Fully autonomous exploration and mobile manipulation in rough terrain are still beyond the state of the art—robotics challenges and competitions are held to facilitate and benchmark research in this direction. One example is the 2013 DLR SpaceBot Cup, for which we developed an integrated robot system to semiautonomously perform planetary exploration and manipulation tasks. Our robot explores, maps, and navigates in previously unknown, uneven terrain using a three‐dimensional laser scanner and an omnidirectional RGB‐D camera. We developed manipulation capabilities for object retrieval and pick‐and‐place tasks. Many parts of the mission can be performed autonomously. In addition, we developed teleoperation interfaces on different levels of shared autonomy, which allow for specifying missions, monitoring mission progress, and on‐the‐fly reconfiguration. To handle network communication interruptions and latencies between robot and operator station, we implemented a robust network layer for the middleware Robot Operating System (ROS). The integrated system has been demonstrated at the 2013 DLR SpaceBot Cup. In addition, we conducted systematic experiments to evaluate the performance of our approaches.

ABSTRACT:Reliable obstacle avoidance is a key to navigating with UAVs in the close vicinity of static and dynamic obstacles. Wheel-based mobile robots are often equipped with 2D or 3D laser range finders that cover the 2D workspace sufficiently accurate and at a high rate. Micro UAV platforms operate in a 3D environment, but the restricted payload prohibits the use of fast state-of-the-art 3D sensors. Thus, perception of small obstacles is often only possible in the vicinity of the UAV and a fast collision avoidance system is necessary. We propose a reactive collision avoidance system based on artificial potential fields, that takes the special dynamics of UAVs into account by predicting the influence of obstacles on the estimated trajectory in the near future using a learned motion model. Experimental evaluation shows that the prediction leads to smoother trajectories and allows to navigate collision-free through passageways.

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Mapping and real-time localization are prereq uisites for autonomous robot navigation. They also facilitate situation awareness of remote operators in exploration or rescue missions. In this paper, we propose methods for efficient 3D mapping of environments and for tracking in real-time the 6D movement of autonomous robots using a continuously rotating 3D laser scanner. Multi-resolution surfel representations allow for compact storage and efficient registration of local maps. Real time pose tracking is performed by a particle filter based on individual laser scan lines. We evaluate our approach using both data generated in simulation and measurements from challenging real environments.