← 研踪 PaperTrailarXiv:2610.37535 · v1
Predict the Swing, Match the Speed: Robotic Pipe Stabbing With Learned Stereo Vision and Residual Reinforcement Learning
首次提交 2026-10-28 00:53
版本更新 2026-10-28 00:53
雷达观测 2026-10-29 00:34
摘要
A joint of pipe run into the hole swings from the elevator over the well and strikes a fixed stabbing guide at whatever speed the swing has. StabBot, a robotic stabbing system, predicts the swing and meets the pipe at its own speed. A pendulum Kalman filter tuned from the pipe tally tracks the joint in stereo images. A six-axis arm with an open V-shaped roller head closes in at matched velocity, confirms the last millimetres by touch, aims the pin over the box when the elevator hangs off centre, and follows the block down with lateral compliance. A ResNet-18 keypoint network locates the pipe, pin nose and box mouth in 3-D, and a residual reinforcement-learning policy refines the capture. On 200 paired random jobs with nine sizes from 2-3/8-in. tubing to 20-in. casing, StabBot seated every joint with the pipe touching only rollers, against 74% for a conventional fixed guide. Median contact speed fell from 18.4 to 2.3 cm/s and median peak head-pipe force from 11.3 to 1.4 kN, and the force was lower in all 200 jobs (Wilcoxon $p < 10^{-30}$). The network locates the pipe with 0.4-mm median error in 2.3 ms per stereo pair. With the network in the loop, success stayed at 100% under 20 mm of added noise, 300-ms delay, 85% frame loss, 20-mm calibration offset, 20-cm elevator offset and 40-cm swings. All results come from a 1:4 Froude-scaled MuJoCo model and are reported at full scale.
作者备注：18 pages, 9 figures, 9 tables. Submitted to Geoenergy Science and Engineering
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v1 · 2026-10-28 00:53 · 最新观测版本
Predict the Swing, Match the Speed: Robotic Pipe Stabbing With Learned Stereo Vision and Residual Reinforcement Learning
A joint of pipe run into the hole swings from the elevator over the well and strikes a fixed stabbing guide at whatever speed the swing has. StabBot, a robotic stabbing system, predicts the swing and meets the pipe at its own speed. A pendulum Kalman filter tuned from the pipe tally tracks the joint in stereo images. A six-axis arm with an open V-shaped roller head closes in at matched velocity, confirms the last millimetres by touch, aims the pin over the box when the elevator hangs off centre, and follows the block down with lateral compliance. A ResNet-18 keypoint network locates the pipe, pin nose and box mouth in 3-D, and a residual reinforcement-learning policy refines the capture. On 200 paired random jobs with nine sizes from 2-3/8-in. tubing to 20-in. casing, StabBot seated every joint with the pipe touching only rollers, against 74% for a conventional fixed guide. Median contact speed fell from 18.4 to 2.3 cm/s and median peak head-pipe force from 11.3 to 1.4 kN, and the force was lower in all 200 jobs (Wilcoxon $p < 10^{-30}$). The network locates the pipe with 0.4-mm median error in 2.3 ms per stereo pair. With the network in the loop, success stayed at 100% under 20 mm of added noise, 300-ms delay, 85% frame loss, 20-mm calibration offset, 20-cm elevator offset and 40-cm swings. All results come from a 1:4 Froude-scaled MuJoCo model and are reported at full scale.
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