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Design, Modeling, Self-Calibration and Grasping Method for Modular Cable-Driven Parallel Robots.

Wanlin Mai1, Yonghe Wang1, Zhiquan Yang1

  • 1School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.

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|April 14, 2026
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Summary
This summary is machine-generated.

This study introduces a modular cable-driven parallel robot (MCDPR) with enhanced modularity and vision-based self-calibration. The MCDPR system significantly improves grasping accuracy and reduces recalibration needs for large-space manipulation tasks.

Keywords:
RGB-D visual graspingkinematic modelingmodular cable-driven parallel robotself-calibration

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Area of Science:

  • Robotics
  • Mechatronics
  • Computer Vision

Background:

  • Cable-driven parallel robots (CDPRs) offer advantages in large-space manipulation due to their lightweight design and reconfigurability.
  • Existing CDPR systems face challenges in modularity, frequent recalibration, and integrating visual perception with grasp execution.

Purpose of the Study:

  • To present a modular cable-driven parallel robot (MCDPR) addressing limitations in current CDPR systems.
  • To develop and validate a novel kinematic model, vision-based self-calibration technique, and visual grasping method for MCDPRs.

Main Methods:

  • A modular mechanical architecture integrating drive, sensing, and cable-guiding functions for ease of assembly and operation.
  • A pulley-considered multilayer kinematic model and a vision-based self-calibration method using AprilTag observations for parameter identification.
  • A vision-guided bin-picking strategy combining RGB-D perception, coordinate transformation, and the calibrated robot model.

Main Results:

  • Self-calibration reduced Euclidean grasping position error from 0.371 mm to 0.048 mm and orientation error from 0.071° to 0.004° in simulations.
  • Experimental results showed a 58.33% reduction in relative position error after self-calibration.
  • The MCDPR demonstrated improved grasping accuracy and reduced recalibration frequency.

Conclusions:

  • The proposed MCDPR system offers enhanced modularity, efficient self-calibration, and effective visual grasping capabilities.
  • The developed methods significantly improve the precision and practicality of CDPRs for large-space manipulation.
  • The integrated software/hardware validation framework confirms the system's effectiveness and potential for real-world applications.