When we
decided to participate in the Amazon Picking Challenge, the first decision was
to a) gather the best team possible, b) design the best possible system, to
win. We would choose the best technologies and components available to build a reliable
and efficient solution, as industry requires.
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| Team Delft has gathered academic researchers and industry professionals, but overall passionate roboticists! |
To make the
baseline design decisions for our solution we analyzed the application
requirements, studied the results from the previous edition of APC, and
considered the expertise we had in our team. We aimed for a fast and robust
system, capable of detecting failed picks and try them again. For example, instead
of complex maneuvers to reach occluded products, our robot would simply
move blocking objects to other bins.
Our solution
was based on the sense-plan-act paradigm. This allows for optimal motions at
the price of more precise sensing information. The loop for the Picking
challenge proceeds as follows. First the order of the picks is decided to
maximize final scoring and guarantee the fastest first pick possible (this
proved key to win!). Then, for each item to pick the robot moves to scan the
bin with the first target item with the 3D camera to: a) locate it, b) get the
collision information to move inside the bin. Then the grasp planner computes
the grasping strategy and grasp pose, and a motion plan is generated to approach,
and retreat from the bin with the item. Following the motion is executed,
including gripper configuration and activation on the way. State is checked to
confirm a successful pick. If so, the robot moves to place the item in the
tote, using a simple logic to drop the item in pre-defined locations without
crashing fragile products. The flow for the Stowing Challenge is similar.
For the
control software we used ROS-Industrial. We had extensive experience in the
team using ROS-I for industrial applications. The use of ROS proved mandatory.
ROS allowed us to quickly adapt, test and integrate extant software components,
and develop the new ones that we needed (e.g. a grasp planner for our hybrid gripper,
or the application executive/coordination component).
We describe
below our basic design approach and core decisions for each general issue n the
APC challenge: robot, perception, grasping and gripper, motion and task planning.
In following posts we will cover more details of our robotic system in each
specific area.
Robot
After
preliminary analysis, we concluded that maximum reachability in the
workspace was a critical requirement for the competition. We chose
a configuration with a SIA20F Motoman robot arm by Yaskawa mounted on a
rail. The total 8 degrees of freedom allowed the system to reach all the bins
with enough maneuverability to pick the objects from any location inside.
Vision
We decided to
use two industrial stereo camera's with rgb overlay camera's. One camera for
detection of objects in the tote and one on the robot gripper to scan the bins.
Detection went in two steps. For object recognition we used a deep learning
neural network based on Faster RCNN. Next we performed localization using an
implementation of super4PCS to do a global optimization of the pose estimation.
After this the estimation is refined using ICP.
Grasping and gripper
We developed a
hybrid gripper customized to handle all the 39 products in the
competition. It incorporates both suction, with 1dof for more grasping
flexibility, and a pinch mechanism for the products difficult to suck. We designed
an algorithm that auto generates grasp locations on the objects based on their
position, geometry and environment constraints. Object-specific heuristics
were also included after testing.
Motion
To make the
robot motions as fast as possible, yet accurate enough for our sense-plan-act
approach, we divided the problem into the motions outside the shelf and inside its
bins. Outside there is no need for dynamic path planning: the environment is
static and known a priori. We created a database of collision-free trajectories
between the relevant fixed locations, e.g. image-capture and approach locations
in front of the shelf’s bin and over the tote. To move inside the bins we
simplified the dynamic path planning problem by using cartesian trajectories.
To implement our solution we developed customized ROS-services on top of the
ROS framework for motion planning MoveIt!
Task planning and coordination
To control the
execution of our sense-plan-act overarching loop we used a state machine approach implemented with ROS-Smach.
We implemented
a dynamic task-planning to decide in which order to pick the items and where to
place them. It coded the competition scoring rules in logic rules, averaged
with heuristics for the difficulty of picking the different items, also
depending on the bin cluttering.
More details about Team Delft robot coming soon!
