A Mobile Welding Robot for Extreme Conditions
Bibliographic record
Abstract
Abstract Welding is an important step in the manufacturing process of many products, yet traditional welding still requires intensive human labor and field welding in cold weather makes it more challenging. As robotic systems advance, they have become viable technologies for automating manufacturing processes in welding. Nevertheless, most existing robotic welding systems still rely on stationary robots and teach-playback programming mode. These limit the flexibility and adaptability of the robotic systems and requiring human intervention for setup, adjustments, and monitoring. Mobile manipulators have attracted researchers to incorporate into the robotics field owing to their variety of real-world applications, and mobile welding is one of the feasible applications. To address these limitations, a mobile welding robot (MWR) system is being developed for laboratory and field settings. This MWR system is equipped with a UGV (unmanned ground vehicle) with several sensors such as, LiDAR and an Inertial Measurement Unit (IMU), a six degree-of-freedom (DOF) robot manipulator, a 3D stereo camera, a flux core welder, and an onboard computer. The welding robot system is designed to navigate on uneven floors and outdoor construction sites autonomously, acquire 3D point cloud data of the workpiece, identify and segment the weld seams, perform automated manipulator path planning, and execute welding tasks with minimal human intervention. This research contributes to the field of robotic welding by developing an autonomous solution capable of operating in different environments, including laboratories, industry workshops, and outdoor with extreme weather conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".