Enhancing Safety in Collaborative Cable-Driven Parallel Robots: Contact Distinction and Management for Carrying Tasks

Cable-Driven Parallel Robots (CDPRs) have shown significant potential in industrial applications due to their large workspace, high payload capacity, and flexibility. Nonetheless, ensuring safe and efficient human-robot collaboration, particularly during carrying tasks, remains an open problem. This paper primarily contributes a cable tension-based metric for detecting contact events, combined with a frequency-domain analysis of estimated external wrenches to distinguish different contact scenarios. Building upon this framework, three common contact scenarios are then addressed: (i) payload placement and removal without rigid attachment to the moving-platform (SC1), (ii) unintentional human-cable collisions (SC2), and (iii) human-platform collisions (SC3). Specific management strategies are then proposed, including real-time mass estimation for payload handling, cable tension release for human-cable collisions, and a compliant trajectory controller for human-platform collisions. Experimental validations on a CDPR prototype demonstrate accurate scenario classification and safe contact handling without compromising overall productivity. By facilitating safer interactions, improved adaptability, and reliable handling of diverse contact events, this work expands the applicability of collaborative CDPRs in real-world industrial and logistic settings. Note to Practitioners—In many industrial applications, robots operating alongside human workers must quickly detect and respond to contact events to maintain safety and efficiency. Traditional methods often rely on force thresholds, which can introduce delays or false alarms, especially in dynamic tasks where rapid collisions must be addressed. Our work proposes an alternative approach that utilizes a frequency-domain analysis of sensed cable tensions and a weight-score system to robustly distinguish among various contact scenarios. By examining tension signals in the frequency domain, more nuanced information can be extracted, enabling faster and more accurate responses than threshold-based methods. From a practical standpoint, this technique is relatively straightforward to implement with various sensors, and it can be adapted to robots composed of different materials and link configurations by tuning the weight-score system for each new setup. Its primary benefit is improved contact distinction with high precision, which can enhance operator safety and reduce downtime without requiring extensive hardware modifications. However, like any sensor-driven control method, performance depends on adequate calibration and consistent signal quality. Future research might focus on integrating additional sensing modalities (e.g., vision or proximity sensors) and exploring broader deployment in diverse collaborative robot systems.