Robot Selection Research Articles

A satisfaction function and distance measure based multi-criteria robot selection procedure

A multi-criteria decision-making model for a robot selection problem is presented in this article. The proposed model uses satisfaction function to convert various robot attributes into a unified scale. Further, a distance measure technique is used to ascertain the highest ranked candidate-robot. The proposed model is tested on the data from the open literature. An example case is presented and the results so obtained considering different attributes are compared with the results obtained earlier utilising the same data.

A comparative study on the ranking performance of some multi-criteria decision-making methods for industrial robot selection

Article history: Received 1 April 2011 Received in revised form May, 08, 2011 Accepted 08 May 2011 Available online 10 May 2011 Industrial robots are mainly employed to perform repetitive and hazardous production jobs, multi-shift operations etc. to reduce the delivery time, improve the work environment, lower the production cost and even increase the product range to fulfill the customers’ needs. When a choice is to be made from among several alternative robots for a given industrial application, it is necessary to compare their performance characteristics in a decisive way. As the industrial robot selection problem involves multiple conflicting criteria and a finite set of candidate alternatives, different multi-criteria decision-making (MCDM) methods can be effectively used to solve such type of problem. In this paper, ten most popular MCDM methods are considered and their relative performance are compared with respect to the rankings of the alternative robots as engaged in some industrial pick-n-place operation. It is observed that all these methods give almost the same rankings of the alternative robots, although the performance of WPM, TOPSIS and GRA methods are slightly better than the others. It can be concluded that for a given industrial robot selection problem, more attention is to be paid on the proper selection of the relevant criteria and alternatives, not on choosing the most appropriate MCDM method to be employed. © 2011 Growing Science Ltd. All rights reserved

Extension of VIKOR method in intuitionistic fuzzy environment for robot selection

Decision making is the process of finding the best option among the feasible alternatives. In classical multiple-criteria decision making methods, the ratings and the weights of the criteria are known precisely. However, if decision makers are not able to involve uncertainty in the defining of linguistic variables based on fuzzy sets, the intuitionistic fuzzy set theory can do this job very well. In this paper, VIKOR method is extended in intuitionistic fuzzy environment, aiming at solving multiple-criteria decision making problems in which the weights of criteria and ratings of alternatives are taken as triangular intuitionistic fuzzy set. For application and verification, this study presents a robot selection problem for material handling task to verify our proposed method.

A fuzzy digraph method for robot evaluation and selection

The selection of a robot for a particular industrial application has been confounded the decision makers for at least four decades. The increment of production demands and the large number of alternative robots, contributed to the complexity of the selection problem. In this article, a fuzzy digraph method is developed for robot evaluation and selection, according to a given industrial application. All the information about the objective and subjective attributes are expressed in linguistic terms, represented by fuzzy numbers. Considering the number of robot selection attributes and their relative importance, a digraph is developed for the optimum representation of interrelations. The digraph model associates the systematical process for attributes combination with the simplicity which characterizes the robot evaluation method, by using a matrix approach. The procedure is discriminated between subjective and objective attributes and the corresponding matrix of relative importances is formed for all the alternative robots. Using the positive determinants of fuzzy matrices, a pair of indexes obtained for each robot. The methodology is concluded by converting the fuzzy output into a crisp value and estimating the selection index. Using the selection index, the evaluation of alternative robots and the selection of the most appropriate is eventually feasible. Two numerical examples are included to illustrate the proposed approach. Moreover, the ‘Fuzzy TOPSIS’ and ‘Digraph’ models are implemented according to the particular example, in order to display the classification per methodology.

Group decision making based on novel fuzzy modified TOPSIS method

The aim of this paper is to present a novel fuzzy modified technique of order preference by a similarity to ideal solution (TOPSIS) method by a group of experts, which can select the best alternative by considering both conflicting quantitative and qualitative evaluation criteria in real-life applications. The proposed method satisfies the condition of being the closest to the fuzzy positive ideal solution and also being the farthest from the fuzzy negative ideal solution with multi-judges and multi-criteria. The performance rating values of alternatives versus conflicting criteria as well as the weights of criteria are described by linguistic variables and are transformed into triangular fuzzy numbers. Then a new collective index is introduced to discriminate among alternatives in the evaluation process with respect to subjective judgment and objective information. This paper shows that the proposed fuzzy modified TOPSIS method is a suitable decision making tool for the manufacturing decisions with two examples for the robot selection and rapid prototyping process selection.

Industrial robot selection using a novel decision making method considering objective and subjective preferences

Robots with vastly different capabilities and specifications are available for a wide range of applications. Selection of a robot from among the large number available in the market to suit a particular application and production environment has become a difficult task. Many methods for robot selection have been developed to date. Keeping in view the research works on industrial robot selection, a subjective and objective integrated multiple attribute decision making method is proposed in this paper for the purpose of robot selection. The method considers the objective weights of importance of the attributes as well as the subjective preferences of the decision maker to decide the integrated weights of importance of the attributes. Furthermore, the method uses fuzzy logic to convert the qualitative attributes into the quantitative attributes. Three examples are presented to illustrate the potential of the proposed method.

Selection of industrial robot using interval-valued trapezoidal fuzzy numbers set combined with VIKOR method

Recently industrial robots are being immensely applied in almost every manufacturing or production industries for improvement of quality as well as productivity. Depending on the nature of the job to be performed, appropriate robot selection has become an important as well as challenging task for an automated manufacturing cell. Several criteria attributes are assumed to be responsible towards performance of a particular robot. Hence, a strong multi–attribute decision support model is indeed required to facilitate this evaluation and selection process. To address this issue, present work explores the concept of interval–valued trapezoidal fuzzy numbers set integrated with VIKOR method to help such a decision–making problem.

A PSO based multi-robot task allocation

Recent research trends and technology developments have been instrumental to the realisation of autonomous multi-robot systems (MRS) performing increasingly complex missions. However the selection of candidate robots from a team of robots for performing the task(s) from a set of desired tasks in order to achieve an economical and feasible process poses a difficult problem. Task distribution methodologies have to make sure that not only the global assignment is achieved, but also the tasks are well assigned among the robots. This paper presents the particle swarm optimisation (PSO) algorithm designed to address this problem. A two-phase solution methodology is used to solve the multi-robot task allocation (MRTA) problem wherein the task capability of the robots is determined during the first segment and the task allocation optimisation is done using PSO during the second segment. The solution to MRTA problem in dynamic environment is proposed using a novel PSO based algorithm and is compared with that using linear programming (LP).

A hybrid multiple attribute decision making method for solving problems of industrial environment

Multiple attribute decision making Graph theory and matrix approach Analytical hierarchy process Electroplating system selection Robot selection Welding process selection

Intelligent task planning and action selection of a mobile robot in a multi-agent system through a fuzzy neural network approach

This paper proposes an intelligent task planning and action selection mechanism for a mobile robot in a robot soccer system through a fuzzy neural network approach. The proposed fuzzy neural network system is developed through the two dimensional fuzzification of the soccer field. A five layer fuzzy neural network system is trained through error back propagation learning algorithm to impart a strategy based action selection. The action selection depends on the field configuration, and the emergence of a particular field configuration results from the game dynamics. Strategy of the robot changes when the configuration of the objects in the field changes. The proposed fuzzy neural network structure is flexible to accommodate all possible filed configurations. Simulation results indicate that the proposed approach is simple and has the capability in coordinating the multi-agent system through selection of sensible actions.

Optimal selection of robots by using distance based approach method

A deterministic quantitative model based on Distance Based Approach (DBA) method has been developed for evaluation, selection and ranking of robots, which is a concept hitherto not employed in selection problem of this kind. As a significant development over and above past approaches to robot selection, it recognizes the need for, and processes the information about, relative importance of attributes for a given application, without which inter-se-attribute comparison could not be accomplished. It successfully presents the results of this information processing in terms of a merit value which is used to rank the robots. In order to demonstrate the aptness of using DBA method as a decision aid, the results so obtained have been compared with other techniques and methods available in the open literature. Sensitivity analysis test have been performed to analyze the critical and non-critical performance attributes for a robot. The model developed using DBA method is explained and illustrated with an example problem.

Selection of industrial robots using compromise ranking and outranking methods

Selection of a robot for a specific industrial application is one of the most challenging problems in real time manufacturing environment. It has become more and more complicated due to increase in complexity, advanced features and facilities that are continuously being incorporated into the robots by different manufacturers. At present, different types of industrial robots with diverse capabilities, features, facilities and specifications are available in the market. Manufacturing environment, product design, production system and cost involved are some of the most influencing factors that directly affect the robot selection decision. The decision maker needs to identify and select the best suited robot in order to achieve the desired output with minimum cost and specific application ability. This paper attempts to solve the robot selection problem using two most appropriate multi-criteria decision-making (MCDM) methods and compares their relative performance for a given industrial application. The first MCDM approach is ‘VIsekriterijumsko KOmpromisno Rangiranje’ (VIKOR), a compromise ranking method and the other one is ‘ELimination and Et Choice Translating REality’ (ELECTRE), an outranking method. Two real time examples are cited in order to demonstrate and validate the applicability and potentiality of both these MCDM methods. It is observed that the relative rankings of the alternative robots as obtained using these two MCDM methods match quite well with those as derived by the past researchers.

2A2-E09 人間上肢型ロボティックアームによるアトラクタ選択モデルを用いた円運動の生成

2A2-E09 人間上肢型ロボティックアームによるアトラクタ選択モデルを用いた円運動の生成

Incremental analysis for MCDM with an application to group TOPSIS

The study aims to exploit incremental analysis or marginal analysis to overcome the drawbacks of ratio scales utilized in various multi-criteria or multi-attribute decision making (MCDM/MADM) techniques. In the proposed 11-step procedure, multiple criteria of alternatives are first reorganized as two categories – benefits and costs – and decision information will be manipulated separately. The performances of alternatives are then evaluated on their incremental benefit–cost ratio, and the rank can be obtained by applying the group TOPSIS (technique for order preference by similarity to ideal solution) model ( Shih et al., 2007). Two representations of cost, i.e., a cost index and utility index, are proposed in the model to better-fit real-world situations. In addition, some considerations on costs and input–output relations are also discussed in order to understand the essentials of incremental analysis. In the final part, a case of robot selection demonstrates the suggested model to be both robust and efficient in a group decision-making environment.

Robot selection using an integrated approach based on quality function deployment and fuzzy regression

The decision-makers have been experiencing difficulties in determining the most suitable robot alternative due to the increase in number of robots and the diversity in their application areas. A robust decision framework for robot selection should consider multiple and conflicting criteria and the dependencies among them. This paper introduces a decision model for robot selection based on quality function deployment (QFD) and fuzzy linear regression. The proposed approach benefits from the fact that QFD focuses on delivering value by taking into account the customer requirements and then by deploying this information throughout the development process, and applies this perspective to robot selection. Fuzzy linear regression is considered as an alternative decision aid for robot selection problems where imprecise relationships among system parameters exist. An example robot selection problem is presented to illustrate the proposed decision-making approach.

Navigation Behavior Selection Using Generalized Stochastic Petri Nets for a Service Robot

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Appropriate design and control of behaviors of mobile robots are important for their successful autonomous navigation in a real dynamic environment. This paper proposes a formal selection framework of multiple navigation behaviors for a service robot. In the presented approach, modeling, analysis, and performance evaluation are carried out based on generalized stochastic Petri nets (GSPNs). By adopting a probabilistic approach, the proposed framework helps the robot to select the most desirable navigation behavior in run time according to environmental conditions. Moreover, after mission completion, the robot evaluates its prior navigation performance from accumulated data, and automatically uses the results to improve its future operations. Also, GSPNs have several advantages over direct use of other modeling formalisms such as finite state automata (FSA) or Markov processes (MPs). We conduct experiments on real guidance tasks with visitors by implementing the framework in the guide robot <emphasis emphasistype="italic">Jinny</emphasis> at the National Science Museum of Korea. The results show that the proposed strategy is useful for a robot's selection of an appropriate navigation behavior in a dynamic environment. </para>

Intelligent task planning and action selection of a mobile robot in a multi-agent system through a fuzzy neural network approach

Intelligent task planning and action selection of a mobile robot in a multi-agent system through a fuzzy neural network approach

Behavior selection of mobile robot based on integration of multimodal information

AbstractRecently, biologically inspired robots have been developed to acquire the capacity for directing visual attention to salient stimulus generated from the audiovisual environment. For the purpose of realizing this behavior, a general method is to calculate saliency maps to represent how much the external information attracts the robot's visual attention, where the audiovisual information and robot's motion status should be involved. In this paper, we represent a visual attention model where three modalities—audio information, visual information, and robot's motor status—are considered, because previous research has not considered all of them. First, we introduce a 2D density map, on which the value denotes how much the robot pays attention to each spatial location. Then we model the attention density using a Bayesian network where the robot's motion statuses are involved. Next, the information from both audio and visual modalities is integrated with the attention density map in integrate‐fire neurons. The robot can direct its attention to the locations where the integrate‐fire neurons are fired. Finally, the visual attention model is applied to make the robot select the visual information from the environment, and react to the content selected. Experimental results show that it is possible for robots to acquire the visual information related to their behaviors by using the attention model considering motion statuses. The robot can select its behaviors to adapt to the dynamic environment as well as to switch to another task according to the recognition results of visual attention. © 2006 Wiley Periodicals, Inc. Electr Eng Jpn, 158(2): 39–48, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20335

Selection, identification and comparison of industrial robots using digraph and matrix methods

In the present work, a methodology based on digraph and matrix methods is developed for evaluation of alternative industrial robots. A robot selection index is proposed that evaluates and ranks robots for a given industrial application. The index is obtained from a robot selection attributes function, obtained from the robot selection attributes digraph. The digraph is developed considering robot selection attributes and their relative importance for the application considered. A step by step procedure for evaluation of robot selection index is suggested. Coefficients of similarity and dissimilarity and the identification sets are also proposed. These are obtained from the robot selection attributes function and are useful for easy storage and retrieval of the data. Two examples are included to illustrate the approach.

Integrating AHP with QFD for robot selection under requirement perspective

Selection of a robot is an important task, as improper selection may adversely affect a firm's production by reducing the quality of the product, thereby reducing productivity as well as profitability. To effectively select a robot for a specified job, several factors have to be considered. The objective of this paper is to explain how, using a combined AHP/QFD model, the authors are able to determine if the deployment of robots in industry helped in performance enhancement from requirement perspective. Incorporating a simple and novel cost factor measure in the proposed integrated AHP/QFD model aids justification of the implementation of a robotic system in a manufacturing firm from an economic point of view also. The proposed integrated approach also identifies technical requirements followed by customer requirements. In this paper, an integrated model combining AHP and QFD has been delineated for the industrial robot selection problem. Seven technical requirement factors have been considered for the case study.