# A Practical Approach for Picking Items in an Online Shopping Warehouse
## 7 Conclusions  
Although the problem of grasp planning has been around in the robotics community for a long time [1], the existing solutions tend to be limited when it comes to deploying them in realistic scenarios. Our main contribution here has been to try and apply previous knowledge together with our own methods in a realistic scenario as proposed by Amazon for its warehouses. The hardware employed is very cost effective: from the Baxter robot itself to the in-house made suction-cup end-effector and the 3D-printed Pincher gripper. This adds extra limitations on the precision and repeatability of the system. The UJI RobinLab team took part in the ARC17 in order to automate this warehouse environment. One of the necessary accomplishments for the robot is to be able to grasp an object, which requires a software approach that matches the robotic hardware. Based on our experience, we present here a practical solution to the problem of robot picking in this unstructured, realistic scenario by using an approach based on two algorithms that takes a point cloud as an input and outputs a position and orientation of the gripper given the hardware specifications and restrictions. Two state-of-the-art algorithms for two-finger grippers were analyzed since they both use a point cloud as an input, and a grasp position and orientation as output. We have implemented and systematically studied them in terms of computation time, robustness and success rate. It was concluded that AGILE was less robust and took longer than HAF. The latter gave fewer grasp options with only vertical grasps, but showed better \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test \#** & **Successful Items** & **Known Items (\%)** & **Unknown Item (\%)** & **Time (min:s)** & **Amazon Score** \\ \hline
1 & 9/10 & 100 & 83 & 8:56 & 135 \\
2 & 7/10 & 66 & 75 & 14:29 & 80 \\
3 & 8/10 & 66 & 100 & 8:40 & 105 \\
4 & 8/10 & 75 & 83 & 8:27 & 95 \\
5 & 8/10 & 83 & 75 & 11:18 & 90 \\
6 & 7/10 & 50 & 100 & 11:11 & 110 \\
7 & 9/10 & 100 & 75 & 10:15 & 100 \\
8 & 6/10 & 60 & 60 & 14:41 & 75 \\ \hline
**Averages** & **7.75/10** & **75** & **81** & **11:00** & **98** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary data of conducted tests. robustness and computational speed. Given the results, it was decided that AGILE did not add significant contribution to the task and was discarded for the system. Another contribution of the work is the creation and testing of the centroid normals approach (CNA) algorithm. It uses the point cloud and the major graspable component of the object in order to find the centroid and its normals in the flattest part of the point cloud of the object. Then, grasps are rotated around the vertical z-axis in such a way that the final grasp is most comfortable for the [PERSON] robot. This approach was the most successful in combination with a two-finger gripper and a vacuum gripper. The final contribution was to match the competition objects to the grasping techniques and grippers to maximize the number of grasped items, as shown in Table 3. This table exposes the analysis that one universal algorithm for one universal gripper has not been found and currently the best solution is to use various algorithms for various grippers. This is supported by the fact that even though our overall success rates of 69% and 77.5% for our two sets of tests, respectively, may appear to not be high enough, our detailed failure analysis provides important clues about the reasons why the system fails, which very often are unrelated to the grasping algorithms themselves (object texture, imprecision of the control and/or point cloud, kinematic restrictions, etc.). We believe that our results will provide a useful addition to the literature in the field towards the deployment of practical working systems. Conceptualization, A.M.; Methodology, N.N. ; Project administration, A.P.d.P. ; Supervision, A.M. and A.P.d.P. ; Validation, E.C. ; Writing--original draft, N.N. ; Writing--review and editing, A.M., E.C. and A.P.d.P.