Method for the collision-free movement of a crane

Patent metadata

Field	Value
Publication number	US-12384665-B2
Application number	US-202218278319-A
Country	US
Kind code	B2
Filing date	Jan 4, 2022
Priority date	Feb 23, 2021
Publication date	Aug 12, 2025
Grant date	Aug 12, 2025

Abstract

In a method for a collision-free movement of a crane in a crane lane, a sensor captures a first training data set of raw data during a movement of the crane outside a crane operation in the crane lane. The first training data set is evaluated while teaching a first neural network based on the captured raw data and first training data are determined from the evaluated first training data set. The sensor captures current sensor data during a movement of the crane during the crane operation in the crane lane. The current sensor data is compared with the first training data, and an anomaly is detected between the current sensor data and the first training data.

First claim

The invention claimed is:

1. A method for a collision-free movement of a crane in a crane lane, said method comprising:
capturing with a sensor a first training data set of raw data during a movement of the crane outside a crane operation in the crane lane;
evaluating the first training data set while teaching a first neural network based on the captured raw data;
determining first training data from the evaluated first training data set;
capturing with the sensor current sensor data during a movement of the crane during the crane operation in the crane lane;
comparing the current sensor data with the first training data; and
detecting an anomaly between the current sensor data and the first training data.

2. The method of claim 1 , wherein the sensor is an optical sensor.

3. The method of claim 1 , further comprising:
at least partially assigning the first neural network to a central IT infrastructure; and
sending the raw data of the first training data set for evaluating the first training data set to the central IT infrastructure.

4. The method of claim 2 , further comprising sending the first training data from the central IT infrastructure to a detection module assigned to the crane.

5. The method of claim 1 , wherein the sensor is designed as a camera, and further comprising capturing lane markings in a region of the crane lane by the camera.

6. The method of claim 1 , further comprising checking a plausibility of a detection of the anomaly through a confidence estimation of the first neural network.

7. The method of claim 1 , further comprising:
providing second training data from a second training data set of a second neural network;
comparing the current sensor data with the second training data; and
detecting an object in the current sensor data.

8. The method of claim 7 , wherein the object is detected at a same time as the anomaly is detected.

9. The method of claim 7 , wherein the object is detected in a detection module assigned to the crane.

10. The method of claim 7 , further comprising checking a plausibility of a detection of the object through a confidence estimation of the second neural network.

11. The method of claim 7 , further comprising stopping the crane after detection of the anomaly and/or detection of the object.

12. The method of claim 1 , further comprising moving the crane in an automated manner in the crane lane.

13. The method of claim 12 , wherein the crane is moved completely in an automated manner in the crane lane.

14. A control unit, comprising at least one of a digital logic module, GPU and AI accelerator for carrying out a method as set forth in claim 1 .

15. The control unit of claim 14 , wherein the digital logic module is a microprocessor, a microcontroller or an ASIC (application-specific integrated circuit).

16. A computer program product, comprising a computer program embodied in a non-transitory computer readable medium, wherein the computer program, when loaded Into a control unit as set forth in claim 15 and executed by the control unit, causes the control unit to perform the steps of:
capturing with a sensor a first training data set of raw data during a movement of the crane outside a crane operation in the crane lane;
evaluating the first training data set while teaching a first neural network based on the captured raw data;
determining first training data from the evaluated first training data set;
capturing with the sensor current sensor data during the movement of the crane during the crane operation in the crane lane;
comparing the current sensor data with the first training data; and
detecting an anomaly between the current sensor data and the first training data.

17. A safety system, comprising:
a sensor designed to capture a first training data set of raw data during a movement of a crane outside a crane operation in a crane lane; and
a control unit communicating with the sensor to receive the first training data set of raw data and designed to carry out a method as set forth in claim 1 .

18. The safety system of claim 17 , wherein the sensor is an optical sensor.

19. A crane, comprising a safety system, said safety system comprising a sensor designed to capture a first training data set of raw data during a movement of a crane outside a crane operation in a crane lane, and a control unit communicating with the sensor to receive the first training data set of raw data and designed to carry out a method as set forth in claim 1 .

20. The crane of claim 19 , designed as a gantry crane and further comprising a further said sensor, said crane being movable in at least two directions of travel, in particular opposite directions of travel, with the directions of travel being assigned to the sensor and further sensor, respectively, which have each a detection area in a corresponding one of the directions of travel.

Assignees

• Siemens Ag

Inventors

• Benkert Johannes
• Dobler Thomas
• Vogel Julian
• Walthelm Axel

Classifications

• B66C15/045 Primary

  electrical · CPC title

• B66C19/00

  Cranes comprising trolleys or crabs running on fixed or movable bridges or gantries (B66C17/00 takes precedence; base supporting structures with legs B66C5/00; jib cranes B66C23/00) · CPC title

• B66C13/48 Primary

  Automatic control of crane drives for producing a single or repeated working cycle; Program control · CPC title