Videos

BIMANUAL CABLE MANIPULATION

This video shows the experimental evaluation of the bi-manual cable manipulation algorithm in a laboratory setup at Poznan University of Technology (PUT). Two UR3 arms, equipped with our own de-signed grippers were manipulating the cable in front of the RealSense D435 camera, that was providing the visual feedback. To show the robustness of the proposed algorithm, we performed the manipulation of a few different DLOs (different lengths, weights, stiffness). Also, we tested our approach in demanding situations, including occlusions and self-intersections. 

TACTILE SENSOR DATA INTERPRETATION FOR ESTIMATION OF WIRES FEATURES

The video shows the sorting of wires on the basis of estimated diameter. The diameter estimation is made by using the SVMs based classifier. Initially the correct distribution of the wires among the different diameter area is shown. Then, an operator puts the wires in a random order and with random poses between the gripper fingers. The robot sorts the wires on the table on the basis of an on-line diameter classification. All wires are correctly sorted. The task is repeated by putting the wires between the fingers with a different random sequence. The second sorting is correct anyway. Finally a comparison of initial distribution and the two obtained sorting is reported

PROXIMITY SENSOR FOR THIN WIRE RECOGNITION AND MANIPULATOR 

The video shows an application of the proximity sensor to locate a wire and to estimate its shape. First, the region where the wire is placed is scanned to generate the corresponding point cloud from the sensor measurements. After the scan, the obtained point cloud is processed segment by segment to locate the points with the maximum z-coordinate, which correspond to points of the wire. Then, the x and y coordinates of the selected points are used to obtain a third order polynomial approximating the wire shape by means of a least squares method. Finally, the shape approximation is used to compute the trajectory for the robot end effector in order to follow the real wire for all its length. 

 

 WIRE SHAPE ESTIMATION  

The video shows side by side the tactile signals with the data related to their post processing with the estimated wire shape and the actual wire used during the experiments. Different diameters have been used.

 

This video compares the same data reported in the video above, but in this case superimposed, in order to allow an immediate comparison between the estimated shape and the ground truth.

 

  CABLE REGRASPING BASED ON TACTILE SENSOR 

The video reports an experiment of re-grasp on the basis of tactile data. After a first grasp where the wire shape is estimated, the wire is re-grasped in order to horizontally align the wire with respect to the tactile sensor frame.

 

CABLE GRASPING BASED ON VISION SENSOR  

The video shows the localization of the cable by the vision sensor through the Ariadne software package. The vision sensor provides the grasp pose on the cable to the robot that then execute the grasp. The system has been tested with cables of different size and colours and with different background and light conditions

 

TEACHING BY DEMOSTRATION

This video shows a teaching process of cable routing trajectories. Trajectory and relevant poses (grasping, release...) are stored for further execution.

TEST OF GRASPING

This video shows the preliminary test of the grasping phase of different wires (referred to section) and the sequential introduction in tray, using the OnRobot gripper with the designed fingers