SUGISAKAMASANORI

Journal of Robotics, Networking and Artificial Life
Vol. 9(1); June (2022), pp. 49–53
ISSN (Online): 2352-6386; ISSN (Print): 2405-9021
https://alife-robotics.org/jrnal.html

Research Article

Discovering Future
Technologies

DirectionalExpectationsofRobot

Takao Ito1, Tsutomu Ito2

Graduate School of Advanced Science and Engineering, Hiroshima University,1-4-1, Kagamiyama, Higagshi Hiroshima, 739-7527, Japan
Department of Business and Administration, Ube National College of Technology, 2-14-1 Tokiwadai, Ube, Yamaguchi, 755-0096, Japan

12ARTIC LE INFO
Article History
Received 24 November 2021Accepted 22 April 2022

Keywords
networking robot systems
swarm robotics
multiple-goal-oriented approaches
fragility

ABSTRACT

Robot technologies have been changed dramatically with rapid development of the Internettechnology. Having reviewed the relevant literature on advanced robot technologies, it is readily
evident that most of typical robot technologies are used in single-cause-oriented products, suchas robot vacuum cleaners and Asimo, a humanoid robot invented by Honda, which have played
an important role in modern society. For further development, networking robot systems
comprised of advanced Internet and artificial intelligence technologies are required to deal withfuture uncertainties. Different technologies should be combined and linked together for multiplegoal-oriented approaches associated with networking robot systems. For evaluating the validityof the proposed new system, a novel approach referred to as the fragility index is introduced inthis research© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

of robot technologies by: (1) Identifying potential future
development directions, and (2) Proposing a new index tomeasure its systematic structure that ascertains direct
bearings on its future developmental directions.
This manuscript is structured as follows: Section 2shows the background of this research with literature
review. Section 3 introduces networking robot systems,
and calculates its structure using a new index known asfragility. Based on the empirical findings, limitations of
this research are described and directions for future
research are explained in the final section.

1. Introduction
A plethora of studies on robot technologies from theviewpoint of materials engineering, information
processing technologies, such as pattern recognition,
image processing, artificial intelligence, network
analysis, and biotechnology have been the subject of
research investigation in the past decades. However,researches focusing on the review of current robot
technologies from interdisciplinary approach that seek tofurther clarify future developmental trends are sparse.
Accordingly, this paper proposes a new networking robot
system and a crucial index to measure its systematic
structure after reviewing the relevant literatures. Thus, bybridging the gap in own knowledge, the contribution ofthis paper to the literature by focusing on the development
Corresponding author’s E-mail: itotakao@hiroshima-u.ac.jp

2. Background
Robots are programmable machines built to assist
humans or mimicking human actions while the field of
robotics lies at the intersection of science, engineering and49

sliding mode controller is designed recently. In 2021,
Duan et al., proposed a new control scheme and verified
that it will reduce the chattering effectively and render thehigh-precision tracking performance [5].
The quadruped robots are considered as the effective
tools for space exploration, military application, industrial
application, and many more in different practical situations.
Kitani et al., proposed the asymmetric amplification of theoutput waveforms of central pattern generators forexcessive vibrations of quadruped robots in the rolldirection when turning by controlling their hip yaw joint
in 2019. They confirmed that compared with theconventional method, the proposed method will suppress
7.4% vibration in the pitch direction and 43.7% vibrationof the robot body in the roll direction [6]. A detailed survey
concentrates on various design and development
approaches for the quadrupedal robot, and environment
perception techniques have been discussed [7].
To develop automatic harvesters is another importanttask. Matsuno et al. introduced new research of tomato
harvesting robot and found smart tomato greenhouseaiming at promoting the automated tomato harvesting willsave harvesting time after detailed analysis of the results
mainly from tomato harvesting robot competition [8].
As illustrated in Figure 2, a new apple picking robot
has been introduced in Australia farm [9].

Figure 1. A small mobile robot developed by Alife Robotics
Corporation Ltd.

technology that produces machines which substitute for
and/or replicate human actions [1]. Robot technologies
have been recognized to play an important role not only inindustrial society, but also in daily life. Recently, manystudies focusing on robot technologies have beenpublished.
A tracking system, one of the important technologies
in robotics, is applied to the observing of objects on themove and supplying a timely ordered sequence of location
data for next step. Figure 1 shows a small mobile robot
with auto-tracking system developed by Alife Robotics
Corporation Ltd. in the early 90s. A tracking control of a
two-wheeled mobile robot in both kinematic and dynamic
models has been investigated [2]. Chen and Jia suggest touse differential flatness and PD-spectral theory forcontroller design and proved the effectiveness of their
proposed method using simulation techniques in 2014.
Another problem of sensor selection for maneuveringtarget tracking in the cluttered environment also has been
investigated. More specifically, by modeling the target
dynamics as jump Markov linear systems, Li and Jia
developed a decentralized tracking algorithm by applying
the extended Kalman filter and the probabilistic dataassociation technique in 2016 [3]. Furthermore, Gholami
et al., presented an inverse kinematic controller usingneural networks for trajectory controlling of a delta robot
in real-time in 2021. They found that the error in trajectory
tracking is bounded, and the negative effect of joint
backlash in trajectory tracking is reduced in the presenceof external disturbance using the control scheme
developed by their team [4]. In addition, for an active
suspension gravity compensation system with unknown
bounds of uncertainties and disturbances, a fast terminal

Figure 2. A new apple picking robot.

Recently, molecule Robotics have been realized byusing bio molecules such as DNA and proteins. DNA andproteins are well structured with a kind of “intelligence”
for adapting to environment change. In 2018, Suzuki andTaniguchi found that DNA molecule can sense
concentration of single strand input sequence or quasiinput and chooses higher concentration one [10].
50approaches serving different goals and purposes. Based on
the aforementioned arguments, and the issues facing
modern society, networking robot systems are proposed inthis paper. The networking robot system is a novel
approach to the combination and coordination of manydifferent autonomous robots with different functions using
Internet and sensor technology to obtain a certain task.
Compared with swarm robotics, the number of
networking robot systems is limited, and its function isspecific. The basic principle of networking robot systems
is division of labor because a production system in which
each robot is assigned a specialized task.
To realize optimization of the networking robot
systems, structure analysis is required.

Advanced robots with different technologies have beendeveloped today. For instance, industrial robots are
installed successfully in the fields of welding, assembly,
material remove and part transfer in automobile industry.
Most typical robot technologies are used in single-causeoriented products, such as robot vacuum cleaners andAsimo, a humanoid robot invented by Honda.

3. Directions of Robot Technologies
For further development of robot technologies, at least tenproblems exist. They are identified in Table 1 [11].
Table 1. Ten issues existing in robot technology.
Ten issues
1
New materials and manufacturing schemes
2Bionic robot and biological hybrid robot
3Power and energy
4
Cluster robot
5Navigation and exploration
6
The artificial intelligence of the robot
7
Brain computer interface
8
Social interaction
9Medical robot
10 The ethics and safety of robot

3.2. Networking robot system and Fragility
As indexes of network structure, a large number ofindexes have been developed for efficient and effective
structure analysis. One of the most effective indexes iscentrality. Calculation of centrality depends on its
definition as multifarious (over 400) conceptualizations
have been offered [15]. For calculating the centrality of
networking robot system, degree and fragility will be
introduced as below.
As one of the basic indices of centrality, degree can becalculated as follows [16].

3.1. Swarm robotics
Swarm robotics could be considered as one of thefuture directions of robot technologies.
Representing a direction for future research, swarm
robotics become a hot issue today. Specifically, swarmrobotics is an approach for coordinating multiple robots asa system which includes large numbers of mostly simple
physical robots [12]. The difference between swarm robots
and individual robots is that a swarm will commonly
decompose its given mission to their subtasks. Comparedwith individual robots, a swarm is more flexible about
different missions and more robust to partial swarm failure[13]. One of the potential applications for swarm robotics
is in rescue missions [14]. The key point of swarm systemis the cost and miniaturization because a large number ofrobots are required in the system.
Undoubtedly swarm robotics should be an important
direction for further development in the forthcoming
decades. Not only cost and miniaturization, but also
different robots having different functions will be
combined and linked together for multiple-goal-oriented

𝐶𝐶𝐷𝐷 (𝑝𝑝𝑘𝑘 ) = ∑𝑛𝑛𝑖𝑖=1 𝑎𝑎(𝑝𝑝𝑖𝑖 , 𝑝𝑝𝑘𝑘 ).

(1)

In equation (1), i is not equal to k, and a (pi, pk) is thevalue of connection if and only if pi and pk are connectedby a connection line, otherwise a (pi, pk) is equal to zero.
Fragility is a physical term, which characterizes how
rapidly the dynamics of a material slow down as it iscooled toward the material transition [17]. Accordingly,
fragility is considered as the structure importance of anetwork. Basically, high fragility means that a slightchange caused from any part will have strong impact on its
whole system. Thus, fragility is defined as the ratio
between the entire degree and the entire degree after
moving a specific node. It is illustrated as follows.
𝐶𝐶 (𝑝𝑝)−𝐶𝐶𝐷𝐷

F(p)= 𝐷𝐷 𝐶𝐶𝐷𝐷51.

(2)

Furthermore, both correlation coefficients of the twomodels are negative. Thus, the assumption of higher
fragility having an inverse association with sales revenue
is confirmed. Thus, higher fragility is associated withlower performance as predicted. Based on our research inthe past, higher degree, higher performance holds [20].
Therefore, higher centrality, lower fragility is expected for
effective networking robot systems.

In equation (3), CD means entire degree of a given
network, and CD (𝑝𝑝̅) means Entire degree after removing
node p.
The entire network is defined as below [18].

where𝐶𝐶𝐷𝐷 =∗∑𝑛𝑛𝑖𝑖=1[𝐶𝐶𝐷𝐷 (𝑝𝑝 )−𝐶𝐶𝐷𝐷 (𝑝𝑝𝑖𝑖 )]𝑛𝑛2 −3𝑛𝑛+2.

(3)

CD (p*) ……maximum degree of node pi;
CD (pi) ……Degree of node pi;
n……number of nodes.

4. Conclusion and Future Research

Table 2. Results of fragility-sales revenue regression model.
Sales Revenue
Fragility
Partial regression coefficientStandard coefficientt value
Probability
Coefficient of determination (R2)
Multiple correlation coefficient
F value
Degree of freedom
Data number

Networking Robot System A

Networking Robot System B-1996633-0.1262-1.09450.27730.015930.12621
1.19787
1, 7476-29733934.04-0.7798-12.0763
00.60807
0.77979145.83724
1, 94
96

In this paper, a plethora of literature of robotics andnetwork analysis have been reviewed. However, forbuilding networking robot systems, multiple-goal-orientedrobots, instead of conventional single-cause-orientedproducts are required in the future. For an effective
networking robot system, it is important to connect witheach other, but much more important issue is how toconnect with each other, and how to find the best structure.
Thus, a new index of network system fragility wasproposed. To find support for its validity, regression modelusing fragility and sales revenue was tested. However, here
are different perspectives such as economy, efficiency,safety, and liability to evaluate a system. To draw firmer
conclusions, future studies should be examined using
longitudinal, multi-year data. Furthermore, additional
indexes for these conclusions are required.

To illustrate the validity of fragility, two famous
networking systems have been selected [19]. Data weredrawn from two typical network systems: networkingrobot system A and B. System B is a famous organization
for its success compared with system A. Data on system Ain 2007 is illustrated as Figure 3.

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Figure 3. The structure of network robot system A.

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Authors Introduction
Dr. Takao Ito

He is Professor of Management ofTechnology (MOT) in Graduate SchoolofEngineering
atHiroshima
University. He has published numerous
papers in refereed journals andproceedings, particularly in the area ofmanagement science, and computerscience. He has published more thaneight academic books including a book on Network
Organizations and Information (Japanese Edition). His
current research interests include automata theory, artificialintelligence, systems control theory, and complex systems
theoryDr. Tsutomu Ito

He is Assistant Professor ofDepartmentofBusiness
Administration at National Institute ofTechnology, Ube College, Japan. He
has published many papers in refereed
journals and proceedings, particularlyin the area of industrial management,
and computer science. His current
research interests include internet ofthings (IoT), mechanical engineering, artificial intelligence(AI), automata theory, and quantitative analysis of Japanese
Keiretsu.

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