  In this paper we present our hardware design and control approaches for a
mobile manipulation platform used in Challenge 2 of the MBZIRC 2020
competition. In this challenge, a team of UAVs and a single UGV collaborate in
an autonomous, wall-building scenario, motivated by construction automation and
large-scale robotic 3D printing. The robots must be able, autonomously, to
detect, manipulate, and transport bricks in an unstructured, outdoor
environment. Our control approach is based on a state machine that dictates
which controllers are active at each stage of the Challenge. In the first stage
our UGV uses visual servoing and local controllers to approach the target
object without considering its orientation. The second stage consists of
detecting the object's global pose using OpenCV-based processing of RGB-D image
and point-cloud data, and calculating an alignment goal within a global map.
The map is built with Google Cartographer and is based on onboard LIDAR, IMU,
and GPS data. Motion control in the second stage is realized using the ROS Move
Base package with Time-Elastic Band trajectory optimization. Visual servo
algorithms guide the vehicle in local object-approach movement and the arm in
manipulating bricks. To ensure a stable grasp of the brick's magnetic patch, we
developed a passively-compliant, electromagnetic gripper with tactile feedback.
Our fully-autonomous UGV performed well in Challenge 2 and in post-competition
evaluations of its brick pick-and-place algorithms.
