J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2026 , Vol. 31 >Issue 1: 99 - 105

DOI: https://doi.org/10.1007/s12204-026-2899-8

Intelligent Robots

Collision Detection for Vacuum Wafer Transfer Robot

Expand
  • 1. College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213200, Jiangsu, China; 2. The 58th Research Institute, China Electronics Technology Group Corporation, Wuxi 214035, Jiangsu, China

Received date: 2025-10-15

  Revised date: 2025-11-13

  Accepted date: 2025-11-15

  Online published: 2026-01-28

Fold

Abstract

In response to safety concerns caused by wafer transmission robot collisions in confined vacuum chambers, which can lead to wafer breakage and production line contamination, a collision detection and response method is proposed, leveraging the robot’s dynamics model and adjustable acceleration thresholds. The structure and motion characteristics of the robot were first analyzed. Subsequently, its kinematic and dynamic models were established and validated via simulation. To reduce noise interference, the dynamics model was computed using a reference trajectory from the host planner. Cross-correlation analysis was used to identify phase differences between the planned and encoder feedback trajectories, enabling phase compensation. For collision threshold settings, an acceleration-based adjustment scheme was developed, taking into account potential collision risk levels. Experimental tests on the vacuum robot verified the effectiveness of the proposed method.

Cite this article

Fang Xingyu, Wei Xianming, Sun Jintao, Xu Linsen . Collision Detection for Vacuum Wafer Transfer Robot[J]. Journal of Shanghai Jiaotong University(Science), 2026 , 31(1) : 99 -105 . DOI: 10.1007/s12204-026-2899-8

References

[1] ZIMMERMANN Y, SOMMERHALDER M, WOLF P, et al. ANYexo 2.0: A fully actuated upper-limb exoskeleton for manipulation and joint-oriented training in all stages of rehabilitation [J]. IEEE Transactions on Robotics, 2023, 39(3): 2131-2150.
[2] KAI J G, HUGHES J, THURUTHEL T G, et al. Improving robotic cooking using batch Bayesian optimization [J]. IEEE Robotics and Automation Letters, 2020, 5(2): 760-765.
[3] KONG P Y. Autonomous robot-like mobile chargers for electric vehicles at public parking facilities [J]. IEEE Transactions on Smart Grid, 2019, 10(6): 5952-5963.
[4] GOLZ S, OSENDORFER C, HADDADIN S. Using tactile sensation for learning contact knowledge: Discriminate collision from physical interaction [C]//2015 IEEE International Conference on Robotics and Automation. Seattle: IEEE, 2015: 3788-3794.
[5] MAVROGIANNIS C, BALDINI F, WANG A, et al. Core challenges of social robot navigation: A survey [J]. ACM Transactions on Human-Robot Interaction, 2023, 12(3): 1-39.
[6] SECIL S, OZKAN M. Minimum distance calculation using skeletal tracking for safe human-robot interaction [J]. Robotics and Computer-Integrated Manufacturing, 2022, 73: 102253.
[7] MIN F Y, WANG G, LIU N. Collision detection and identification on robot manipulators based on vibration analysis [J]. Sensors, 2019, 19(5): 1080.
[8] CHEN Y X, WENG L, LIU Y, et al. Development and characterization of a highly sensitive magnetic electronic skin for intelligent manipulators [J]. Measurement, 2024, 224: 113939.
[9] SUN S W, SONG C Y, WANG B, et al. Research on dynamic parameter identification and collision detection method for cooperative robots [J]. Industrial Robot, 2023, 50(6): 1024-1035.
[10] HE Y B, CHEN J J, GAO J, et al. Research on motion simulation of wafer handling robot based on SCARA [C]//2018 19th International Conference on Electronic Packaging Technology. Shanghai: IEEE, 2018: 734-739.
[11] XIONG W Q, PAN C R, QIAO Y, et al. Reducing wafer delay time by robot idle time regulation for single-arm cluster tools [J]. IEEE Transactions on Automation Science and Engineering, 2021, 18(4): 1653-1667.
[12] HAN B Y, ZHAO B, SUN R H. Research on motion control and wafer-centering algorithm of wafer-handling robot in semiconductor manufacturing [J]. Sensors, 2023, 23(20): 8502.
[13] LI W, HAN Y, WU J H, et al. Collision detection of robots based on a force/torque sensor at the bedplate [J]. IEEE/ASME Transactions on Mechatronics, 2020, 25(5): 2565-2573.
[14] SALEEM Z, GUSTAFSSON F, FUREY E, et al. A review of external sensors for human detection in a human robot collaborative environment [J]. Journal of Intelligent Manufacturing, 2025, 36(4): 2255-2279.
[15] SHARKAWY A N, ASPRAGATHOS N. Human-robot collision detection based on neural networks [J]. International Journal of Mechanical Engineering and Robotics Research, 2016, 7(2): 150-157.
[16] GAROFALO G, MANSFELD N, JANKOWSKI J, et al. Sliding mode momentum observers for estimation of external torques and joint acceleration [C]//2019 International Conference on Robotics and Automation. Montreal: IEEE, 2019: 6117-6123.

Options
Outlines

/

〈 〉