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MYU Tokyo
S & M 2680

Service Robots in Wuhan Cabin Hospitals
Sheng Bao,1# Bill Huang,2# Jianjun Yuan,1* Bing Wang,2**
Lin Xia,2 Mingyuan Wang,1 and Yanchen Liu2
1Shanghai Robotics Institute, Shanghai Key Laboratory of Intelligent Manufacturing and Robotics,

School of Mechatronic Engineering and Automation, Shanghai University,
99 Shangda Road, Baoshan District, Shanghai 200444, China
2CloudMinds Technologies Inc., 85 Hong’an Road, Fangshan District, Beijing 102488, China(Received December 25, 2020; accepted May 25, 2021)

Keywords: medical service robot, human–robot interaction, COVID-19, sensor fusion

The cabin hospitals used for COVID-19 victims have an infectious environment and thus
require highly autonomous systems to provide various services. This article presents a multirobot architecture designed for cabin hospitals with special focus on the sensors used by service
robots. The robots’ functions include autonomous disinfection, delivery, cleaning, temperature
measurement, physical interaction, and conversation assistance. The robots collaborate witheach other by using multiple sensors in large and complicated scenes and evolve together. The
robots are controlled by cloud controllers considering a human-centered multi-robot
collaboration mechanism. This article presents the details of these robots, including the hardware
and software architecture of the robots with sensors, the communication modules, and the cloudbased central controllers. It also presents several applications of the robots and discusses open
problems.

1.

Introduction

The cabin hospitals in Wuhan were special hospitals built to accept COVID-19-infectedpatients with mild symptoms(1,2) to avoid pre-symptomatic, symptomatic, or asymptomatictransmission.(3) The hospitals were opened on February 5th, 2020 as temporary low-cost
infectious disease hospitals, similarly to battlefield hospitals. Images of a cabin hospital are
shown in Fig. 1. To quickly deploy and respond to the epidemic, the cabin hospitals require acontact-free environment, and there is a strong need for robotic assistance to reduce crossinfection.(4,5) Under this background, we developed a cloud robotic architecture for cabin
hospitals.
The cabin hospitals have the following features and requirements for robots:
 The indoor ground is generally flat, the walkways and compartments usually have sufficientmoving space for people and robots, and the indoor light is sufficient. Mobile robots caneasily move around to complete repetitive tasks.
*Corresponding author: e-mail: terabit@shu.edu.cn**Corresponding author: e-mail: charly@cloudminds.com#Co-first authors.

https://doi.org/10.18494/SAM.2021.3235

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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Fig. 1. (Color online) Images of a Wuhan cabin hospital. (a) Overall layout of beds. (b) Close-up view of one
walkway. (c) Ground and obstacles. (d) Close-up of some details in (c).

 The environment is semi-structured and semi-open, which is suitable for pre-constructing
maps. Robots may share common pre-constructed 3D maps while being corrected using
multi-sensor information collected online.
 Patients and medical staff move around the working area. Robots must avoid themautonomously using the sensors inside and around the robots.
 The inside areas of cabin hospitals are covered by 5G wireless communication networks.
Problems such as bandwidth, time delay, signal strength, and communication stability canthus be ignored.
Robots have long been required to take on dangerous, dull, and dirty jobs, often in
environments unsuitable for humans. In 2021, Gao et al.(6) reviewed the fundamental
requirements for robotics for infectious disease management and how robotic technologies canbe used in different medical scenarios. In 2009, Ozkil et al.(7) emphasized that there are
numerous tasks in hospitals where material delivery is required. Service robots include mobile
platforms with control pads so that patients can be examined remotely by doctors. One of themost well-known receptionist robots named Pepper can handle visitors continuously without
becoming tired, and the main function of Pepper is to direct the patients to the physician of theirchoice.(8) As part of research on human–robot interaction, in 2021, Hwang et al.(9) proposed asocial robot system using an end-to-end dialogue system in the context of hospital receptionist
robots to optimize the dialogue system. Disinfection robots are widely used to spray antiseptic
disinfectants over large areas. These robots are usually remotely controlled to avoid hazardous
contact between the disinfectant and humans. For instance, the Light Strike Germ-Zapping
Robot(10) is operated by trained hospital environmental services staff, while Courtney andRoyall(11) proposed a robot that relies on simultaneous localization and mapping (SLAM) to
autonomously scan and navigate a specific facility such as a laboratory. To address the two
scenarios of logistics and disinfection, Tamantini et al.(12) proposed a functional integration
robotic system and carried out a two-month experiment.
The main purpose of medical service robots is to minimize human-to-human contact and tosupport the hospital through cleaning, disinfection, and so forth. However, the above robots were
all developed under the condition of independent operation, so the data about the infectious
environment and navigation routes can only be stored and managed separately. Robot
communities play an important role in assisting the control of epidemics,(13) making it valuable
to study the collaboration between different types of robots and humans.

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The cloud can offer various advantages and provide good ideas for solving the existing
problems of service robots. Using a series of mobile service robots with multiple sensors undercentral cloud control(14) to work with many humans satisfies the above requirements. We
specially focus on the sensors used by service robots as part of a human-centered(15) multirobot(16) collaboration mechanism, which includes a human–robot collaborative security check,high modularization, rapid deployment under special circumstances, and strong adaptability tocomplicated scenarios. The functions of the involved robots include autonomous disinfection,delivery, cleaning, temperature measurement, and natural communication. The robots are notindependent and are connected to each other by modern communication modules and cloudbased central controllers. They collaboratively work in large and complicated scenes and evolve
together. This article presents the details of these robots with sensors, including the hardware
and software architecture, the communication modules, the cloud-based central controllers, andso forth. It also presents several useful applications with robots and discusses open problems.

2.

Architecture of Medical Service Robots under a Cloud Controller

Figure 2 shows an overview of the proposed architecture, including the series of service
robots, sensors, central controllers, and their communications. The interactions between
humans, the environment, and the robots produce important knowledge data. These data arecaptured by sensors and are uploaded to cloud controllers through the information transmission
channel formed by a wireless network. The cloud controllers are responsible for data analysis
and processing, decision-making, and the generation of specific execution instructions according
to application requirements. The cloud controllers run on remote servers or computer clusters
according to specific application conditions. In the application layer, the robots use theexperiences and knowledge of the cloud to overcome the limitations of each individual’s local
knowledge. They share and expand the knowledge based on the cloud center, giving them abetter perception of the surrounding environment and making it easier for them to react tospecific applications.
The various types of robots included in the architecture are shown in Table 1. They were used
to solve the problems of cleaning disinfection, measuring body temperature, reception, anddelivery. The key technical issues of each type are listed in the lower part of the table.
Technologies such as autonomous navigation, semantic recognition, and IR human bodyrecognition are required to solve these issues. These robots, sensors, and the working
environment form an ecosystem. They collaborate with each other and evolve together. Detailsof the implementation of the technologies will be presented in the following section.

3.

Essential Techniques for Service Robots

Medical service robots are realized through the integration and fusion of various technologies.
The most useful and basic functions are SLAM and navigation, semantic recognition, and IR
human recognition.

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Fig. 2.

(Color online) Cloud robotic architecture designed for cabin hospitals.

Table 1
Various robots involved in the architecture and their technical issues.
Robot Type Cleaning/Disinfection
Patrol Security
DeliveryTechnical
IssuesReception
Dynamic Human Body
SLAM and Navigation SLAM and Navigation SLAM and Navigation
Recognition
Autonomous Obstacle
Environmental
Automatic Speech
UV/UV-C Disinfection
Avoidance
Perception
Recognition (ASR)
Photoionic Catalytic
Autonomous Obstacle
Face Recognition
Intelligent Diagnosis
Purification
Avoidance
Hydrogen Peroxide
Limb Movement
IoT Technologies
Face Recognition
Vapor
Recognition
IR Thermal Imaging
Face Recognition

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SLAM and navigation

SLAM(17) and path planning(18) are essential techniques to implement autonomous navigation.
The cloud platform acts as an agent and service provider for robots. The cloud collects data fromthe robot ecosystem and connects the robot ecosystem to the computing and storage clusters
through the robot operating system (ROS) and the Hadoop Distributed File System (HDFS). The
server serves as a master node that maintains the ROS publisher list. The ROS nodes on theindividual robot query the master node to subscribe and receive data from the HDFS or fromother robots, as shown in the right of Fig. 3.
Different from the current single-robot SLAM process,(19,20) which establishes a map throughthe autonomous movement of the robot, realizes its position, plans a reasonable path according
to the established task, and finally executes the movement, the cloud-based architecture
coordinates the robots and results through a remote server as follows:(1) Structured maps of cabin hospitals are prepared in the cloud in advance to save time andlabor. On the basis of this structured map, the robot completes the unstructured areas throughdynamic scanning and continuous correction.
(2) Each robot can obtain a local map by incrementally navigating to different locations. Aftereach robot completes its local map, the map data are uploaded to the remote servers and are
integrated to complete a whole image.
(3) Each robot only performs pre-determined tasks. They no longer require a huge computing
load, which helps reduce the difficulty of algorithmic design and the power consumption ofindividual robots, improving the system’s robustness.(21,22)
We design and control each robot, such as the patrol robot shown in Fig. 3, as follows.
(1) Hardware: The following sensors are installed on the robot body, which work together tocollect data: thermal imaging cameras for human IR body temperature measurement, foureye panoramic night vision devices to help patrol robots work in the dark, and sonar sensors

Fig. 3. (Color online) A patrol robot communicates with the cloud server to upload data and receive commands.
The proposed architecture involves a series of robots that evolve together under the control of cloud servers.

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and 2D/3D cameras for SLAM and obstacle avoidance, face recognition, limb motion
recognition, and so forth.
(2) 3D map construction: We construct a 3D environmental map by semantically fusing the dataof robot vision, ultrasound, LiDAR, and other sensors, as shown in Fig. 4. The underlying
idea is to combine deep-learning-based environmental scene recognition, object detection
and recognition, geometric model representation, semantic annotation, and other technologies
in the cloud. A hierarchical method is implemented. The bottom layer is the parameter layerof the sensors, which stores the parameter information of the sensors (LiDAR, camera, GPS,etc.) needed for robot positioning. The upper layer is the sensors’ data layer, where the data ofeach sensor in the process of robot mapping and positioning are aligned and stored. The layer
above that is the sparse data layer, which stores sparse 3D maps obtained by either LiDAR or
cameras. Then, there is a layer for storing dense 3D maps, including dense point cloud andgrid information. This layer is also used for visualization and semantic analysis. The top
layer is the semantic layer, where the data are processed by detection and segmentation of thedense layer, assisted by semantic analysis. This layer represents the semantics of the 3D map
and is used to construct a complete 3D representation map database.
(3) Control and planning: Along with the 3D map construction, we use the software stack withthe help of ROS for planning and autonomous navigation. A complete software stack consistsof both open-source and internally developed software.
3.2

Semantic recognition

Reception robots are required to understand patient consultation semantics and carry out
communication(23) by analyzing and understanding emotions. They also respond to requests
from patients. Implementing these functions in a single robot is difficult due to the limitations ofhardware and intellectual development. Generating a personalized dialogue system requires ahuge amount of storage and calculation. In contrast, the cloud server combines the multi-robot
ontology to provide more humanized services to users. Its intelligent customer service system,
which incorporates natural language processing (NLP) technology, recognizes the information

Fig. 4.

Flowchart of the data fusion of 3D LiDAR and cameras of autonomous mobile robots in SLAM.

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communicated by the user and the user’s emotions through patient inquiry, appointment,
consultation, and so forth, and then helps, guides, and persuades the user.
The system can also predict the possible future development and spread of the epidemic
through data analysis, give recommendations for prevention and control, and then help
implement these measures. With the development of the epidemic, the service function
requirements of the robots may become more complex. In such a case, the reception robots canparticipate in the work by collaborating with other robots.
We demonstrate semantic recognition by using a reception robot named Ginger as an example
as follows.
(1) Hardware: Ginger is built on a platform with three omnidirectional wheels. The platform has
a LiDAR sensor for mapping and navigation, sonar sensors and a 3D camera for obstacle
avoidance, a 2D camera for searching for the charging connector, a 2D camera for facerecognition, a 3D camera for limb motion recognition, and other auxiliary sensors such as a
microphone array.
(2) ASR, NLP, and text to speech (TTS): The cloud servers run ASR,(24–26) NLP,(27–29) andTTS(30,31) algorithms to perform natural communication. Figure 5 shows the relations
between these algorithms. Here, ASR is used to recognize human speech and output it as text
by analyzing the phoneme features of the audio signal. NLP classifies the converted text,
judges the domain of the questions or commands, and returns the answers. A library of
dialogue Q&A is maintained during the NLP process using Artificial Intelligence Marker
Language (AIML). The library enables multi-round judgment and counter-question
management. TTS is used for speech synthesis. Text is transformed into a natural speech
flow assisted by the neural network in TTS.(32) TTS also makes the output voice smooth andrhythmically natural by leveraging some ensuing algorithms and performing directional
optimization.
3.3

Human IR recognition

There are two main functions of human IR recognition: human body temperature
measurement and mask recognition. These two functions are of equal importance for pandemic
prevention in regions with high traffic.

Fig. 5.

(Color online) Cloud services for understanding and responding to natural language.

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Sensors and Materials, Vol. 33, No. 9 (2021)(1) Continuous body temperature measurement: The temperature-measuring robots in the
proposed architecture implement a contact-free body temperature measurement method to
avoid cross-infection and reduce the measurement time. IR sensors(33) are the main sensors
used in the system. The essential algorithms include face detection and target tracking using
thermal images and neural networks, as shown in Fig. 6. Immediately after the thermal
detection of a face, the face is automatically tracked and the temperature of the region ofinterest (ROI) is continuously measured. The temperature-measuring robots receive and store
human body temperature data through their IR sensors locally, and at the same time upload
the data to the cloud servers. They can also obtain data from the cloud servers to view thechanges in body temperature in a certain period.
(2) Face detection and mask recognition: The face detection algorithm is based on feature fusion
in convolutional neural networks.(34) It uses a feature fusion pyramid structure to fuse highand low layers simultaneously by spatial and channel attention learning, which prevents thesemantic information on the high-level feature map from obscuring the detailed informationon the low-level feature map. The spatial and channel attention also makes the semantics anddetails complement each other. The semantic information is used as contextual clues toimprove the low-level features without losing details.(35) The mask recognition algorithm
expands the detected face area according to the proportions of the face, cuts out a sub-imageof the face area, and classifies the sub-image of the face area to establish whether a mask isbeing worn.

Fig. 6.

(Color online) Continuous body temperature measurement by detecting and tracking human faces.

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In addition to the temperature measurement, face detection, and mask recognition, the humanIR recognition module can also be quickly expanded to the safety testing of medical personnel
protection equipment (i.e., whether protective clothing is being correctly worn) and to therecognition and assistance of care recipients (such as pregnant, elderly, and disabled people andchildren) according to specific conditions and needs.

4.

Examples and Results

The cabin hospitals in Wuhan were temporarily built hospitals to host COVID-19 victims.
The hospitals had an infectious environment and thus required highly autonomous systems toprovide various services. A series of robots worked together at several cabin hospitals in Wuhan.
This section shows some examples of robot operation, mainly at the cabin hospitals, such as theone built in the Hongshan Gymnasium, Wuhan. Figure 7(a) shows a logistics robot transporting
medicines in a cabin hospital. Figure 7(b) shows a disinfection robot disinfecting the workspace.
Figure 7(c) shows a Ginger reception robot in operation, which was designed with features
including a friendly face and big eyes to help patients relax. These robots worked together toupdate cloud servers and receive commands. Figure 7(d) shows a view of the user interface forthe cloud servers.

Fig. 7. (Color online) (a) Examples of human–robot interaction between hospital staff and a delivery robot. (b)Examples of cleaning and disinfection robot at work. (c) Ginger reception robot in a Wuhan cabin hospital. (d)Hospital and user interface of the cloud servers of the hospital.

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From February 2020, 21 Ginger reception robots were used in the Wuhan cabin hospitals. On
March 10, all the cabin hospitals were closed and the Ginger robots were transferred to otherhospitals. The numbers of times the Ginger robots communicated with people and thepercentages of different topics discussed are shown in Table 2.
Figure 8 shows results obtained from the temperature measurement system and the mask
recognition system at a hospital entrance. The mask recognition system locates the face area in
the image, analyzes the face attributes and determines whether a mask is being worn. The same
temperature measurement system was used in the Wuhan cabin hospitals and many otherhospitals specially allocated to COVID-19. Table 3 lists the statistical data of temperaturedetection in other hospitals in Beijing, Inner Mongolia, and Anhui. The time period was from
April to June 2020 and the data unit is person-times.
Table 2
Dialogue data of the Ginger reception robots.
Dialogue times distribution
Dialogue contents
Data
Dialogue (times)
Content
Percentage (%)
Feb. 2020
11935
Easy Chat
37
Mar. 2020
17773
User Knowledge Q&A
32
Apr. 2020
12132
Public Knowledge Q&A
22May. 2020
20586
System Skills
7
Jun. 2020
12047
Default Replies
2

Fig. 8. (Color online) (a) IR continuous body temperature measurement, during which the maximum temperature
is extracted. (b) Comparison of a masked face and an unmasked face.

Table 3
Statistics of temperature detection at several hospitals using the proposed system.
Month
Total
April
May
June
# of people
3698
410131362061775890Beijing≥37.2 ℃
782
32547
47461
80790
# of people
9634
—
—
9634
Inner
Mongolia≥37.2 ℃
43——
43
# of people
614
1039
7678
9325
Anhui
≥37.2 ℃
23
60
216
299

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Discussion and Conclusions

In this article, we proposed a series of service robots with multiple sensors under cloudcontrol based on the analysis of the specific application environment and the requirements of the
Wuhan cabin hospitals. The details of the system architecture and several applications were
presented and developed, such as autonomous disinfection, delivery, cleaning, temperature
measurement, and natural communication. The framework can quickly and flexibly support
independent robot functional applications according to the application conditions, and can also
be expanded to multi-person–multi-robot collaborative work assisted by the sensors used by theservice robots, 5G, and cloud and artificial intelligence technologies in a large working area.
However, owing to the short duration of the cabin hospitals, a large part of this study is still in its
conception. In the future, several multi-person–multi-robot collaborative work application
scenarios could be further considered, such as the following: (1) Collaborative work by multiple
robots of the same type. For example, disinfection robots may work in coordination to perform
spraying and disinfecting tasks, where the work area may be distributed among the robots for
efficiency and efficacy. (2) Collaborative work by multiple robots of different types. For
example, a temperature-measuring robot may work with a reception robot. When thetemperature-measuring robot detects that a patient has a fever, the cloud will command thenearest reception robot to ask questions and answer the questions from patients, understand thepatient’s current state, and calm the patient. Based on the proposed framework, these concepts
could be implemented by task scheduling in the application management module according to
specific application requirements. With the development of new sensors, we will continue toimprove the proposed cloud-based human-centered multi-robot collaboration architecture, trackand observe the global development of the COVID-19 epidemic, and develop and deploy optimal
applications according to the progress of the epidemic. We hope that our work will inspire otherresearch groups.

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About the Authors
Sheng Bao received her bachelor’s (1998), master’s (2000), and Ph.D. (2004) degrees from
Harbin Institute of Technology, China. She worked as postdoctoral researcher (2006) at Tsinghua
University and as an assistant professor (2009) at Shanghai Robotics Institute, Shanghai
University. She was a visiting scholar of Vanderbilt University in 2014. Her current researchinterests include kinematic and dynamic algorithms of manipulators and joint servo driving andcontrol. (baos@shu.edu.cn)
Bill Huang received his B.S. degree from Huazhong University of Science and Technology,
China, in 1982. He worked at Bell Laboratories in the United States and was a senior vicepresident and chief technology officer of UT Starcom until 2006. He was president of China
Mobile Communications Research Institute in 2007. He is the founder of CloudMinds
Technologies Inc. and has been its CEO since 2015. He is committed to developing cloud
intelligent robot technology. (bill@cloudminds.com)
Jianjun Yuan has been working as a professor and as deputy director of Shanghai Robotics
Institute, Shanghai University, since 2019. He received his bachelor’s (1999) and master’s (2001)
degrees from Tsinghua University, China, and his Ph.D. (2005) degree from Tokyo Institute ofTechnology, Japan. He worked as a postdoctoral researcher (2006) at Tokyo Institute ofTechnology and as an associate professor (2006–2019) at the Research Institute of Robotics,
Shanghai Jiao Tong University. He was a visiting scholar of CMU in 2014 and a Pu Jiang Scholarof Shanghai City in 2007. His research interests include innovative design, manipulators andapplications, kinematic and dynamic algorithms, and special-needs robotics. He is a senior
member of IEEE. (terabit@shu.edu.cn)
Bing Wang received his MBA degree from the University of Hertfordshire, UK, in 2010. Since
graduating in 1988, he has been engaged in network security research and is a leader of a teamcreating a new security mechanism used in a variety of software systems. He is currently
building a security system applied to cloud robots. (charly@cloudminds.com)
Lin Xia received his B.S. degree from Shanghai Jiao Tong University in 1998. Since his firstproject, he has focused on telecoms and software platforms. In 2016, he joined an AI team thatapplies AI technology to life services. His main research directions are better human–robot
interaction and smarter AI based on cloud robot solutions. (linairy@163.com)
Mingyuan Wang received his B.S. and M.S. degrees from Shanghai University of Engineering
Science, China, in 2017 and 2020, respectively. He is currently working toward his Ph.D. degree
at Shanghai Robotics Institute, Shanghai Key Laboratory of Intelligent Manufacturing andRobotics, Shanghai University. His research interests are in mechanical design and teleoperation robotics. (wmy20008@163.com)

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Sensors and Materials, Vol. 33, No. 9 (2021)

Yanchen Liu received her B.S. degree from Xidian University in 2014. From 2015 to 2018, she
worked as a communication engineer, designing a communication system used between ships
and land command centers. Since 2019, she has been researching communication systems usedby cloud robots. (Vicky.liu@cloudminds.com)