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.

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.

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.

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.

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.

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.

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.

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.

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.

Abstract: A new technique on the dithering of one-dimensional signals is presented. The novel technique determines whether dither is needed when reducing the bit depth and evaluates whether the total error of the un-dithered quantization is white noise. If this is the case, then no undesired harmonics are added in the quantization or re-quantization process. The novel technique presents a state of the art signal-dependent dither having a lower quantization noise.