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Cable-driven parallel robots (CDPRs) are attractive for large-space manipulation because of their lightweight structure, large workspace, and reconfigurability. However, existing systems still face three practical challenges: limited modularity of the mechanical architecture, repeated calibration after reconfiguration, and insufficient integration between visual perception and grasp execution. To address these issues, this paper presents a modular cable-driven parallel robot (MCDPR), together with its kinematic modeling, vision-based self-calibration, and visual grasping methods. First, a modular mechanical architecture is developed in which the drive, sensing, and cable-guiding functions are integrated to support rapid assembly/disassembly, convenient debugging, and cable anti-slack operation. Second, a pulley-considered multilayer kinematic model is established, and a vision-based self-calibration method is proposed to identify the structural parameters after assembly using onboard sensing and AprilTag observations, thereby reducing the number of recalibrations required during robot operation after reconfiguration. Third, a vision-guided bin-picking method is developed by combining RGB-D perception, coordinate transformation, and the calibrated robot model. Simulation and prototype experiments are conducted to validate the proposed system. A software/hardware combined validation framework is established, in which the CoppeliaSim-based simulation and the hardware prototype are used together to verify the proposed design and methods. In simulation, self-calibration reduces the Euclidean grasping position error from 0.371 mm to 0.048 mm and the orientation error from 0.071° to 0.004°. In experiments, the relative position error is reduced by 58.33% after self-calibration.

Traditional control strategies for Cable-Driven Parallel Robots (CDPRs) rely heavily on global kinematic modeling and precise calibration, severely limiting their adaptability in unstructured or dynamic environments. This study addresses the challenge of rapid deployment without geometric priors by proposing a reconfigurable CDPR system composed of modular units. A novel heuristic control strategy based on “4+2+1” local rules is introduced, comprising translational, attitude correction, and tension maintenance logic. By utilizing local feedback—including cable tension, attitude, and anchor orientation—this method generates control commands without requiring boundary condition calibration, thereby supporting real-time reconfiguration. Numerical simulations of a façade cleaning scenario demonstrate that the system maintains stability across varying topologies, including anchor position changes and unit failures. Compared to a benchmark kinematic method, the proposed strategy reduces trajectory tracking error by approximately 50.5% and suppresses the pitch Root Mean Square Error (RMSE) from a divergent 42.75° (traditional) to 1.52°, effectively preventing the attitude failure typical of uncalibrated model-based control. These findings confirm that the proposed rule-based approach significantly enhances robustness and adaptability, offering a practical solution for deploying CDPRs in complex environments without pre-existing maps.