Method for positioning a limp, flat workpiece and positioning apparatus

21:

• The method according to claim 20, wherein the flat workpiece is gripped in an automated manner by the first gripper and the second gripper, the first gripper gripping at the first gripping point and the second gripper gripping at the second gripping point.

22:

• The method according to claim 20, wherein
• the flat workpiece is held by the second gripper and the first gripper is moved along the gripped edge until the first gripper grips a first corner of the flat workpiece, and/or
• the flat workpiece is held by the first gripper and the second gripper is moved along the gripped edge until the second gripper grips a second corner of the flat workpiece.

23:

• The method according to claim 22, wherein for moving along with the first gripper, a gripping force of the first gripper is reduced in comparison to a gripping force of the second gripper, or the first gripper is opened by a specified amount.

24:

• The method according to claim 22, wherein for moving along with the second gripper, a gripping force of the second gripper is reduced in comparison to a gripping force of the first gripper, or the second gripper is opened by a specified amount.

25:

• The method according to claim 21, wherein the flat workpiece is deposited in a flat state by the first gripper and the second gripper.

26:

• The method according to claim 20, wherein prior to generating the camera image, an area of contact of the flat workpiece with the manipulation surface is enlarged starting from the random state.

27:

• The method according to claim 20, wherein prior to identifying the grippable edge, a type of the flat workpiece is identified by a third machine learning module of the control unit.

28:

• The method according to claim 27, wherein the third machine learning module includes a trained artificial neural network configured to receive the camera image or partial images of the camera image for extracting characteristic image features.

29:

• The method according to claim 20, wherein the first machine learning module includes a trained artificial neural network configured to receive the camera image or partial images of the camera image for extracting characteristic image features.

30:

• The method according to claim 20, wherein for identifying the grippable edge, the camera image is subdivided into a plurality of partial images, and characteristic image features are extracted by the first machine learning module of the control unit for each partial image and, based on the characteristic image features, for each partial image a probability is established with which it shows an edge of the flat workpiece.

31:

• The method according to claim 20, wherein the first gripping point and the second gripping point are determined in a section of the flat workpiece for the imaging of which in the camera image or in an associated partial image the relatively highest probability with which the camera image or the partial image shows an edge has been established by the first machine learning module.

32:

• The method according to claim 20, wherein the second machine learning module includes a trained artificial neural network which, for determining the first gripping point and the second gripping point, receives the camera image or at least one partial image of the camera image and extracts characteristic image features of the camera image or of the at least one partial image, wherein, based on the characteristic image features, gripping coordinates and a gripping orientation are calculated for the first gripper and/or gripping coordinates and a gripping orientation are calculated for the second gripper.

33:

• The method according to claim 20, wherein the flat workpiece is provided in a folded state on a manipulation surface in step a).

37:

• The positioning device according to claim 34, wherein a drop distance for the flat workpiece and a ventilation unit are provided, the ventilation unit comprising a plurality of outflow openings and delimiting the drop distance at least in sections.

38:

• The positioning device according to claim 37, wherein the plurality of outflow openings is provided at a ventilation surface, the ventilation surface extends substantially horizontally, and the ventilation surface coincides with the manipulation surface.

39:

• The positioning device according to claim 37, wherein the plurality of outflow openings is provided at a ventilation surface that is inclined at an angle of less than 90 degrees in relation to a horizontal plane, and the manipulation surface adjoins a lower edge of the ventilation surface.