Evidence map›Paper›PMID 41744547›Full record

ArticleBiomimetics (Basel, Switzerland)2026

Space Human-Robot Interaction with Gaze Tracking Based on Attention Mechanism.

Lihong Dai, Jinguo Liu, Zhaojie Ju

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lihong DaiSchool of Electrical and Electronics Engineering, Hubei Polytechnic University, Huangshi 435003, China.ORCID 0000-0003-3617-2691
Jinguo LiuState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.ORCID 0000-0002-6790-6582
Zhaojie JuKey Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310007, China.

Funding

AiBle project co-financed by the European Regional Development FundCAS Interdisciplinary Innovation Team JCTD-2018-11National Key Research and Development Program of China 2018YFB1304600National Natural Science Foundation of China 52075530Talent Introduction Project of Hubei Polytechnic University 23xjz13R
6 · The paper itself

Abstract

Gaze is a natural and rapid non-verbal interaction mode, particularly well-suited for human-robot interaction in busy space environments. However, the space human-robot interaction based on gaze is still in its infancy. Therefore, this paper conducts a preliminary exploration in this area. Using the AAR-2, a free-flying astronaut-assistant robot, as the platform, we establish a gaze tracking database, construct a gaze tracking model based on an attention mechanism, and develop a human-robot interface. When the astronaut gazes at a control button on the interface, the corresponding control instruction is transmitted to the STM32 controller within the AAR-2 via radio frequency communication. Subsequently, the AAR-2 is propelled by ducted fans to perform the corresponding action. At the same time, the AAR-2 feeds back its operational state to the astronaut, thereby enabling space human-robot interaction. In the system, we achieve an effective gaze tracking model with high accuracy and implement an efficient image preprocessing method with high real-time performance. The experimental results demonstrate that the system can meet the actual requirements for accuracy and real-time processing.

Indexed as

attention mechanismgaze trackingradio frequency communicationspace human–robot interaction

Identifiers

PMID41744547
PMCPMC12938590

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.