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Rahmath Ulla Baig
Shaik Dawood
Mohamed Mansour
Tarik Tawfeek
https://doi.org/10.21278/TOF.44302
ISSN 1333-1124
eISSN 1849-1391
IDENTIFYING AND PRIORITISING FUTURE ROBOT CONTROL
RESEARCH WITH MULTI-CRITERIA DECISION-MAKING
Summary
The gap between researchers who carry out scientific exploration and practitioners who
can make use of the research results is well known. In addition, while practitioners place a
high value on research, they do not read many research papers. This paper attempts to define
and prioritise future research in robotics using the analytical hierarchy process (AHP). Fifteen
research alternatives and gaps, five performance criteria, eight industry types, and six
production processes, investigated by both academics and practitioners, are filtered to six
alternatives, four performance criteria, three industry types, and three production processes,
respectively, based on the most important factors in decision-making. Subsequently, they are
analysed by the Expert Choice software. This research aims at bridging the gap between
academics and practitioners in robotics research and at conducting research that is relevant to
industry. The results indicate that the research in multi-robot control ranked first with 26.8%,
followed by the research in safe control with 23.3% and the research in remote robot
supervision with 19.0%. The research in force control ranked fourth with 17.8%, followed by
the research in 3D vision and wireless communication with 8.4% and 6.4%, respectively.
Based on the results, the academics involved in robotics research should direct their effort to
the research activities that received the highest priority in the AHP model.
Key words: future research in robotics, multi-criteria decision-making, analytical
hierarchy process, qualitative analysis
1. Introduction
Today, much scientific research focuses on multi-robot control, safe control, force
control, remote robot supervision, and wireless communication [1]. Prioritising research
directions occurs in health care sciences and services [2], health policy and services, and
economics, whereas there is no research focusing on future directions in the research into
robotics control. According to the web of science core collection search [3], in the period
1905-2019, there have been 74,153 studies with a title identifying and prioritising future robot
control research with multi-criteria decision-making (MCDM). 52% of the research include
proceeding papers and 42% articles; 80% of them were published between 2008 and 2018.
95% of the studies were conducted in 24 countries, where the USA, China, and Japan excelled
and Iran and Turkey were the only Middle Eastern countries contributing to the robotics
control research.
R. Baig, S. Dawood, Identifying and Prioritising Future Robot Control
M. Mansour, T. Tawfeek Research with Multi-Criteria Decision-Making
The presence of a research plan in robotics control is valuable in guiding the efforts of
researchers in applying the results of scientific research to industrial evolution. This area
needs specialized research to be directed and carried out. This would allow specialized areas
and their associated sub-disciplines, which determine the areas of scientific interest over time,
to be prioritised compared to other area specializations. In this way, growing demands or
emergency events occurring in society can be addressed. Prioritising research directions can
assist in finding research topics for the next planning horizon, in directing financial support to
areas of research preferred by community without neglecting other key scientific areas, in
encouraging the formation of scientific research teams and centres, encouraging practical
multidisciplinary engineering disciplines, and supporting scientific publishing. The
prioritisation scheme is intended to rank a subset of alternatives with respect to a pre-defined
set of criteria for complex decision-making problems.
We can consider the aspects of scientific research in the field of robotics control from
several points of view, such as robot types, performance criteria, industry types, and
production processes. Most research found in robotics control literature concentrates on one
or two aspects by dealing with a single problem, such as the study by Xue et al. [4]. The
research can have one or more objectives. In the case of multiple objectives, the researcher
attempts to find a satisfactory trade-off between many conflicting criteria and achieve the best
solution. The pairwise comparisons between alternatives in prioritising research directions can
be based on objective and/or subjective attributes. Objective attributes are numerical, such as
the cost of a robot. Subjective attributes are qualitative, such as the programming flexibility of
a robot. Additionally, the pairwise comparisons are based on beneficial and/or non-beneficial
attributes. Beneficial attributes are those whose higher values are desirable, such as load
carrying capacity, programming flexibility, and speed. The non-beneficial attributes are those
whose lower values are desirable, such as cost and repeatability. When prioritising one robotic
control research direction over another, the decision maker seeks to maximize the
performance measure through altering the attributes.
The robot plays a very important role in modern manufacturing industries [5], where it
is a major asset in key manufacturing processes, such as painting and welding in the
automotive industry [6], metal cutting and forming in the assembly industry [7], and labour-
related tasks in dangerous work areas. Moreover, in the space industry robots play an
important role in imaging and investigating the characteristics of components in space
research [8]. The heavy use of robots in industrial applications results from their stable quality
and productivity level, cost minimization, and safety improvements in dangerous
environments [9]. The selection of robots for a particular application and manufacturing
environment is a difficult task for decision makers. Robot selection has become more
complicated due to the increase in complexity, advanced features, and facilities of robots. The
decision maker must identify and select the best-suited robot to achieve the desired output
with respect to many criteria. MCDM is a complex decision-making tool involving both
quantitative and qualitative factors. In recent years, several MCDM techniques and
approaches have been utilised for choosing optimal probable options. Robotics research area
alternatives can be defined by solving a certain problem related to functionality to optimize
criteria for a certain application by specifying a manufacturing process type.
MCDM is a complex technique that defines criteria weights, the relationship between
alternatives, and solution trade-offs through mathematical techniques [10]. The analytical
hierarchy process (AHP) is an MCDM technique that enables the decision maker to choose
one or more trade-off scenarios suitable to available resources. It has various applications
from the construction industry [11], network selection and supply chain management [12] to
healthcare services [13], material selection [14], risk assessment [15] and energy
management.
Identifying and Prioritising Future Robot Control R. Baig, S. Dawood,
Research with Multi-Criteria Decision-Making M. Mansour, T. Tawfeek
Robot selection for a given industrial application is a challenging and complicated
procedure. In robot selection research, alternatives are clearly defined for finding solutions to
certain problems, which refers to optimizing predefined criteria related to manufacturing area
to choose the appropriate robots. Koulouriotis and Ketipi [16] provided a comprehensive
integrated review and annotated taxonomy for robot evaluation and selection until 2014.
Parameshwaran et al. [17] presented an integrated approach for optimal robot selection by
considering both objective and subjective criteria by sequentially using the fuzzy
Delphi/AHP/TOPSIS framework and VIKOR validation and the Brown-Gibson model.
VIKOR is closest to the ideal solution. Sen et al. [18] explored the preference ranking
organization method for enrichment evaluation as an efficient decision-making tool which
provides the complete ranking order of all available alternatives. Sen et al. highlighted the
application potential of preference ranking organization methods in relation to the robot
selection problem when subjected to a set of quantitative evaluation data collected from the
literature.
Simion et al. [19] applied the AHP method to select the most favourable industrial robot
configuration for arc welding of a tracked mini-robot housing, which is used in military
applications. The study highlighted the usefulness of the AHP in automated technological
processes to facilitate and simplify the robot selection from a set of alternatives that carry out
one or more tasks. Wang et al. [20] explored two key issues of robot evaluation and selection:
assessment representation and robot rankings. Wang et al. developed a decision support
model which utilised a cloud model and TODIM (a Portuguese acronym equivalent to
interactive multi-criteria decision-making) for robot selection with hesitant linguistic
information, which was used along with an entropy-based combination weighting technique
to estimate the criteria weights. The TODIM approach is proposed for robot selection for
automobile manufacturers, which validates the benefits and effectiveness of a robot through a
comparative analysis. The results highlighted its unique advantages in a complex
environment. Zhou et al. [21] proposed a VIKOR-based implementation method for
integrating a fuzzy extended analytical hierarchy to select the mobile robot option with the
smallest fuzzy utility value. The results highlight the sensitivity analysis, effectiveness, and
robustness of the VIKOR extended approach.
Liu et al. [22] proposed a novel robot selection model by integrating the quality function
development (QFD) theory and the qualitative flexible multiple criteria method under the
interval-valued Pythagorean uncertain linguistical context. A modified qualitative flexible
multiple criteria technique is utilised to generate the ranking order of alternative robots to
determine the most suitable robot. Narayanamoorthy et al. [23] proposed using interval valued
intuitionistic hesitant fuzzy entropy to determine the importance of criteria and the interval
valued intuitionistic hesitant fuzzy VIKOR method to rank alternatives to perform
complicated and hazardous tasks. The interval-valued intuitionistic hesitant fuzzy set includes
a set of several possible interval-valued intuitionistic fuzzy membership and non-membership
values to enhance the quality and efficiency of the work.
Kapoor and Tak [24] used the AHP method with selection criteria factors such as cost,
velocity, repeatability, load capacity, stability, compliance, and accuracy. Chatterjee [25] used
the AHP method with selection criteria factors such as load capacity, repeatability, maximum
tip speed, memory capacity, and manipulator reach. Tansel [26] also used the AHP method
with selection criteria factors such as pay load, repeatability, vertical reach, robot mass, axis
motion range, and maximum axis speed. Abdel-Malek [27] used the decision support system
method with selection criteria factors such as robot type (articulated robots, SCARA robots,
etc.), degrees of freedom, pay load, horizontal reach, vertical reach, velocity, repeatability,
and power supply. Bhangale et al. [28] utilised the optimal reciprocal collision avoidance and
the technique for order of preference by similarity to ideal solution (TOPSIS) method with
R. Baig, S. Dawood, Identifying and Prioritising Future Robot Control
M. Mansour, T. Tawfeek Research with Multi-Criteria Decision-Making
selection criteria such as load capacity, repeatability, maximum speed, type of drive, memory
capacity, manipulator reach, and degree of freedom. Tao et al. [29] used the data envelopment
analysis, AHP, TOPSIS, and axiomatic fuzzy set (AFS) theory selection criteria such as cost,
load capacity, velocity, and repeatability error. Karsak [30] used QFD and the fuzzy linear
regression method along with selection criteria such as load capacity, repeatability, vertical
reach, horizontal reach, and warranty period.
Vahdani et al. [31] used fuzzy TOPSIS selection criteria such as purchase cost, load
capacity, and positioning accuracy. Bhattacharya et al. [32] highlighted the QFD and AHP
drive system, geometrical dexterity, path measuring system, robot size, robot material, robot
weight, initial operating cost, pay load, accuracy, life-expectancy, robot velocity,
programming flexibility, and total cost. Karsak et al.[33] used a fuzzy regression-based
decision-making approach and selection criteria such as cost, velocity, repeatability, and load
capacity. Chatterjee et al. [34] used the VIKOR and ELECTRE methods and selection criteria
such as velocity, load capacity, cost, repeatability, vendor service quality, and programming
flexibility. Kumar and Garg [35] proposed a distance-based approach, with selection criteria
such as velocity, repeatability, load capacity, degree of freedom, stability, compliance, and
accuracy. Koulouriotis and Ketipi [36] used fuzzy digraph and matrix methods and selection
criteria such as man–machine interface, programming flexibility, repeatability error, purchase
cost, and velocity.
In this study, after a rigorous review of articles addressing the application of MCDM to
selection problems in different areas, such as robot selection, site allocation, knowledge
management and supply chain design, the concomitant problems and opportunities that exist
in implementing the AHP in prioritising future robot research are revealed. Based on the
existing literature, it is evident that no studies integrate the MCDM technique in prioritising
research directions, especially in the field of robotics control and in directing scientific
research in general. Accordingly, this study seeks to motivate researchers to further explore
state-of-the-art tools, including MCDM, and their applications in directing scientific research
activities. The main objective of this research is to disseminate researcher approaches toward
improving strategies, tools, and techniques of MCDM in the scientific research sector with a
particular focus on its implementation.
The objective of this study is to prioritise research directions in the field of robotics
control based on academics’ and practitioners’ opinions. This research adopted the general
AHP approach introduced by Saaty [37]. The adopted hierarchy includes levels of goals,
criteria, sub-criteria, and processes for alternative research topics. This study allows
researchers in the robotics control domain to strengthen and focus their efforts for industry
purposes and to reduce the inherent gap between academic research and industrial needs. It
directs the budding robotic research efforts in the developing countries to the area of using
robots in automation. Given the problem criteria, application field, processes, and robotics
research alternatives, we hypothesized that researcher’s and industrial expert’s preferences are
not equal for each research direction in the field of robotics. Since this problem is not
examined in details in the literature, we focused on solving the prioritisation problem using an
MCDM and presented results to direct the research efforts and optimize the resources
available in a certain planning period to create a research plan. This type of problem treatment
presents a new way of conducting robotics research by focusing and directing its efforts.
This paper is composed of four sections. In the second section, the adopted AHP model
is illustrated. The third section applies the solution methodology to prioritise future research
in robotics control based on the opinion and judgement of academic and industrial experts and
the results are discussed. The fourth section provides conclusions and recommendations for
future research.
Identifying and Prioritising Future Robot Control R. Baig, S. Dawood,
Research with Multi-Criteria Decision-Making M. Mansour, T. Tawfeek
2. Materials and methods
In this study, the Saaty [37] procedure was implemented to design and develop an AHP
model. The efficacy of the AHP model depends on the diversity and expertise opinion of the
population, hence a diverse population from both the academic and the industrial sector was
established, and a sample of people from both developed and developing countries were
selected as illustrated in Table 1, where na and ne represent the number of academic and
industrial expertise participants for a corresponding country, respectively. The number of
participants is indicated in the ordered pair in parentheses. The total number of respondents
was 629, however, there were 548 completed and validated questionnaires including 377
academics and 274 practitioners from 24 countries. The questionnaires were prepared online
for respondents. The academics were selected based on their publication of robotic research
articles in renowned journals, and industrial experts were contacted based on their
involvement with pioneer robotics companies that have existed for over a decade. Participants
were contacted, and their consent for the participation in the study was obtained via email.
The responses are protected and will not be revealed unless a written consent from the
respondent is acquired. Expert Choice [38] was used to conduct the AHP, and IBM SPSS
V25.0.0 was used to calculate the geometric mean of the collected data.
Table 1 Sample characteristics
Country (na,ne) Country (na,ne) Country (na,ne)
People's Republic
of China
(12,13) France (21,13) Turkey (14,9)
USA (9,7) Canada (24,19) Australia (12,10)
Japan (28,23) India (23,18) Taiwan (18,14)
Germany (15,11) Spain (18,10) Switzerland (5,4)
Italy (12,7) Iran (19,15) Russia (10,8)
South Korea (11,8) Singapore (20,14) Mexico (18,13)
England (14,12) Netherlands (15,10) Sweden (12,8)
Brazil (20,13) Portugal (13,4) Malaysia (14,11)
The AHP was executed in Expert Choice, a widely-used MCDM software package.
This process translated the answers of participants to the questions regarding the pairwise
comparisons of level alternatives with the next highest corresponding level in the hierarchy.
Five online 60-80-minute group sessions with a panel of experts were conducted to validate
and refine the decision context, model content, and hierarchy. A questionnaire in the form of a
comparison matrix was used to collect participant opinions. Fig. 1 shows the online
questionnaire created to measure the relative pairwise comparison matrix of the main criteria
with respect to the goal. Each participant decided on the relative pairwise comparison value of
the main criteria with respect to the goal. It was required to circle a number per row using a
scale value range from 1 to 9. The “1” value indicates that the two alternatives have equal
importance regarding the higher hierarchy level, while “3” indicates a moderate importance
and “9” represents an extreme importance. Fig. 2 illustrates the relative pairwise comparison
matrix of the main criteria with respect to cost, while Fig. 3 shows the relative pairwise
comparison matrix of the main criteria with respect to automotive industry. 410 participants
filled the pairwise comparison matrices, and the data were entered into the IBM Statistics v
25.0.0 software to calculate the geometric mean, which was then entered into Expert Choice.
We used the ideal synthesis mode, which is designed to identify the single best alternative or
the most important criterion [39].
R. Baig, S. Dawood, Identifying and Prioritising Future Robot Control
M. Mansour, T. Tawfeek Research with Multi-Criteria Decision-Making
A sensitivity analyses was performed to understand the impacts of objectives and
weights on the results. Minimizing the cost, maximizing the tip speed/velocity, maximizing
the robot repeatability, and maximizing the manipulator reach priorities were increased to
100%, and the analysis was conducted in the distributive mode. The distributive mode
produced results that evaluated alternatives or criteria proportionately. The consistency index
was used to evaluate the transitivity of the weights [40]. An inconsistency percentage of less
than 10% was accepted by the general AHP theory as defined by Saaty.
Fig. 1 Relative pairwise comparison matrix of main criteria with respect to goal.
Fig. 2 Relative pairwise comparison matrix of main criteria with respect to cost.
Fig. 3 Relative pairwise comparison matrix of main criteria with respect to automotive industry.
3. Results and discussion
When using a descriptive analysis for the sample presented in Table 1 in terms of mean,
standard deviation (SD), minimum value (Min), maximum value (Max), median (Med), and
mode, it is noted that a) the observations for na had a mean of 15.71, SD of 5.32, Min of 5,
Max of 28, Med of 14.50, and mode of 12.00, and b) the observations for ne had a mean of
11.42, SD of 4.51, Min of 4.00, Max of 23, Med of 11.00, and mode of 13.00. The number of
academic participants was higher than the number of industrial experts in all countries that
participated in the questionnaire by an average value of 4.29.
3.1 Priorities of the main criteria with respect to goal
As depicted in Table 2, repeatability has more weight than cost, speed, and robot reach.
This result indicates that a robot’s ability to return to the same position repeatedly is a vital
indicator for robotic research as industrial robots have to repeatedly perform the same task
millions of times [17]. Cost of the robot is ranked second, as cost is a deciding factor for
many of the small and medium-sized enterprises in their decision whether to utilise a robot for
their operations. It has been a constant endeavour of robot suppliers and researchers to create
a cost-effective robot. The speed and reach of the robot falls further on the priority list; if a
robot possesses high repeatability with cost effectiveness, then its speed should also be high
to complete the task in less time and with a higher manipulator reach.
Identifying and Prioritising Future Robot Control R. Baig, S. Dawood,
Research with Multi-Criteria Decision-Making M. Mansour, T. Tawfeek
3.2 Priorities of the sub-criteria with respect to cost
Robot selection based on cost is shown in Table 2. The electric and electronic
industries, which have a market share of 32% (according to the International Federation of
Robotics Report), require cost-effective robots. As these industries require products which
possess an economic advantage over their competitors, for the products to be delivered to
customers, cost has a greater weight compared to other industries. In the electric and
electronic industries, the key operation that robots perform is assembly of electronic
components, as depicted by its weight of 65.9%. Next in the cost criterion is the automotive
industry where welding is the primary operation for which robots are purchased because of
the need for high weld quality and consistency [41]. Assembly robots have revolutionized the
automotive industry. They were the first robots which found acceptance in the industry and to
this day they drive research as the complexity of products to be assembled is constantly
increasing. The metal and machine industry, which is prioritised third in the selection of
robots with reference to cost, requires welding operations to be automated using robots and
has a weight of 68.3%.
The scope of the robotic research in industry with cost as the main criterion includes
multi-robot control, force control, safe control, remote robot supervision, 3D vision, and
wireless communication. Welding robots play a critical role in the automotive and metal and
machine industries; hence, the multi-robot control of welding robots is ranked high in the
robotic research areas. Assembly robots are imperative in the electric and electronic industries
and force control [42] in assembly lines has also great potential.
3.3 Priorities of the sub-criteria with respect to maximum tip speed/velocity
The speed of the robot is taken into consideration while selecting a robot in an industry
as it affects productivity of a firm. The priority of speed is weighted third in the main criteria
for robot selection. An increase in speed significantly affects the repeatability of the robot
[43]; hence, in industries such as electric, electronic, and automotive where repeatability is a
prime factor in efficient operation speed takes a back seat as depicted by the weights. Speed is
distinctly prioritised in the metal and machine industries for welding operations [44, 45]. It is
also valued in assembly operations in the electric, electronic, and automotive industry. The
ranking of robotic research areas with speed as a criterion is safe control (speed with safety),
remote robot supervision, force control, wireless communication, multi-robot control, and 3D
vision. The study of speed for remote robot supervision is vital as is indicated by the rankings;
a supervising robot with high speed and safety requirements is a Herculean task for
researchers.
3.4 Priorities of the sub-criteria with respect to repeatability
Repeatability of operations is of prime importance to automotive, electric and electronic
industries compared to metal and machine industries as designated by the weights of the sub-
criteria. Welding an automobile requires producing precise welds, and welding robots are
required to execute this repeatedly [45], which is depicted by the higher weight of 64.9%.
Assembly operations of electric and electronic components also require a high degree of
repeatability as systems continue to mature with stringent requirements, which opens up areas
of research for researchers in multi-robot control, safe control, and remote robot supervision.
Repeatability in the metal and machine industries is essential with welding and the assembly
of components compared to material handling. The scope of robotic research with
repeatability as the criterion includes multi-robot control, safe control, remote robot
supervision, force control, 3D vision, and wireless communication.
R. Baig, S. Dawood, Identifying and Prioritising Future Robot Control
M. Mansour, T. Tawfeek Research with Multi-Criteria Decision-Making
3.5 Priorities of the sub-criteria with respect to manipulator reach
Manipulator reach is the least favoured criterion compared to repeatability, cost, and
speed. Robot reach primarily refers to grasping the components in the metal and machine
industry [46] and to reaching out to extreme points to perform welding operations.
Correspondingly, in the automotive industry, the reach of the robot is of utmost importance
for welding operations [47], whereas robot reach in the electronic and electrical industries is
used for assembly operations requiring dexterity [48]. The hierarchy of robotic research areas
regarding robot reach as the main criterion is multi robot control, force control, safe control,
remote robot supervision, 3D vision, and wireless communication. Based on the AHP
comparison of the overall weights, the hierarchy of robotics research areas in order of overall
priority are: multi-robot control (0.268), safe control (0.233), remote robot supervision
(0.190), force control (0.178), 3D vision, and wireless communication.
Table 2 Results of pairwise comparisons of the total weights of alternatives using Expert Choice
Criteria Criterion
weight
Sub-
criteria
Local
sub-
criterion
weight
Sub-
subcriterion
Local
sub-
subcriterion
weight
Global
sub-
subcriterion
weight
Robot research area alternatives
Multi-
robot
control
(612)
Safe
control
(56)
Force
control
(495)
3D
vision
(70)
Remote
robot
supervision
(2)
Wireless
communication
(66)
Cost
0.334
Auto 0.327
W 0.570 0.062 0.021 0.018 0.008 0.006 0.006 0.004
A 0.333 0.036 0.012 0.003 0.011 0.004 0.004 0.002
M 0.097 0.011 0.002 0.003 0.002 0.001 0.001 0.001
Electr 0.413
W 0.185 0.026 0.009 0.005 0.004 0.003 0.003 0.001
A 0.659 0.091 0.027 0.010 0.023 0.008 0.016 0.006
M 0.156 0.022 0.001 0.006 0.002 0.003 0.007 0.003
Metam 0.260
W 0.683 0.059 0.023 0.010 0.009 0.005 0.010 0.003
A 0.200 0.017 0.007 0.003 0.002 0.002 0.003 0.001
M 0.117 0.010 0.001 0.003 0.003 0.001 0.002 0.001
Cost total 1.000 3.000 0.334 0.103 0.062 0.063 0.033 0.052 0.022
Cost rank 1 3 2 5 4 6
Max tip speed/velocity
0.136
Auto 0.345
W 0.218 0.010 0.001 0.004 0.002 0.001 0.001 0.002
A 0.715 0.034 0.003 0.011 0.006 0.003 0.006 0.006
M 0.067 0.003 0.000 0.001 0.001 0.000 0.001 0.000
Electr 0.109
W 0.333 0.005 0.000 0.002 0.001 0.000 0.001 0.001
A 0.570 0.008 0.000 0.003 0.001 0.001 0.002 0.001
M 0.097 0.001 0.000 0.000 0.000 0.000 0.000 0.000
Metam 0.547
W 0.574 0.043 0.004 0.015 0.007 0.003 0.011 0.002
A 0.361 0.027 0.003 0.009 0.005 0.002 0.007 0.001
M 0.065 0.005 0.000 0.001 0.002 0.000 0.001 0.000
Max tip speed/velocity total 1.000 3.000 0.136 0.012 0.046 0.024 0.011 0.030 0.014
Max tip speed/velocity rank 5 1 3 6 2 4
Repeatability
0.451
Auto 0.413
W 0.649 0.121 0.051 0.018 0.013 0.008 0.024 0.006
A 0.279 0.052 0.019 0.012 0.005 0.003 0.011 0.003
M 0.072 0.013 0.001 0.005 0.002 0.001 0.003 0.001
Electr 0.327
W 0.279 0.041 0.004 0.014 0.006 0.004 0.011 0.002
A 0.649 0.096 0.010 0.033 0.014 0.009 0.025 0.006
M 0.072 0.011 0.004 0.002 0.001 0.001 0.002 0.000
Metam 0.260
W 0.582 0.068 0.030 0.012 0.008 0.004 0.012 0.002
A 0.348 0.041 0.006 0.003 0.020 0.002 0.008 0.001
M 0.069 0.008 0.000 0.004 0.002 0.001 0.001 0.001
Repeatability total 1.000 2.999 0.451 0.125 0.103 0.070 0.033 0.097 0.023
Repeatability rank 1 2 4 5 3 6
Manipulator reach
0.08
Auto 0.352
W 0.701 0.020 0.008 0.003 0.003 0.002 0.002 0.001
A 0.240 0.007 0.003 0.001 0.002 0.000 0.001 0.000
M 0.590 0.017 0.001 0.007 0.004 0.002 0.001 0.001
Electr 0.089
W 0.285 0.002 0.000 0.000 0.000 0.000 0.000 0.000
A 0.653 0.005 0.000 0.001 0.002 0.000 0.001 0.000
M 0.062 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Metam 0.559
W 0.649 0.029 0.013 0.007 0.003 0.002 0.002 0.001
A 0.279 0.012 0.002 0.001 0.006 0.001 0.002 0.001
M 0.072 0.003 0.000 0.001 0.000 0.001 0.000 0.000
Manipulator reach total 1.000 3.531 0.095 0.028 0.022 0.022 0.008 0.010 0.005
Manipulator reach rank 1 3 2 5 4 6
Grand total 0.268 0.233 0.178 0.084 0.190 0.064
Overall priority/Rank 1 2 4 5 3 6
Auto: Automotive industry; Electr: Electric/electronic industry; Metam: Metal & machine industry; W: Welding; A: Assembly; M: Material handling
Identifying and Prioritising Future Robot Control R. Baig, S. Dawood,
Research with Multi-Criteria Decision-Making M. Mansour, T. Tawfeek
4. Conclusions
This study investigated the criteria and indicators of the robotics research to concentrate
the efforts of the research community to addressing industrial needs. Diverse multi-national
experts from both industry and academia participated in the priority evaluation of the robotics
research. The level of importance of the subcriteria is based on the pairwise comparisons
conducted by experts. Based on the AHP comparison of the overall weights, the hierarchy of
robotic research areas in descending order is: multi-robot control, safe control, remote robot
supervision, force control, 3D vision, and wireless communication. This hierarchy indicates
how research efforts should be channelled out to the development of effective industrial
robotics.
The natural expansion of this research may involve exploring the applicability of using
Fuzzy-AHP, TOPSIS, and other MCDM techniques for solving the research direction
prioritisation problem. This research can also be extended to explore the relationship between
the descriptive variable as a multivariate analysis and structure equation modelling for a
cause-and-effect analysis of the factors affecting goals and objectives. Therefore, the
advantages and disadvantages of the decision-making trial and evaluation laboratory
compared to other methods remain unknown and can be further examined. Integrating the
AHP with the QFD to prioritise research direction in the field of robotics control is a
candidate research area related to the topic of this study. Availability of resources, planning
horizons, and national/international directions can also be included in future research.
Acknowledgments: The authors are grateful to anonymous reviewers and editors for their
comments and suggestions on this article.
REFERENCES
[1] Zolkiewski, S.; Pioskowik, D. Robot Control and Online Programming by Human Gestures Using a
Kinect Motion Sensor, In: Rocha Á., Correia A., Tan F., Stroetmann K. (eds) New Perspectives in
Information Systems and Technologies, Volume 1. Advances in Intelligent Systems and Computing, vol
275. Springer, Cham 2014, 593-604. https://doi.org/10.1007/978-3-319-05951-8_56
[2] Basu, A; Carlson, J.J.; Veenstra, D.L. A Framework for Prioritizing Research Investments in Precision
Medicine, Value in Health 2015, 18 (7), A729-A729. https://doi.org/10.1016/j.jval.2015.09.2777
[3] Analytics Clarivate, "Web of Science," Clarivate Analytics, 2019.
[4] Xue, Y.X.; You, J.X.; Zhao, X.F.; Liu, H.C. An Integrated Linguistic MCDM Approach for Robot
Evaluation and Selection with Incomplete Weight Information, International Journal of Production
Research 2016, 54 (18), 5452-5467. https://doi.org/10.1080/00207543.2016.1146418
[5] Francesco, P.; Paolo, G.G. AURA: An Example of Collaborative Robot for Automotive and General
Industry Applications, 27th International Conference on Flexible Automation and Intelligent
Manufacturing, 2017, 338-345, Procedia Manufacturing M. Pellicciari and M. Peruzzini, eds.
[6] Pfeiffer, S. Robots, Industry 4.0 and Humans, or Why Assembly Work is more than Routine Work,
Societies 2016, 6 (2), https://doi.org/10.3390/soc6020016
[7] Heydaryan, S.; Bedolla, J.S.; Belingardi, G. Safety Design and Development of a Human-Robot
Collaboration Assembly Process in the Automotive Industry, Applied Sciences-Basel 2018, 8 (3),
https://doi.org/10.3390/app8030344
[8] Yurdakul, M.; Ic Y.T. Application of Correlation Test to Criteria Selection for Multi Criteria Decision
Making (MCDM) Models, International Journal of Advanced Manufacturing Technology 2009, 40 (3-4),
403-412. https://doi.org/10.1007/s00170-007-1324-1
[9] Cowan, L.S.; Walker, I.D. The Importance of Continuous and Discrete Elements in Continuum Robots,
International Journal of Advanced Robotic Systems 2013, 10 https://doi.org/10.5772/55270
R. Baig, S. Dawood, Identifying and Prioritising Future Robot Control
M. Mansour, T. Tawfeek Research with Multi-Criteria Decision-Making
[10] Giuseppe, M. Multiple Criteria Decision Analysis and Sustainable Development, International Series in
Operations Research & Management Science, International Series in Operations Research &
Management Science, 2005, 953-986, New York, NY: Springer-Verlag.
[11] Santoso, I.; Suripin; Darsono, S. Review of Criteria on Multi Criteria Decision Making (MCDM)
Construction of Dams, International Journal of Geomate 2019, 16 (55), 184-194.
https://doi.org/10.21660/2019.55.87673
[12] Banasik, A.; Bloemhof-Ruwaard, J. M.; Kanellopoulos, A.; Claassen, G.D.H.; van der Vorst, J.G.A.J.
Multi-Criteria Decision Making Approaches for Green Supply Chains: A Review, Flexible Services and
Manufacturing Journal 2018, 30 (3), 366-396. https://doi.org/10.1007/s10696-016-9263-5
[13] Mutlu, M.; Tuzkaya, G.; Sennaroglu, B. Multi-Criteria Decision Making Techniques for Healthcare
Service Quality Evaluation: A Literature Review, Sigma Journal of Engineering and Natural Sciences-
Sigma Muhendislik Ve Fen Bilimleri Dergisi 2017, 35 (3), 501-512.
[14] Noryani, M.; Sapuan, S.M.; Mastura, M.T. Multi-Criteria Decision-Making Tools for Material Selection
of Natural Fibre Composites: A Review, Journal of Mechanical Engineering and Sciences 2018, 12 (1),
3330-3353. https://doi.org/10.15282/jmes.12.1.2018.5.0299
[15] Gul, M. A Review of Occupational Health and Safety Risk Assessment Approaches based on Multi-
Criteria Decision-Making Methods and their Fuzzy Versions, Human and Ecological Risk Assessment
2018, 24 (7), 1723-1760. https://doi.org/10.1080/10807039.2018.1424531
[16] Koulouriotis, D.E.; Ketipi, M.K. Robot Evaluation and Selection Part A: An Integrated Review and
Annotated Taxonomy, International Journal of Advanced Manufacturing Technology 2014, 71 (5-8),
1371-1394. https://doi.org/10.1007/s00170-013-5525-5
[17] Parameshwaran, R.; Kumar, S.P.; Saravanakumar, K. An Integrated Fuzzy MCDM Based Approach for
Robot Selection Considering Objective and Subjective Criteria, Applied Soft Computing 2015, 26 31-41.
https://doi.org/10.1016/j.asoc.2014.09.025
[18] Sen, D.K.; Datta, S.; Patel, S.K.; Mahapatra, S.S. Multi-Criteria Decision Making towards Selection of
Industrial Robot Exploration of PROMETHEE II Method, Benchmarking-an International Journal 2015,
22 (3), 465-487. https://doi.org/10.1108/bij-05-2014-0046
[19] Simion, M.; Socaciu, L.; Giurgiu, O.; Petrisor, S.M. The Selection of Industrial Robots for Military
Industry using AHP Method: A Case Study, Acta Technica Napocensis Series-Applied Mathematics
Mechanics and Engineering 2018, 61 (2), 231-240.
[20] Wang, J. J.; Miao, Z.H.; Cui, F.B.; Liu, H.C. Robot Evaluation and Selection with Entropy-Based
Combination Weighting and Cloud TODIM Approach, Entropy 2018, 20 (5), 19.
https://doi.org/10.3390/e20050349
[21] Zhou, F.L.; Wang, X.; Goh, M. Fuzzy Extended VIKOR-Based Mobile Robot Selection Model for
Hospital Pharmacy, International Journal of Advanced Robotic Systems 2018, 15 (4), 11.
https://doi.org/10.1177/1729881418787315
[22] Liu, H.C.; Quan, M.Y.; Shi, H.; Guo, C. An Integrated MCDM Method for Robot Selection under
Interval-Valued Pythagorean Uncertain Linguistic Environment, International Journal of Intelligent
Systems 2019, 34 (2), 188-214. https://doi.org/10.1002/int.22047
[23] Narayanamoorthy S.; Geetha S.; Rakkiyappan R.; Joo Y.H. Interval-Valued Intuitionistic Hesitant Fuzzy
Entropy Based VIKOR Method for Industrial Robots Selection, Expert Systems with Applications 2019,
121, 28-37. https://doi.org/10.1016/j.eswa.2018.12.015
[24] Vikas, K.; Shyam Singh, T. Fuzzy Application to the Analytic Hierarchy Process for Robot Selection,
Fuzzy Optimization Decision Making 2005, 4 (3), 209-234. https://doi.org/10.1007/s10700-005-1890-3
[25] Chatterjee, P.; Athawale, V.M.; Chakraborty, S. Selection of Industrial Robots using Compromise
Ranking and Outranking Methods, J Robot. Comput.-Integr. Manuf 2010, 26 (5), 483-489.
https://doi.org/10.1016/j.rcim.2010.03.007
[26] Tansel, Y.; Yurdakul, M.; Dengiz, B. Development of A Decision Support System for Robot Selection, J
Robot. Comput.-Integr. Manuf 2013, 29 (4), 142-157. https://doi.org/10.1016/j.rcim.2012.11.008
[27] Abdel-Malek, L.; Resare, L.J. Algorithm Based Decision Support System for the Concerted Selection of
Equipment in Machining/Assembly Cells, International Journal of Production Research 2000, 38 (2),
323-339. https://doi.org/10.1080/002075400189437
Identifying and Prioritising Future Robot Control R. Baig, S. Dawood,
Research with Multi-Criteria Decision-Making M. Mansour, T. Tawfeek
[28] Bhangale, P P.; Agrawal, V.P.; Saha, S.K. Attribute Based Specification, Comparison and Selection of a
Robot, Mechanism and Machine Theory 2004, 39 (12), 1345-1366.
https://doi.org/10.1016/j.mechmachtheory.2004.05.020
[29] Tao, L.L.; Chen, Y.; Liu, X.D.; Wang, X. An Integrated Multiple Criteria Decision Making Model
Applying Axiomatic Fuzzy Set Theory, Applied Mathematical Modelling 2012, 36 (10), 5046-5058.
https://doi.org/10.1016/j.apm.2011.12.042
[30] Karsak, E.E. Robot Selection using an Integrated Approach based on Quality Function Deployment and
Fuzzy Regression, International Journal of Production Research 2008, 46 (3), 723-738.
https://doi.org/10.1080/00207540600919571
[31] Vahdani, B.; Tavakkoli-Moghaddam, R.; Mousavi, S.M.; Ghodratnama, A. Soft Computing Based on
New Interval-Valued Fuzzy Modified Multi-Criteria Decision-Making Method, Applied Soft Computing
2013, 13 (1), 165-172. https://doi.org/10.1016/j.asoc.2012.08.020
[32] Bhattacharya, A.; Sarkar, B.; Mukherjee, S. K. Integrating AHP with QFD for Robot Selection under
Requirement Perspective, International Journal of Production Research 2005, 43 (17), 3671-3685.
https://doi.org/10.1080/00207540500137217
[33] Karsak, E.E.; Sener, Z.; Dursun, M. Robot Selection using a Fuzzy Regression-Based Decision-Making
Approach, International Journal of Production Research 2012, 50 (23), 6826-6834.
https://doi.org/10.1080/00207543.2011.627886
[34] Chatterjee, P.; Athawale, V.M.; Chakraborty, S. Selection of Industrial Robots using Compromise
Ranking and Outranking Methods, Robotics and Computer-Integrated Manufacturing 2010, 26 (5), 483-
489. https://doi.org/10.1016/j.rcim.2010.03.007
[35] Kumar, R.; Garg, R.K. Optimal Selection of Robots by using Distance Based Approach Method, Robotics
and Computer-Integrated Manufacturing 2010, 26 (5), 500-506.
https://doi.org/10.1016/j.rcim.2010.03.012
[36] Koulouriotis, D.E.; Ketipi, M.K. A Fuzzy Digraph Method for Robot Evaluation and Selection, Expert
Systems with Applications 2011, 38 (9), 11901-11910. https://doi.org/10.1016/j.eswa.2011.03.082
[37] Saaty; Thomas, L., Fundamentals of the Analytic Hierarchy Process: Springer Netherlands, 2001.
[38] Garson; David, G., "Expert Choice," 2, Expert Choice, Inc., 1997, 221-224.
[39] Forman, E.H.; Gass, S.I. The Analytic Hierarchy Process - An exposition, Operations Research 2001, 49
(4), 469-486. https://doi.org/10.1287/opre.49.4.469.11231
[40] Dolan, J.G. Multi-Criteria Clinical Decision Support A Primer on the Use of Multiple-Criteria Decision-
Making Methods to Promote Evidence-Based, Patient-Centered Healthcare, Patient-Patient Centered
Outcomes Research 2010, 3 (4), 229-248. https://doi.org/10.2165/11539470-000000000-00000
[41] Siciliano, B.; Sciavicco, L.; Villani, L.; Oriolo, G., Robotics: Modelling, Planning and Control: Springer
Publishing Company, Incorporated, 2008.
[42] Brogardh, T. Present and Future Robot Control Development - An Industrial Perspective, Annual
Reviews in Control 2007, 31 (1), 69-79. https://doi.org/10.1016/j.arcontrol.2007.01.002
[43] Offodile, O.F.; Ugwu, K. Evaluating the Effect of Speed and Payload on Robot Repeatability, Robotics
and Computer-Integrated Manufacturing 1991, 8 (1), 27-33.
https://doi.org/10.1016/0736-5845(91)90004-c
[44] Limbu, D.K.; Tan, Y.K.; Wong, C.Y.; Jiang, R.D.; Wu, H.X.; Liu, L.Y.; Kah, E.H.; Yu, X.G.; Liu, D.;
Liu, H.Z. Experiences with a Barista Robot, FusionBot, Progress in Robotics, Proceedings,
Communications in Computer and Information Science J. H. Kim, S. S. Ge, P. Vadakkepat, N. Jesse, A.
AlManum, S. Puthusserypady, U. Ruckert, J. Sitte, U. Witkowski, R. Nakatsu, T. Braunl, J. Baltes, J.
Anderson, C. C. Wong, I. Verner and D. Ahlgren, eds. 2009, 140-151
[45] Kah, P.; Shrestha, M.; Hiltunen, E.; Martikainen, J. Robotic Arc Welding Sensors and Programming in
Industrial Applications, International Journal of Mechanical and Materials Engineering 2015, 10 (1),
https://doi.org/10.1186/s40712-015-0042-y
[46] Chu, T.C.; Lin, Y.C. A Fuzzy TOPSIS Method for Robot Selection, International Journal of Advanced
Manufacturing Technology 2003, 21 (4), 284-290. https://doi.org/10.1007/s001700300033
[47] Bolmsjo, G.; Olsson, M.; Cederberg P. Robotic Arc welding-Trends and Developments for Higher
Autonomy, Industrial Robot 2002, 29 (2), 98-104. https://doi.org/10.1108/01439910210419088
R. Baig, S. Dawood, Identifying and Prioritising Future Robot Control
M. Mansour, T. Tawfeek Research with Multi-Criteria Decision-Making
[48] Lopez-Juarez, I.; Corona-Castuera, J.; Pena-Cabrera, M.; Ordaz-Hernandez, K. On the Design of
Intelligent Robotic Agents for Assembly, Information Sciences 2005, 171 (4), 377-402.
https://doi.org/10.1016/j.ins.2004.09.011
Submitted: 21.12.2019
Accepted: 28.02.2020
Rahmath Ulla Baig1
Shaik Dawood2
1,2,3Industrial Engineering Department,
College of Engineering, King Khalid
University, Abha, Saudi Arabia
Mohamed Mansour*3,4
4Industrial Engineering Department,
College of Engineering, Zagazig
University, Zagazig, EL-Sharkia, Egypt
*Corresponding author
e-mail: momansor@kku.edu.sa
phone: +966-54-575-0250
Tarik Tawfeek5
5Mechanical Engineering Department,
College of Engineering, Benha university,
Shoubra, Egypt
