Fog Robotics: An Introduction
Siva Leela Krishna Chand Gudi, Suman Ojha, Benjamin Johnston, Jesse Clark and Mary-Anne Williams
Abstract— Cloud Robotics (CR) is an emerging and success-
ful approach to robotics. The number of robots or other IoT
devices may increase drastically in the future which might need
enormous bandwidth and there might be security concerns. If
robots in CR are not secured then robots can even become
surveillance bot by hackers. Moreover, if an internet connection
is lost due to network hitches then in that crucial moment robot
may not be available to complete its given task. For example,
a robot assisting a person can stop working unexpectedly or
work with the instructions from hacker. In order to address
such problems, we propose a new approach to robotics - Fog
Robotics (FR) in this paper, so a network of robots can be used
more securely and efﬁciently as compared to CR.
Cloud Robotics (CR) was coined by J. Kuffner claiming
robots could be independent with no limits towards process-
ing and computation power . CR provides on demand
tasks/ services, large data storage related to maps, object,
data, images, libraries, trajectories etc. all together in one
place. It was also claimed that it could reduce burden on
robots as they share information from the cloud. But right
now, the situation is changing related to security and speed,
and a new method should be available to tackle this issue.
Using the well-established concept of Fog Computing ,
we present the concept of Fog Robotics(FR) in this paper. It
can be deﬁned as an architecture which consists of storage,
networking functions, control with decentralized computing
closer to robots. Since this is a short introduction, our
discussion will mainly revolve around the advantages of FR.
II. FOG ROBOTICS ARCHITECTURE
The Fog Robotics architecture consists of a Fog Robot
Server (FRS), a Robot, and a Cloud system(Fig. 1). If a
robot requests data then it will ﬁrst query the FRS and upon
availability, it can be directly used without involving the
cloud. If not, then the cloud will be utilized. It can teach
robots by sharing dynamic data with neighboring robots,
such as to be careful while moving towards a room as
recently one robot got crashed. FR improves security by
allowing sensitive information to be shared without requiring
it to be sent over external networks.
An FRS contains a knowledge base, computing resources,
environment models, recent robot outcome results such as
*This research was supported by Innovation and Enterprise Research
Laboratory (The Magic Lab), Centre for Artiﬁcial Intelligence, University
of Technology Sydney, Australia
S.L.K.C. Gudi, S. Ojha, B. Johnston, J. Clark, M.A. Williams
are with the Innovation and Enterprise Research Laboratory (The
Magic Lab), Centre for Artiﬁcial Intelligence, University of Tech-
nology Sydney, 81 Broadway, Sydney NSW 2007, Australia. (e-
email@example.com, firstname.lastname@example.org, email@example.com).
maps, user details, deep learning models, etc. It works as a
bridge between the robots and the cloud. It reduces band-
width and processing burden on cloud servers by processing
Fig. 1. Architecture of Fog Robotics.
Service is delivered with the help of a local FRS which
makes latency and delay in jitter low. The distance between
client and server is near (probably one hop) based on the
circumstances such as the location of robot. FRS nodes
will be high in number, providing mobility and wireless
connectivity for all kinds of real time interactions. It is more
secure and hard to hack as they are not directly connected
to cloud. This whole process involves observing, realizing,
sharing, and reacting based on the condition of robot.
An important application of FR can be seen in airports.
For example, if we consider a person asking a robot about
his departure place, then it can guide him until the escalator
and hand over its job to another robot waiting on the other
side of the escalator. The other robot will guide the person
to their destination. This process involves saving the persons
name, face, gender, shirt color, and age; and this helps the
second robot to recognize the person. In a similar way, FR
techniques can be applied in hotels, universities, subways,
bus terminals, train stations, homes and the list goes on.
 J. Kuffner, “Cloud-enabled humanoid robots,” in Humanoid Robots
(Humanoids), 2010 10th IEEE-RAS International Conference on,
Nashville TN, United States, Dec., 2010.
 A. V. Dastjerdi, H. Gupta, R. N. Calheiros, S. K. Ghosh, and R. Buyya,
“Fog computing: Principles, architectures, and applications,” in Internet
of Things. Elsevier, 2016, pp. 61–75.