Survey on Key Technology of Robocup Rescue Robot

Zhang, Xuexi; Yuming, An; Fu, Genping; Gao, Lu; Shiliu, Liang

doi:10.23919/chicc.2019.8866137

Cited by 7 publications

(1 citation statement)

References 8 publications

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“…In [10] an overview of mobile manipulators used in industrial environments is given. Different search and rescue robots are described in [21,95] as well as in [11] (Part F, Sect. 50).…”

Section: Physical Capabilities (Hardware)mentioning

confidence: 99%

A review of the challenges in mobile manipulation: systems design and RoboCup challenges

Sereinig

Werth

Faller

2020

Elektrotech. Inftech.

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Mobile robotics is already well established in today’s production lines. Navigation, control and perception for mobile robots are vivid fields of research fostering advances in Industry 4.0. In order to increase the flexibility of such mobile platforms, it is also common practice to add serial manipulator arms to their yielding systems with nine degrees of freedom and more. These platforms are not limited to industry but are supportive in various field such as service, assistance, teleoperation and also rehabilitation. Due to the operation of such increasingly complex systems in less structured and dynamic environments - often in close contact with humans - more demanding challenges evolve in terms of systems design, control and sensors. These challenges are also reflected in the various RoboCup leagues. In this paper, we discuss state-of-the-art developments in mobile manipulation using developments and work done in the context of the RoboCup competition as design examples. Additionally, we elaborate on the recent challenges of the RoboCup Rescue League as well as on the RoboCup@Work League.

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“…In [10] an overview of mobile manipulators used in industrial environments is given. Different search and rescue robots are described in [21,95] as well as in [11] (Part F, Sect. 50).…”

Section: Physical Capabilities (Hardware)mentioning

confidence: 99%

A review of the challenges in mobile manipulation: systems design and RoboCup challenges

Sereinig

Werth

Faller

2020

Elektrotech. Inftech.

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Recent Advances in Mobile Robot Localization in Complex Scenarios

Zhang

Liu

et al. 2023

Lecture Notes in Electrical Engineering

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Human impressions of robot performance are often measured through surveys. As a more scalable and cost-effective alternative, we study the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques. To this end, we first contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in a Virtual Reality simulation, together with impressions of robot performance provided by users on a 5-point scale. Second, we contribute analyses of how well humans and supervised learning techniques can predict perceived robot performance based on different combinations of observation types (e.g., facial, spatial, and map features). Our results show that facial expressions alone provide useful information about human impressions of robot performance; but in the navigation scenarios we tested, spatial features are the most critical piece of information for this inference task. Also, when evaluating results as binary classification (rather than multiclass classification), the F1-Score of human predictions and machine learning models more than doubles, showing that both are better at telling the directionality of robot performance than predicting exact performance ratings. Based on our findings, we provide guidelines for implementing these predictions models in real-world navigation scenarios.

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