Supplemental Human-Machine Interface for Haptic Shared Control to Convey Sensor Reliability Information to an Operator of Underwater Vehicles

In haptic shared control (HSC) for teleoperation, the human operator and autonomous system share control via torque on the input device. When a discrepancy exists between the human's maneuvering intent and the autonomous maneuver presentation, the human can apply additional force to the device to achieve the desired maneuvering. The required force level is generally predetermined during design and closely relates to the human workload. This study investigated scenarios where discrepancies between human and machine control occur frequently owing to unreliable sensor information. We propose a method to allow the operator to adjust the machine's assist level, facilitating smoother control transitions and proving useful when machine suggestions are unreliable. Conventional HSC systems generally lack sufficient information for operators to make informed decisions regarding adjusting assist levels, leading to delayed decisions and increased workloads. To address this, our research objective is to communicate sensor reliability to humans, thereby facilitating deciding the machine's assist level. We focused on the HSC of a remotely operated underwater vehicle (ROV) where the sensor system is prone to unreliable. Maneuvering experiments with an ROV simulation involving sixteen participants demonstrated the proposed method effectively communicates sensor reliability to operators and reduces mental demand compared to conventional methods.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Haptics
Published
2025-04-22
DOI
https://doi.org/10.1109/toh.2025.3563451
Citations
4
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
9.75
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Supplemental Human-Machine Interface for Haptic Shared Control to Convey Sensor Reliability Information to an Operator of Underwater Vehicles

Norimitsu Sakagami, Yasuaki Orita, Eito Sato, Takahiro Wada
4 citations
IEEE Transactions on Haptics
Human-Automation Interaction and Safety
9.75
article

Supplemental Human-Machine Interface for Haptic Shared Control to Convey Sensor Reliability Information to an Operator of Underwater Vehicles

Norimitsu Sakagami, Yasuaki Orita, Eito Sato, Takahiro Wada
article en
4 citations

Abstract

In haptic shared control (HSC) for teleoperation, the human operator and autonomous system share control via torque on the input device. When a discrepancy exists between the human's maneuvering intent and the autonomous maneuver presentation, the human can apply additional force to the device to achieve the desired maneuvering. The required force level is generally predetermined during design and closely relates to the human workload. This study investigated scenarios where discrepancies between human and machine control occur frequently owing to unreliable sensor information. We propose a method to allow the operator to adjust the machine's assist level, facilitating smoother control transitions and proving useful when machine suggestions are unreliable. Conventional HSC systems generally lack sufficient information for operators to make informed decisions regarding adjusting assist levels, leading to delayed decisions and increased workloads. To address this, our research objective is to communicate sensor reliability to humans, thereby facilitating deciding the machine's assist level. We focused on the HSC of a remotely operated underwater vehicle (ROV) where the sensor system is prone to unreliable. Maneuvering experiments with an ROV simulation involving sixteen participants demonstrated the proposed method effectively communicates sensor reliability to operators and reduces mental demand compared to conventional methods.

IEEE Transactions on HapticsVol. 18(3)
Ryukoku University (JP), Nara Institute of Science and Technology (JP)
Life below water
Openalex Percentile: Top 2%
Human-Automation Interaction and Safety
9.75
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.