Backend
Cognitive and Behavioural Effects of Haptic Feedback for ROV Teleoperation in Hazardous Environments
arXiv eess.SY0 views
arXiv:2611.06689v1 Announce Type: cross Abstract: Teleoperating a Remotely Operated Vehicle (ROV) in complex underwater environments presents unique challenges. Dynamic, three dimensional obstacles and unseen water currents pose real risks to expensive robotic hardware in environments in which recovery can be next to impossible. This paper explores the cognitive effects of haptic feedback to improve vehicle safety in such environments. An in-water study is analysed with a ROV exposed to controlled cross-currents, with environmental information presented via a soft and vibrating haptic touchpad. Results demonstrate that while encountering an unseen cross-current significantly increases an operator's physical and temporal demand, the introduction of haptic feedback mitigates these factors and improves self-assessed performance. Interestingly, the haptic condition also produced the highest levels of mental demand and operator frustration. Despite this increased mental workload, operators drove the ROV significantly faster when receiving haptic cues compared to the current-only condition, indicating a higher level of user confidence. Ultimately, these findings suggest that while haptic feedback may initially require more cognitive processing to interpret unseen hazards, it successfully enhances operator awareness and promotes more confident navigation in hazardous environments.
Source:https://arxiv.org/abs/2611.06689PreviousTo lift or not to lift: Operator inference for nonlinear PDEs with boundary controlNextWhen to Ask for Help: Uncertainty-Triggered Aerial Refinement for Off-Road Navigation
Related posts
Coexistence-Aware Antenna Tilt and Resource Allocation for Cellular-Connected UAV Corridors
0arXiv cs.NI
StarWorld: A Cross-Layer World Model for Starlink Prediction and Streaming
0arXiv cs.NI
The Confidence Score: a New Measure on Kernels
0arXiv eess.SP
Box-Shift: Fixed-Complexity Counter-Hypothesis Generation for Soft-Output MIMO Box Decoding
0arXiv eess.SP
Vision-Aided Near-Field Channel Estimation for XL-MIMO
0arXiv eess.SP
Ghost Target Elimination in 5G Reference Signals ISAC via Multi-static Multilateration
0arXiv eess.SP