 7-Degree of Freedom (DoF) torque controlled KUKA/DLR Lightweight Robot for static and dynamic environmental conditions. To the authors knowledge, this is the first time that collision avoidance is demonstrated in real-time on a real robot using parallel GPU processing.