公开(公告)号：US20220305593A1

公开(公告)日：2022-09-29

申请号：US17679983

申请日：2022-02-24

Applicant: Path Robotics Inc.

Inventor： Alexander James LONSBERRY ,  Andrew Gordon LONSBERRY ,  Nima Ajam GARD ,  Colin BUNKER ,  Carlos Fabian BENITEZ QUIROZ ,  Madhavun Candadai VASU

IPC: B23K37/02 ,  B25J11/00 ,  B25J9/16

Abstract: In various examples, a computer-implemented method of generating instructions for a welding robot. The computer-implemented method comprises identifying an expected position of a candidate seam on a part to be welded based on a Computer Aided Design (CAD) model of the part, scanning a workspace containing the part to produce a representation of the part, identifying the candidate seam on the part based on the representation of the part and the expected position of the candidate seam, determining an actual position of the candidate seam, and generating welding instructions for the welding robot based at least in part on the actual position of the candidate seam.

公开(公告)号：US20220410402A1

公开(公告)日：2022-12-29

申请号：US17902748

申请日：2022-09-02

Applicant: Path Robotics, Inc.

Inventor： Alexander James LONSBERRY ,  Andrew Gordon LONSBERRY ,  Nima Ajam GARD ,  Colin BUNKER ,  Carlos Fabian BENITEZ QUIROZ ,  Madhavun Candadai VASU

IPC: B25J9/16 ,  B23K37/04 ,  G06T7/00 ,  G06V10/764 ,  B23K37/02 ,  B25J15/00 ,  G06T7/70 ,  B25J13/08 ,  B25J11/00 ,  G06V10/82

Abstract: In some examples, an autonomous robotic welding system comprises a workspace including a part having a seam, a sensor configured to capture multiple images within the workspace, a robot configured to lay weld along the seam, and a controller. The controller is configured to identify the seam on the part in the workspace based on the multiple images, plan a path for the robot to follow when welding the seam, the path including multiple different configurations of the robot, and instruct the robot to weld the seam according to the planned path.

公开(公告)号：US20230278224A1

公开(公告)日：2023-09-07

申请号：US18179796

申请日：2023-03-07

Applicant: Path Robotics, Inc.

Inventor： Colin BUNKER ,  Alexander James LONSBERRY ,  Andrew Gordon LONSBERRY ,  Nima Ajam GARD ,  Milad KHALEDYAN ,  Carlos Fabian BENITEZ-QUIROZ

IPC: B25J9/16 ,  B25J11/00

CPC classification number: B25J9/1692 ,  B25J9/1697 ,  B25J11/005

Abstract: A method for calibrating a tool center point (TCP) of a robotic welding system. The method includes receiving a plurality of images captured from a plurality of image sensors of the robotic welding system, the plurality of images containing at least a portion of a protrusion extending from a tip of a weldhead of the robotic welding system, and identifying by a controller of the robotic welding system the protrusion extending from the weldhead in the plurality of images. The method additionally includes defining by the controller a longitudinal axis of the protrusion based on the protrusion identified in the plurality of images, and identifying by the controller a location in three-dimensional (3D) space of the weldhead based on the protrusion identified in the plurality of images and the defined longitudinal axis of the protrusion.

公开(公告)号：US20230173676A1

公开(公告)日：2023-06-08

申请号：US18056443

申请日：2022-11-17

Applicant: PATH ROBOTICS, INC.

Inventor： Alexander LONSBERRY ,  Andrew LONSBERRY ,  Nima Ajam GARD ,  Madhavun Candadai VASU ,  Eric SCHWENKER

IPC: B25J9/16 ,  B25J11/00 ,  B25J19/02

CPC classification number: B25J9/1664 ,  B25J9/163 ,  B25J9/161 ,  B25J11/005 ,  B25J19/021

Abstract: This disclosure provides systems, methods, and apparatuses, including computer programs encoded on computer storage media, that provide for training, implementing, or updated machine learning logic, such as an artificial neural network, to model a manufacturing process performed in a manufacturing robot environment. For example, the machine learning logic may be trained and implemented to learn from or make adjustments based on one or more operational characteristics associated with the manufacturing robot environment. As another example, the machine learning logic, such as a trained neural network, may be implemented in a semi-autonomous or autonomous manufacturing robot environment to model a manufacturing process and to generate a manufacturing result. As another example, the machine learning logic, such as the trained neural network, may be updated based on data that is captured and associated with a manufacturing result. Other aspects and features are also claimed and described.

公开(公告)号：US20230047632A1

公开(公告)日：2023-02-16

申请号：US17980769

申请日：2022-11-04

Applicant: Path Robotics, Inc.

Inventor： Alexander James LONSBERRY ,  Andrew Gordon LONSBERRY ,  Nima Ajam GARD ,  Colin BUNKER ,  Carlos Fabian BENITEZ QUIROZ ,  Madhavun Candadai VASU

IPC: B25J9/16 ,  B23K37/02 ,  G06T7/70 ,  G06V10/82 ,  B25J11/00 ,  B23K37/04 ,  G06T7/00 ,  G06V10/764 ,  B25J15/00 ,  B25J13/08

Abstract: In various examples, a computer-implemented method of generating instructions for a welding robot. The computer-implemented method comprises identifying an expected position of a candidate seam on a part to be welded based on a Computer Aided Design (CAD) model of the part, scanning a workspace containing the part to produce a representation of the part, identifying the candidate seam on the part based on the representation of the part and the expected position of the candidate seam, determining an actual position of the candidate seam, and generating welding instructions for the welding robot based at least in part on the actual position of the candidate seam.