Best Viewpoints for External Robots or Sensors Assisting Other Robots

Dufek, Jan; Xiao, Xuesu; Murphy, Robin R.

doi:10.1109/thms.2021.3090765

Cited by 12 publications

(2 citation statements)

References 114 publications

(317 reference statements)

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“…5 and 6) always followed the main robot offering an external point of view and assisting the operator in gaining Situational Awareness (SA). This principle is followed by KHG in every remote robot-assisted mission and is a common practice in robot field deployments [23]. Lastly, each robot was controlled by one operator.…”

Section: B Robotic Apparatusmentioning

confidence: 99%

Robot-Assisted Nuclear Disaster Response: Report and Insights from a Field Exercise

Chiou¹,

Epsimos²,

Nikolaou³

et al. 2022

Preprint

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This paper reports on insights by robotics researchers that participated in a 5-day robot-assisted nuclear disaster response field exercise conducted by Kerntechnische Hilfdienst GmbH (KHG) in Karlsruhe, Germany. The German nuclear industry established KHG to provide a robotassisted emergency response capability for nuclear accidents. We present a systematic description of the equipment used; the robot operators' training program; the field exercise and robot tasks; and the protocols followed during the exercise. Additionally, we provide insights and suggestions for advancing disaster response robotics based on these observations. Specifically, the main degradation in performance comes from the cognitive and attentional demands on the operator. Furthermore, robotic platforms and modules should aim to be robust and reliable in addition to their ease of use. Last, as emergency response stakeholders are often skeptical about using autonomous systems, we suggest adopting a variable autonomy paradigm to integrate autonomous robotic capabilities with the human-inthe-loop gradually. This middle ground between teleoperation and autonomy can increase end-user acceptance while directly alleviating some of the operator's robot control burden and maintaining the resilience of the human-in-the-loop.

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Section: B Robotic Apparatusmentioning

confidence: 99%

Robot-Assisted Nuclear Disaster Response: Report and Insights from a Field Exercise

Chiou¹,

Epsimos²,

Nikolaou³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…With the development of 3D imaging technology [3], techniques such as structured light measurement and laser scanning have become mature, and the 3D coordinates of object surfaces can be obtained accurately and quickly, generating 3D data of the scene for better perception and understanding of the surrounding environment. 3D data contains depth information of the scene and can represent the shape of objects' surfaces, with broad application prospects in fields such as robotics [4], AR/VR [5], human-computer interaction [6], remote sensing mapping [7], etc. 3D data can provide depth information that can help solve many problems in hazardous chemical warehouse storage, and thus using 3D data for classification and monitoring of goods in hazardous chemical warehouses has become a mainstream research direction in recent years [8].…”

Section: Introductionmentioning

confidence: 99%

A Point Cloud Classification Method and Its Applications Based on Multi-Head Self-Attention

Xue-Jun Liu,

Wen-Hui Wang

et al. 2023

Journal of Computers

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<p>In the monitoring the safety status of hazardous chemical warehouses by three-dimensional re-construction of deep camera point clouds, there are classification difficulties such as large space, sparse distribution of point clouds in cargo images, and similar distribution in low dimensions. Based on the above problem, a point cloud recognition method based on multi-head attention mechanism is proposed. The algorithm first normalizes the distribution of the point cloud data set through the affine transformation algorithm to solve the problem of sparse distribution. Then, the high-dimensional feature map is obtained by fusing the data down-sampling and curve feature aggregation algorithms to solve the problem of low-dimensional distribution approximation. The feature map is then encoded using a multi-head self-attention encoder to obtain features under different heads, which are then merged into a feature map. Finally, a multi-layer fully connected neural network is used as the decoder to decode the feature map into the final object classification. Comparative experiments were performed on the ModelNet40 dataset and the self-built dataset of warehouse goods, and the results showed that the accuracy of this paper was improved by 0.5% to 7.8% compared with that of other classification algorithms.</p> <p> </p>

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Monocular Visual Navigation Algorithm for Nursing Robots via Deep Learning Oriented to Dynamic Object Goal

Fu,

Wang,

Yang

et al. 2023

J Intell Robot Syst

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Best Viewpoints for External Robots or Sensors Assisting Other Robots

Cited by 12 publications

References 114 publications

Robot-Assisted Nuclear Disaster Response: Report and Insights from a Field Exercise

Robot-Assisted Nuclear Disaster Response: Report and Insights from a Field Exercise

A Point Cloud Classification Method and Its Applications Based on Multi-Head Self-Attention

Monocular Visual Navigation Algorithm for Nursing Robots via Deep Learning Oriented to Dynamic Object Goal

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