Adaptive Screw-Drive In-Pipe Robot with Hall-Effect Force Sensing and Active Gripping Control

Screw-drive in-pipe robots are widely used for inspection and maintenance of pipeline infrastructure because their tilted-wheel locomotion enables continuous traversal of horizontal, vertical, and curved pipes. However, most existing designs rely on passive spring mechanisms to generate wall-contact forces, making traction performance highly sensitive to pipe-diameter variations, friction changes, and manufacturing tolerances. This paper presents an adaptive screw-drive in-pipe robot that integrates adjustable radial geometry, embedded Hall-effect force sensing, and closed-loop gripping-force control. A unified mechanical–geometric model is developed to describe the coupling between actuator displacement, spring compression, wheel-tilt geometry, and pipe-diameter variation. Based on this model, a minimum safe gripping-force condition is derived and used to define a reference force for real-time control. A proportional–derivative controller regulates the gripping force of the front traction module, while a rear stabilizing module ensures axial alignment and suppresses body rotation. Simulation results under realistic diameter transitions and external disturbances demonstrate stable force regulation, preservation of a positive traction margin, and reduced unnecessary actuator effort. The proposed approach enables robust and energy-aware screw-drive locomotion in variable-diameter pipelines. A physical prototype of the robot has been fabricated to support the forthcoming experimental campaign; however, the validation presented in this study is limited to modeling and simulation, with experimental evaluation planned for future work.

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Publication Details

Journal
Electronics
Published
2026-02-26
DOI
https://doi.org/10.3390/electronics15050960
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
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article

Adaptive Screw-Drive In-Pipe Robot with Hall-Effect Force Sensing and Active Gripping Control

Riadh Zaier, Amur Salim Al Yahmedi
Electronics
Soft Robotics and Applications
article

Adaptive Screw-Drive In-Pipe Robot with Hall-Effect Force Sensing and Active Gripping Control

Riadh Zaier, Amur Salim Al Yahmedi
article en

Abstract

Screw-drive in-pipe robots are widely used for inspection and maintenance of pipeline infrastructure because their tilted-wheel locomotion enables continuous traversal of horizontal, vertical, and curved pipes. However, most existing designs rely on passive spring mechanisms to generate wall-contact forces, making traction performance highly sensitive to pipe-diameter variations, friction changes, and manufacturing tolerances. This paper presents an adaptive screw-drive in-pipe robot that integrates adjustable radial geometry, embedded Hall-effect force sensing, and closed-loop gripping-force control. A unified mechanical–geometric model is developed to describe the coupling between actuator displacement, spring compression, wheel-tilt geometry, and pipe-diameter variation. Based on this model, a minimum safe gripping-force condition is derived and used to define a reference force for real-time control. A proportional–derivative controller regulates the gripping force of the front traction module, while a rear stabilizing module ensures axial alignment and suppresses body rotation. Simulation results under realistic diameter transitions and external disturbances demonstrate stable force regulation, preservation of a positive traction margin, and reduced unnecessary actuator effort. The proposed approach enables robust and energy-aware screw-drive locomotion in variable-diameter pipelines. A physical prototype of the robot has been fabricated to support the forthcoming experimental campaign; however, the validation presented in this study is limited to modeling and simulation, with experimental evaluation planned for future work.

ElectronicsVol. 15(5)
Sultan Qaboos University (OM)
Industry, innovation and infrastructure
Openalex Percentile: Top 43%
Soft Robotics and Applications
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