  In this paper, we propose a novel approach to wheeled robot navigation
through an environment with movable obstacles. A robot exploits knowledge about
different obstacle classes and selects the minimally invasive action to perform
to clear the path. We trained a convolutional neural network (CNN), so the
robot can classify an RGB-D image and decide whether to push a blocking object
and which force to apply. After known objects are segmented, they are being
projected to a cost-map, and a robot calculates an optimal path to the goal. If
the blocking objects are allowed to be moved, a robot drives through them while
pushing them away. We implemented our algorithm in ROS, and an extensive set of
simulations showed that the robot successfully overcomes the blocked regions.
Our approach allows a robot to successfully build a path through regions, where
it would have stuck with traditional path-planning techniques.
