Remote Supervision of an Autonomous Surface Vehicle using Virtual Reality

Lager, Mårten; Topp, Elin Anna

doi:10.1016/j.ifacol.2019.08.104

Cited by 18 publications

(5 citation statements)

References 12 publications

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“…All this can be achieved with affordable sensors, which we have used in this work. Our future efforts are targeting the integration of our positioning approach into an earlier presented VR based visualization and control tool for remote supervision of (semi-)autonomous vessels [3]. We also plan to develop an ML-based tool for evaluation of the current seabed and magnetic field anomaly map.…”

Section: Discussionmentioning

confidence: 99%

“…Humans are skilled in having a good overview of the navigation, and can reason about and adapt to the situation if the surrounding area does not match what is seen in the sea chart. The reasoning comes at a cognitive cost, though, and after a while, a human loses focus with increased risks for mistakes [3]. This is one of the reasons why 90-95% of all collision accidents at sea since 1999 are caused by human errors [4].…”

Section: Introductionmentioning

confidence: 99%

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Robust Terrain-Aided Navigation through Sensor Fusion

Lager

Topp

Malec

2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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To make autonomous, affordable ships feasible in the real world, they must be capable of safely navigating without fully relying on GPS, high-resolution 3D maps, or high-performance navigation sensors. We suggest a method for estimating the position using affordable navigation sensors (compass and speed log or inertial navigation sensor), sensors used for perception of the environment (cameras, echo sounder, magnetometer), and publicly available maps (sea charts and magnetic intensity anomalies maps). A real-world field trial has shown that the proposed fusion mechanism provides accurate and robust navigation, applicable for affordable autonomous ships.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Terrain-Aided Navigation through Sensor Fusion

Lager

Topp

Malec

2020

2020 IEEE 23rd International Conference on Information Fusion (FUSION)

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“…The experimental results fully prove the importance of data fusion. Lager and Topp (2019) designed a remote monitoring system of an unmanned autonomous ship under limited bandwidth communication conditions. The system uses 3D modelling and virtual reality technology to provide users with surrounding environment situational awareness and collision detection functions.…”

Section: Related Workmentioning

confidence: 99%

Trajectory risk cognition of ship collision accident based on fusion of multi-model spatial data

et al. 2022

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When conducting accident analysis, the assessment of risk is one of the important links. Moreover, with regards to crew training, risk cognition is also an important training subject. However, most of the existing researches only rely on a single or a few data sources. It is necessary to fuse the collected multi-source data to obtain a more comprehensive risk evaluation model. There are few studies on the three-dimensional (3D) multi-modal data-fusion-based trajectory risk cognition. In this paper, a fuzzy logic-based trajectory risk cognition method is proposed based on multi-model spatial data fusion and accident data mining. First, the necessity of multi-model spatial data fusion is analysed and a data-fusion-based scene map is constructed. Second, a risk cognition model fused by multiple factors, multi-dimensional spatial calculations as well as data mining results is proposed, including a novel ship boundary calculation approach and newly constructed factors. Finally, a radar chart is used to illustrate the risk, and a risk cognition system is developed. Experiment results confirm the effectiveness of the method. It can be applied to train human operators of unmanned ship systems.

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“…In addition, the scope of the work is limited to the effect of these information filtering methods within virtual reality. Comparisons between VR and 2D interfaces have been investigated for robot teleoperation ( Lager and Topp, 2019 ; Roldan et al, 2017 ).…”

Section: Interface Designmentioning

confidence: 99%

Exploring Effects of Information Filtering With a VR Interface for Multi-Robot Supervision

2021

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Supervising and controlling remote robot systems currently requires many specialised operators to have knowledge of the internal state of the system in addition to the environment. For applications such as remote maintenance of future nuclear fusion reactors, the number of robots (and hence supervisors) required to maintain or decommission a facility is too large to be financially feasible. To address this issue, this work explores the idea of intelligently filtering information so that a single user can supervise multiple robots safely. We gathered feedback from participants using five methods for teleoperating a semi-autonomous multi-robot system via Virtual Reality (VR). We present a novel 3D interaction method to filter the displayed information to allow the user to read information from the environment without being overwhelmed. The novelty of the interface design is the link between Semantic and Spatial filtering and the hierarchical information contained within the multi robot system. We conducted a user study including a cohort of expert robot teleoperators comparing these methods; highlighting the significant effects of 3D interface design on the performance and perceived workload of a user teleoperating many robot agents in complex environments. The results from this experiment and subjective user feedback will inform future investigations that build upon this initial work.

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Remote Supervision of an Autonomous Surface Vehicle using Virtual Reality

Cited by 18 publications

References 12 publications

Robust Terrain-Aided Navigation through Sensor Fusion

Robust Terrain-Aided Navigation through Sensor Fusion

Trajectory risk cognition of ship collision accident based on fusion of multi-model spatial data

Exploring Effects of Information Filtering With a VR Interface for Multi-Robot Supervision

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