Authors:
Marcondes Silva Júnior
1
;
Jonas Silva
1
and
João Teixeira
2
Affiliations:
1
Informatics Center, Universidade Federal de Pernambuco, Recife, Brazil
;
2
Electronics and Systems Department, Universidade Federal de Pernambuco, Recife, Brazil
Keyword(s):
Robot Manipulation, Computer Vision, RGB-D Camera, Brazilian 2022’s Election, Integrity Testing.
Abstract:
The Brazilian electoral system uses the electronic ballot box to increase the security of the vote and the speed of counting the votes. It is subjected to several security tests, and the one that has the most human interaction and personnel involved is the integrity test. Our macro project proposed a solution to optimize the testing process and reduce the amount of human beings involved, using a robotic arm with the aid of computer vision to optimize the personal demand from 8 people to 2. However, in order to use the robot, technical knowledge was still required, and it could not be used by any user, as it was necessary to manually map the keys to the places where the robotic arm would press to perform the test. We present a solution for automatically mapping a workspace to a robotic arm. Using an RGB-D camera and computer vision techniques with deep learning, we can move the robotic arm with 6 Degrees of Freedom (DoF) through Cartesian actions within a workspace. For this, we use a
YOLO network, mapping of a robot workspace, and a correlation of 3D points from the camera to the robot workspace coordinates. Based on the tests carried out, the results show that we were able to map the points of interest with high precision and trace a path plan for the robot to reach them. The solution was then applied in a real test scenario during the first round of Brazillian elections of 2022, and the obtained results were compatible to the conventional non-assisted approach.
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