  Selectively picking a target fruit surrounded by obstacles is one of the
major challenges for fruit harvesting robots. Different from traditional
obstacle avoidance methods, this paper presents an active obstacle separation
strategy that combines push and drag motions. The separation motion and
trajectory are generated based on the 3D visual perception of the obstacle
information around the target. A linear push is used to clear the obstacles
from the area below the target, while a zig-zag push that contains several
linear motions is proposed to push aside more dense obstacles. The zig-zag push
can generate multi-directional pushes and the side-to-side motion can break the
static contact force between the target and obstacles, thus helping the gripper
to receive a target in more complex situations. Moreover, we propose a novel
drag operation to address the issue of mis-capturing obstacles located above
the target, in which the gripper drags the target to a place with fewer
obstacles and then pushes back to move the obstacles aside for further
detachment. Furthermore, an image processing pipeline consisting of color
thresholding, object detection using deep learning and point cloud operation,
is developed to implement the proposed method on a harvesting robot. Field
tests show that the proposed method can improve the picking performance
substantially. This method helps to enable complex clusters of fruits to be
harvested with a higher success rate than conventional methods.
