D-Bot: A Food Serving Robot During Pandemic Situation

Guan, Wong Hooi; Melvern, Choy; Hou, Foo Tun; Zaw, Aung Moe; Khan, Mohamed K.; Aramugam, Kalaiselvi; Ramaswamy, Manicam

doi:10.1109/raaicon54709.2021.9929991

Cited by 5 publications

(6 citation statements)

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“…The country of origin for most cobot studies covered here is India (see “Design location” columns in Table 3 ). Interestingly, all the COVID-motivated articles ( n = 7) were published by the design team majorly in India ( n = 5) to provide services such as “Aido-Bot” used for cleaning and sterilization, as well as taking patient’s vitals ( Hossain et al, 2020 ); aid logistics of items such as food, and medication for patients in isolation ( Guan et al, 2021 ; Oishi et al, 2021 ). Of the 21 articles published by the design team, three were designed to aid elder care and/or memory support with the major goal of providing service and logistic support (see “Cobot application” columns), and three articles stated social support as the design team (design location are South Korea, Canada and the U.S.) motivation of design as an emotional support and companion for patients (see “Cobot application” columns).…”

Section: Resultsmentioning

confidence: 99%

“…Various target populations were described, including patients (e.g., Oishi et al, 2021 ; Hossain et al, 2020 , etc. ), clinical staff (e.g., Boumans et al, 2018 ; Guan et al, 2021 , etc. ), elderly (e.g., Liu et al, 2018 ; Bohlen et al, 2020 ), Healthcare workers (e.g., Virgolin et al, 2021 ; Konara et al, 2020 ; Dangi et al, 2021 , etc.…”

Section: Resultsmentioning

confidence: 99%

“…In terms of morphology, many cobots had relatively mechanistic designs ( n = 16) in which the designers did not try to create a humanlike or animal-like appearance (i.e., zoomorphic) and/or the design focused on utilitarian function instead of form but may choose to adopt a different appearance for future versions of the cobot. Some applications showed exposed electronics and wires which were likely due to the stage of the prototype and not the intention for the finalized product such as an autonomous mobile cobot (AMR) called “D-Bot” ( Guan et al, 2021 ), or a cobotic cart called “CARVER” ( Thamrongaphichartkul et al, 2020 ; Vongbunyong et al, 2021 ). The rest of the cobots in the included articles featured anthropomorphic designs ( n = 12) such as the humanoid cobot “Pepper” from United Group Robotics ( Boumans et al, 2018 ; Boumans et al, 2019 ), “NAO” and “MEDi” developed by Aldebaran from United Robotics Group ( Farrier et al, 2019 ; Ali et al, 2020 ; Rossi et al, 2022 ), “PR2” ( Dalal et al, 2018 ; Lundberg et al, 2022 ), or a multimodal interactive mobile cobot called “ISOLDE” ( Virgolin et al, 2021 ; see “Morphology” columns in Table 3 ).…”

Section: Resultsmentioning

confidence: 99%

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A systematic review of collaborative robots for nurses: where are we now, and where is the evidence?

Babalola,

Gaston,

Trombetta

et al. 2024

Front. Robot. AI

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Introduction: Robots present an opportunity to enhance healthcare delivery. Rather than targeting complete automation and nurse replacement, collaborative robots, or “cobots”, might be designed to allow nurses to focus on high-value caregiving. While many institutions are now investing in these platforms, there is little publicly available data on how cobots are being developed, implemented, and evaluated to determine if and how they support nursing practice in the real world.Methods: This systematic review investigates the current state of cobotic technologies designed to assist nurses in hospital settings, their intended applications, and impacts on nurses and patient care. A comprehensive database search identified 28 relevant peer-reviewed articles published since 2018 which involve real studies with robotic platforms in simulated or actual clinical contexts.Results: Few cobots were explicitly designed to reduce nursing workload through administrative or logistical assistance. Most included studies were designed as patient-centered rather than nurse-centered, but included assistance for tasks like medication delivery, vital monitoring, and social interaction. Most applications emerged from India, with limited evidence from the United States despite commercial availability of nurse-assistive cobots. Robots ranged from proof-of-concept to commercially deployed systems.Discussion: This review highlights the need for further published studies on cobotic development and evaluation. A larger body of evidence is needed to recognize current limitations and pragmatic opportunities to assist nurses and patients using state-of-the-art robotics. Human-centered design can assist in discovering the right opportunities for cobotic assistance. Committed research-practice partnerships and human-centered design are needed to guide the technical development of nurse-centered cobotic solutions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A systematic review of collaborative robots for nurses: where are we now, and where is the evidence?

Babalola,

Gaston,

Trombetta

et al. 2024

Front. Robot. AI

View full text Add to dashboard Cite

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“…Due to their high capabilities and human-friendly interfaces, indoor mobile robots have become increasingly prevalent daily. Indoor mobile robots are capable of performing many tasks in a given indoor environment, including cleaning rooms ( Muthugala et al, 2020 ), providing efficient human services ( Ruan, Wu & Xu, 2021 ), and serving meals ( Guan et al, 2021 ). However, indoor mobile robots’ dexterous and safe locomotion becomes challenging on surfaces with varying features.…”

Section: Introductionmentioning

confidence: 99%

Indoor surface classification for mobile robots

Demirtaş,

Erdemir,

Bayram

2024

PeerJ Computer Science

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The ability to recognize the surface type is crucial for both indoor and outdoor mobile robots. Knowing the surface type can help indoor mobile robots move more safely and adjust their movement accordingly. However, recognizing surface characteristics is challenging since similar planes can appear substantially different; for instance, carpets come in various types and colors. To address this inherent uncertainty in vision-based surface classification, this study first generates a new, unique data set composed of 2,081 surface images (carpet, tiles, and wood) captured in different indoor environments. Secondly, the pre-trained state-of-the-art deep learning models, namely InceptionV3, VGG16, VGG19, ResNet50, Xception, InceptionResNetV2, and MobileNetV2, were utilized to recognize the surface type. Additionally, a lightweight MobileNetV2-modified model was proposed for surface classification. The proposed model has approximately four times fewer total parameters than the original MobileNetV2 model, reducing the size of the trained model weights from 42 MB to 11 MB. Thus, the proposed model can be used in robotic systems with limited computational capacity and embedded systems. Lastly, several optimizers, such as SGD, RMSProp, Adam, Adadelta, Adamax, Adagrad, and Nadam, are applied to distinguish the most efficient network. Experimental results demonstrate that the proposed model outperforms all other applied methods and existing approaches in the literature by achieving 99.52% accuracy and an average score of 99.66% in precision, recall, and F1-score. In addition to this, the proposed lightweight model was tested in real-time on a mobile robot in 11 scenarios consisting of various indoor environments such as offices, hallways, and homes, resulting in an accuracy of 99.25%. Finally, each model was evaluated in terms of model loading time and processing time. The proposed model requires less loading and processing time than the other models.

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“…The country of origin for most cobot studies covered here is India (see "Design location" columns in Table 3). Interestingly, all the COVID-motivated articles (n = 7) were published by the design team majorly in India (n = 5) to provide services such as "Aido-Bot" used for cleaning and sterilization, as well as taking patient's vitals (Hossain et al, 2020); aid logistics of items such as food, and medication for patients in isolation (Guan et al, 2021;Oishi et al, 2021). Of the 21 articles published by the design team, three were designed to aid elder care and/or memory support with the major goal of providing service and logistic support (see "Cobot application" columns), and three articles stated social support as the design team (design location are South Korea, Canada and the U.S.) motivation of design as an emotional support and companion for patients (see "Cobot application" columns).…”

Section: Reviewed Articles and Product Matrixmentioning

confidence: 99%

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D-Bot: A Food Serving Robot During Pandemic Situation

Cited by 5 publications

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A systematic review of collaborative robots for nurses: where are we now, and where is the evidence?

A systematic review of collaborative robots for nurses: where are we now, and where is the evidence?

Indoor surface classification for mobile robots

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