Advancements in Human-Cable Collision Detection and Management in Cable-Driven Parallel Robots

This paper presents significant advancements in the detection and management of human-cable collisions within Cable-Driven Parallel Robots. Building upon previous research in RA-L, a novel frequency-based filter is developed and applied to tension sensor measurements, enabling collision detection based on cable tension measurements. This approach facilitates the identification of the colliding cable and allows for the reduction of the corresponding cable tension, permitting safe contact between the cable and the environment or operator without causing damage. Additionally, a robust method for detecting the end of collisions is proposed, ensuring the system can promptly return to normal operation. An adaptive control method for cable length release is also developed, optimizing collision management during dynamic human-robot interactions. The proposed management approach effectively handles both severe and minor collisions. Experiments conducted with the CRAFT prototype validate these improvements, demonstrating that they substantially enhance safety and responsiveness in physical human-robot collaboration, thereby marking a noteworthy progression in collaborative robotic environments.

Recommended citation: H. Gao, C. Chevallereau, and S. Caro. (2025). "Advancements in Human-Cable Collision Detection and Management in Cable-Driven Parallel Robots." The Seventh International Conference on Cable-Driven Parallel Robots (CableCon 2025), Jul 2025, Hong Kong, China. ⟨hal-04912207v1⟩.
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