Seite - 70 - in Proceedings of the OAGM&ARW Joint Workshop - Vision, Automation and Robotics

cameras, the depth information defined by z-axis can be calculated. The vision sensor in our setup has both stereo cameras fixed in relation to each other looking slightly inwards,with rotationaroundY (vertical) axis. Solving Eq. 1 provides the real-world coordinates X, Y andZ of a point seen by the stereo cameras. Inputs (x1,y1) and (x2,y2) are thepoint coordinates incamera 1 and camera 2 respectively. Variable f is the focal length of the camera and b defines a baseline (dis- tance) between the stereo cameras. Rotation between the cameras aroundY-axis is defined by θ. Z0= b tan(θ) Z= b∗ f x1−x2+ f∗bZ0 X= x1∗Z f Y= y1∗Z f (1) After the charging port is found in the input im- ages, stereo triangulation is used to obtain 3D real- world coordinates of the port position, providing 5 to 7 reference points depending on the charging port type. Using the points, a perspective transformation is calculated using the least squares fit method to obtain the exact position and orientation of the charging port in relation to the vision sensor. Least squares fit for finding the orientation optimises for 3 unknowns (A,B andC), which later are mapped to roll, pitch and yaw angles. The least square error function is defined in Eq. 2, wherex,yand zare coordinates of the reference points. e(A,B,C)=∑(Ax+By+C−z)2 (2) Then, the error function is differentiated and set to zero, as shown in Eq. 3. ∂e ∂A =∑2(Ax+By+C−z)x=0 ∂e ∂B =∑2(Ax+By+C−z)y=0 ∂e ∂C =∑2(Ax+By+C−z)=0 (3) The resulting linear equations with 3 unknowns are solved to get the orientation of the object. This can also be seen as 3D plane fitting to the given points. B. Marker-less Eye-to-HandCalibration In order to operate the vision sensor and the robot in the same coordinate system, eye-to-hand calibration is necessary. The eye-to-hand calibration estimates the transformation between the vision sensor and the robot base. Using this transformation, the position of any object detected by the vision sensor can be recalculated into the coordinate system of the robot, allowing the robot to move to, or avoid that location. Normally, a well structured object, like a checker- board of known size and structure is used in the calibration process. However, it requires mounting it on the end-effector of the robot and can still result in additional offsets. We use the known structure of the connector plug and previously presented shape- based template matching with orientation estimation to obtain the precise pose. Eye-to-hand calibration is based on an automatic calibration procedure for 3D camera-robot systems, which uses the calibration method proposed by Tsai et al [15] [21]. The result of the eye-to-hand calibration are two transformation matrices. The first one defines the position of the vision sensor in relation to the robot base and the second one defines the position of the end point of the connector plug in relation to the end- effector of the robot. The marker-less eye-to-hand calibration can be ben- eficial if the robot is placed on a moving platform, so the relative position between the vision sensor and the robot can change. Furthermore, it would benefit in cases when the robot has interchangeable end-effector attachments with different connector plugs. In both of these cases, recalibration procedure could be done automatically without any reconfiguration. C. RobotMotion Planning Given the limited workspace and all the movements being defined by camera measurements, robot control in Cartesian coordinates was used. TheMoveIt! frame- work, containingmultiplemotionplanningalgorithms, was used for the initial testing [20]. The best perfor- mance in the defined case was demonstrated by the RRT-connect algorithm, which is based on the rapidly exploring random trees [13]. In order to get smoother motion execution and more human-like motions, a velocity based controller was used instead of the standard one provided in ROS. Better performance is achieved by calculating and directly sending speed commands to each of the robot joints, thus reducing theexecutionstart time to50−70 ms compared to around 170 ms using the official ROS UR10 drivers [10]. D. Plugging-In Procedure After the pose of the charging port is calculated, the coordinate system is assigned with the origin placed at the center of the plug and Z-axis looking outwards. Similarly, the coordinate system is assigned to the connector plug, which is held by the robot. The goal of the plug-in procedure is to perfectly align connector plug with the charging port, so the last movement is simply along one axis. In order to achieve that, a 70