Cable tension sensing improves safety of tethered robot movement
Tension Tells the Tail: Cable-Obstacle Interaction Sensing for Tethered Mobile Robot Navigation
Robotics
Summary
Tethered robots use cables for power and communication, but these cables can get caught or damage obstacles during movement. The authors show that by measuring how the cable pulls or relaxes when touching an obstacle, a robot can tell if the obstacle can be moved or not. They trained a computer model on tension data from real robot experiments and achieved over 95% accuracy in identifying obstacle movability. This helps robots avoid problems like snagging and improves their ability to navigate safely.
What this means in practice
- •For industrial robot operators: Detect when tether cables interact with movable or fixed obstacles to prevent cable damage and maintain uninterrupted operation in factories or warehouses.
- •For search and rescue teams: Improve navigation of tethered robots in cluttered disaster sites by sensing cable tension changes to avoid getting stuck or snagged on debris.
Authors
Ryuki Shimada, Koki Suzuki, Genya Ishigami
Abstract
Tethered mobile robots can sustain long-duration operation through continuous power supply and stable communication via their cables. However, cable-environment interaction causes unintended obstacle displacement, cable snagging or entanglement, and even cable damage, leading to mission failure. To assess the risk of individual cable-obstacle contacts, this study investigates cable tension as a sensing modality for estimating whether a contacted obstacle is movable or non-movable. We conducted experiments with a tethered mobile robot and collected tension data while varying robot velocity, contact angle between the obstacle and cable, obstacle mass, and obstacle side-surface material. The results show that obstacle movability is characterized by the initial tension rise and, more importantly, by whether the tension relaxes or remains high after its peak. Based on these observations, we select six features from the tension profiles and train a logistic-regression classifier using 117 labeled trials. The proposed method distinguishes movable and non-movable cases with a balanced accuracy of 95.5% after 30 cm of post-contact travel. These results demonstrate the potential of cable tension for risk assessment of cable-obstacle interactions and provide a step toward online, cable-aware autonomous navigation.