Robot Selection Research Articles

A new approach for Robot selection in manufacturing using the ellipsoid algorithm

The choice of suitable robots in manufacturing, to improve product quality and to increase productivity, is a complicated decision due to the increase in robot manufacturers and configurations. In this article, a novel approach is proposed to choose among alternatives, differently assessed by decision makers on different criteria, to make the final evaluation for decision-making. The approach is based on the ellipsoid algorithm for systems of linear inequalities. Most of the ranking methods depend on integration that becomes complicated for nonlinear membership functions, which is the case in robot selection. The method simply uses the membership function or its derivative. It takes the decision maker’s attitude in ranking. It effectively ranks fuzzy numbers and their images, preserving symmetry. It is a simple recursive algebraic formula that can be easily programmed. The performance of the algorithm is compared with the performance of some existing methods through several numerical examples to illustrate its advantages in ranking, and a robot selection problem is solved.

Integrating Linear Physical Programming and Fuzzy Logic for Robot Selection

An increasing number of companies are using robots to perform a variety of repetitive and hazourdous tasks. Existence of many different robot alternatives force companies to consider several conflicting criteria before determining the most suitable robot alternative. Researchers have developed various multi-criteria decision making based methodologies in order to assist the decision makers in robot selection process. However, those methodologies require decision makers to assign physically meaningless weights to evaluation criteria. This article eliminates this weight assignment process by proposing a robot selection methodology based on linear physical programming. In addition, fuzzy logic was integrated into the proposed approach in order to determine the preference values of subjective robot evaluation criteria. A numerical example is also provided in order to present the applicability of the proposed methodology.

Extension of TODIM for decision making in fuzzy environment: a case empirical research on selection of industrial robot

Extension of TODIM for decision making in fuzzy environment: a case empirical research on selection of industrial robot

Extension of TODIM for decision making in fuzzy environment: a case empirical research on selection of industrial robot

In order to facilitate decision making in robot selection problem, in this paper, an efficient fuzzy-based multi-criteria decision making (MCDM) tool has been highlighted. TODIM coupled with generalised fuzzy numbers (GFNs) set theory (fuzzy-TODIM) has been utilised here to determine the most preferable robot among all possible candidate alternatives. The results obtained thereof, have been compared to that of existing fuzzy-TOPSIS technique. We also propose a new formulation of fuzzy-TODIM (F-TODIM) by exploring the concept of similarity measure (between two fuzzy numbers) in order to compute relative gain and loss for pairs of alternatives with respect to a particular criteria. Similar ranking order of the alternative robots as obtained (in comparison with the F-TODIM formulation based on fuzzy distance measure) exhibits that fuzzy degree of similarity can fruitfully be utilised to evaluate dominance between two alternatives.

The Proposal of Structure for Workplaces with Palletizing Robot

The article describes design of structure palletizing robot workplace. There is described a composition of work and criteria for selection of palletizing robot. End of the article are shown the typical structure of the palletizing workstations.

Electroencephalography as implicit communication channel for proximal interaction between humans and robot swarms

Search and rescue, autonomous construction, and many other semi-autonomous multirobot applications can benefit from proximal interactions between an operator and a swarm of robots. Most research on proximal interaction is based on explicit communication techniques such as gesture and speech. This study proposes a new implicit proximal communication technique to approach the problem of robot selection. We use electroencephalography (EEG) signals to select the robot at which the operator is looking. This is achieved using steady-state visually evoked potential (SSVEP), a repeatable neural response to a regularly blinking visual stimulus that varies predictively based on the blinking frequency. In our experiments, each robot was equipped with LEDs blinking at a different frequency, and the operator’s SSVEP neural response was extracted from the EEG signal to detect and select the robot without requiring any conscious action by the user. This study systematically investigates several parameters affecting the SSVEP neural response: blinking frequency of the LED, distance between the robot and the operator, and color of the LED. Based on these parameters, we study two signal processing approaches and critically analyze their performance on 10 subjects controlling a set of physical robots. Our results show that despite numerous artifacts, it is possible to achieve a recognition rate higher than 85 % on some subjects, while the average over the ten subjects was 75 %.

Application of TODIM (Tomada de Decisión Inerativa Multicritero) for industrial robot selection

Purpose Robot selection is a critical decision-making task frequently experienced in almost every industries. It has become increasingly complex due to availability of large variety of robotic system in the present market with varying configuration, specification and flexibility. Improper selection may yield loss for the company in terms of potential profit as well as productivity. Hence, selection of an appropriate robot to suit a particular industrial application is definitely a challenging task. The paper aims to discuss these issues. Design/methodology/approach During robot selection, different criteria-attributes need to be taken under consideration. Criteria may be subjective or objective or a combination of both, depending on the situation. Criteria many be conflicting, in the sense that some criteria may require to be of higher value (higher-is-better), i.e. beneficial; while, others should correspond to lower values (lower-is-better), i.e. adverse or non-beneficial. Hence, the situation can be articulated as a multi-criteria decision-making problem. The specialty of Tomada de Decisión Inerativa Multicritero (TODIM) method is that it explores a global measurement of value calculable by the application of the paradigm of non-linear cumulative prospect theory. The method is based on a description, proved by empirical evidence, of how decision makers’ effectively make decisions in the face of risk. Findings Hence, the present work has aimed to explore the TODIM approach for industrial robot selection. Assuming all criteria have been quantitative in nature; the paper utilizes two different numeric data sets from available literature resource in perspectives of robot selection. Procedural hierarchy and application potential of the TODIM approach has been illustrated in detail in this reporting. Originality/value Variety of tools and techniques have already been documented in literature to solve different kinds of industrial decision-making problems; however, it seems that application of TODIM has got limited usage. Hence, application potential of TODIM has been demonstrated here in light of a robot selection problem.

A Multi Attribute Selection of Mobile Robot using AHP/M-GRA Technique

Today's highly competitive environment and volatile market conditions at national and international level are forcing the manufacturing concerns and its management to adopt the advance automated material handling equipment like mobile robots in a flexible manufacturing system (FMS) to meet the customers' demand regarding quality and variety of product at minimal cost. Selection of a robot is one of the most difficult problems in today's manufacturing environment. The problem has become more challenging due to increasing specifications and complexity of the robot. The main aim of this paper is to develop and implement an integrated methodology based on AHP (Analytical Hierarchy Process) and M-GRA (Modified Grey Relational Analysis) for the selection of a mobile robot for material handling in FMS environment. In this methodology, AHP technique has been used to assign the relative importance between mobile robot selection attributes and M-GRA technique is applied to determine mobile robot selection utility index. The proposed AHP/M – GRA technique is more suitable for the decision making in the presence of vagueness. The methodology is illustrated by means of an example. The ranking and evaluation of this process will provide a good guidance to the decision maker/user to select the appropriate material handling equipment on the basis of attributes. This is the novel effort in the area of robot selection.

A new distance-based decision model in interval-valued hesitant fuzzy setting for industrial selection problems

In classical group decision-making (GDM) analysis, ratings of potential alternatives and weights of criteria or decision makers (DMs) are known precisely. However, DMs for dealing with uncertain situations can define their opinions in linguistic variables based on fuzzy sets in industrial selection problems. In this respect, an interval-valued hesitant fuzzy set (IVHFS) is the suitable and capable theory that could help the DMs by assigning some interval-valued membership degrees for a candidate or option under a set. This paper introduces a novel interval-valued hesitant fuzzy distance-based group decision (IVHF-DBGD) model by a group of DMs, in which the best potential alternative can be appraised and selected among the conflicting criteria. In the proposed IVHF-DBGD model, the weight of each criterion is determined by extended IVHF-entropy method along with the DMs’ opinions about the criteria’ weights. Also, the weight of each DM is computed by a new IVHF-order preference method with the relative closeness. Moreover, this paper introduces a new IVHF-collective index to discriminate among potential alternatives in the selection process. Finally, the computational results with a robot selection from the literature indicate that the proposed IVHF-DBGD model is the suitable group decision-making tool for industrial selection problems.

IMPLEMENTATION OF ANALYTIC HIERARCHY PROCESS (AHP) AS A DECISION SUPPORT TOOL FOR SELECTION OF ROBOT FOR SPOT WELDING OPERATION

Flexible production lines are most essential for any lean manufacturing system for cost effectiveness. Automobile industry is extensively making use of lean manufacturing philosophy where reduction of waste is paramount importance without sacrificing the quality. Production of an automobile consists of number of phases in which fabrication of body by portable spot welding guns using robots is a significant stage. Robotic Spot welding operation is accomplished to manufacture the body of the automobile, because of its inherent accuracy and repeatability. Robot is a programmable machine which offers flexibility to switch over from one type of model of automobile to another type of model swiftly. But selection of an appropriate robot for the desired task to be accomplished is a crucial decision to be taken. Many factors are to be considered before arriving at an apt decision. Thescenario of this sort may be considered as multi-criteria decisionmaking (MCDM) problem. Though many techniques are available to solve problems of this type, Analytic Hierarchy Process (AHP) has been selected, because of its strong mathematical basis. In the present paper AHP is implemented to address this MCDM problem which acts as a decision support tool to select an appropriate robot for spot welding operation.

Robots poised to revolutionise agriculture

Purpose This paper aims to provide details of a number of recent and significant agricultural robot research and development activities. Design/methodology/approach Following an introduction, this first provides a brief overview of agricultural robot research. It then discusses a number of specific activities involving robots for precision weed control and fertiliser application. A selection of harvesting robots and allied technological developments is then considered and is followed by concluding comments. Findings Agricultural robots are the topic of an extensive research and development effort. Several autonomous robots aimed at precision weed control and fertiliser application have reached the pre-production stage. Equally, harvesting robots are at an advanced stage of development. Both classes exploit state-of-the-art machine vision and image processing technologies which are the topic of a major research effort. These developments will contribute to the forecasted rapid growth in the agricultural robot markets during the next decade. Originality/value Robots are expected to play a significant role in meeting the ever increasing demand for food, and this paper provides details of some recent agricultural robot research and development activities.

Autonomous Viewpoint Selection of Robot Based on Aesthetic Evaluation of a Scene

Abstract In this paper, we propose an optimal viewpoint selection system for monitoring robots to search for the optimal viewpoint of a scene with the highest aesthetic property. Using the information of the targets, we propose a novel method for predicting human aesthetic sense for a scene. We construct evaluation functions based on certain known composition rules using three factors, namely, target size, visual balance, and composition fitting value. Then a score, which is a reflection of human evaluation, will be obtained using these functions. The optimal viewpoint will be selected from a number of candidates around the target group, by evaluating the aesthetic properties of scenes for each candidate viewpoint. Finally, once the optimal viewpoint is confirmed, path planning and path following controls are implemented for the robots during the moving process.

Commentary on Robotic Follicular Unit Graft Selection

Commentary on Robotic Follicular Unit Graft Selection

Extension of PROMETHEE for robot selection decision making

Purpose – Robot selection is basically a task of choosing appropriate robot among available alternatives with respect to some evaluation criteria. The task becomes much more complicated since apart from objective criteria a number of subjective criteria need to be evaluated simultaneously. Plenty of decision support systems have been well documented in existing literature which considers either objective or subjective data set; however, decision support module with simultaneous consideration of objective as well as subjective data has rarely been attempted before. The paper aims to discuss these issues. Design/methodology/approach – Motivated by this, present work exhibits application potential of preference ranking organization method for enrichment evaluations (extended to operate under fuzzy environment) to solve decision-making problems which encounter both objective as well as subjective evaluation data. Findings – An empirical case study has been demonstrated in the context of robot selection problem. Finally, a sensitivity analysis has been performed to make the robot selection process more robust. A trade-off between objective criteria measure and subjective criteria measure has been shown using sensitivity analysis. Originality/value – Robot selection has long been viewed as an important decision-making scenario in the industrial context. Appropriate robot selection helps in enhancing value of the product and thereby, results in increased profitability for the manufacturing industries. The proposed decision support system considering simultaneous exploration of subjective as well as objective database is rarely attempted before.

Scaled conjugate gradient back-propagation algorithm for selection of industrial robots

Purpose: Development of industrial robots and its usage by different manufacturing industries in lot many diverse applications is a very much serious task for the selection of robots. As a consequence, the selection process of the robot becomes incredibly complicated for the potential users because they have a large set of attributes and parameters of the robots are available at their disposal. Moreover, the proposed work gives the efficient decision for the selection of the robots to the potential users of the robots or robot manufacturer. Design/methodology/approach: In this paper, it has been proposed that the scaled conjugate gradient fastest back-propagation algorithm used for the optimized way of selection of robot based on the appropriate parameters. Findings: The ranks of the desired industrial robots are evaluated using the proposed algorithm from the entire best possible robot that specifies the most genuine yardstick of robot selection for the particular application. The complete design performance of the proposed method discussed with different prediction errors such as mean square error, root mean square error, and R-squared error. Originality/value: The proposed methodology is purely original scientific work and very fastest algorithm for the selection of industrial robot. In this work maximum, ten numbers of parameters of the robot are used for the selection of the industrial robot and eight broad categories of the robot are proposed called robot-rank.

An integrated linguistic MCDM approach for robot evaluation and selection with incomplete weight information

Nowadays selecting the most suitable robot is a difficult task for manufacturing firms due to increase in production demands and availability of various robot models. Robot evaluation and selection can be regarded as a multiple criteria decision-making (MCDM) problem and three key issues are the assessment of robots, the determination of criteria weights and the prioritisation of alternatives. This paper aims to propose an integrated model based on hesitant 2-tuple linguistic term sets and an extended QUALIFLEX approach for handling robot selection problems with incomplete weight information. The new model can not only manage uncertain and imprecise assessment information of decision-makers with the aid of hesitant 2-tuple linguistic term sets, but also derive the important weights of criteria objectively when the weight information is incompletely known. Moreover, based on the extended QUALIFLEX algorithm, the priority orders of robots can be clearly determined and a more reasonable and credible solution can be yielded in a particular industrial application. Finally, a robot selection case study is carried out, and comparative experiments indicate the practicality and effectiveness of the proposed integrated linguistic MCDM approach.

Soft computing-based new interval-valued hesitant fuzzy multi-criteria group assessment method with last aggregation to industrial decision problems

Recent studies have identified that multi-criteria group analysis methods should take the concepts of risk and uncertainty into account. In some real-life situations, determining the exact values for the potential alternatives' performances and criteria weights is so difficult. To overcome with these situations, their values should be regarded as fuzzy and fuzzy intervals. In this respect, interval-valued hesitant fuzzy sets (IVHFSs) as a suitable modern fuzzy sets theory can be considered because this theory allows decision makers (DMs) to assign some interval-values membership degrees for an alternative in terms of selected criteria under a set to margin of errors. Hence, this paper proposes a novel soft computing approach, namely IVHF-MCGA, based on new interval-valued hesitant fuzzy complex proportional assessment (IVHF-COPRAS) method that can be applied in solving the multi-criteria group decision-making (MCGDM) problems under uncertainty. In this approach, preference values of potential alternatives versus the selected criteria and weights of each criterion are expressed by linguistic variables and then are transformed to interval-valued hesitant fuzzy elements (IVHFEs). In addition, an interval-valued hesitant fuzzy entropy (IVHF-entropy) method is extended to determine the criteria weights by considering the DMs' opinions about the relative importance. Also, a new interval-valued hesitant fuzzy compromise solution (IVHF-CS) method is introduced to estimate the weight of each DM in the group decision-making process along with the last aggregation for the DMs' judgments to avoid the data loss. Then, three practical applications about the robot selection, industrial site selection and rapid prototyping process selection problems are considered to explain steps of the proposed IVHF-MCGA approach and to indicate its validity and applicability. Finally, a comparative analysis between the proposed approach and fuzzy group TOPSIS method is presented based on four comparison parameters, including adequacy to changes of alternatives and criteria, agility in decision process, influence of DMs' weights and impact of first and last aggregations, to show its suitability.

A study on the ranking performance of some MCDM methods for industrial robot selection problems

A study on the ranking performance of some MCDM methods for industrial robot selection problems

Extension of TODIM combined with grey numbers: an integrated decision making module

Purpose – Decision making is the task of selecting the most appropriate alternative among a finite set of possible alternatives with respect to some attributes. The attributes may be subjective or objective (or combination of both), depending upon the situation; requirements may also be conflicting. In practice, most of the real-world decision-making problems are based on subjective evaluation criteria which are basically ill-defined and vague. Since subjective human judgment bears ambiguity and vagueness in the decision making; application of grey numbers set theory may be proved fruitful in this context. The paper aims to discuss these issues. Design/methodology/approach – Owing to the advantages of grey numbers set theory in tackling subjectivity in decision making; the crisp-TODIM needs to be extended by integrating with grey numbers set theory in order to facilitate decision making consisting of subjective data. Hence, the unified objective of this paper is to propose a grey-based TODIM approach in the context of decision making. Findings – Application potential of grey-TODIM has been demonstrated through a case empirical robot selection problem. Result obtained thereof, has also been compared to that of existing grey-based decision support systems available in literature. Originality/value – Application potential of grey-based decision support systems (grey-TOPSIS, grey analysis, grey-MOORA) have been highlighted in available literature resource. However, the shortcoming of these approaches is that they do not consider decision-makers’ risk attitude while decision making. TODIM method is derived from the philosophy of Cumulative Prospect Theory (CPT) which considers risk averting attitude of the decision maker in case of gain and risk seeking attitude in case of loss, while comparing dominance between two alternatives with respect to a particular criterion. Hence, this paper contributes a mathematical foundation of TODIM coupled with grey numbers set theory for logical decision making.

A comparative evaluation of three industrial robots using three reference measuring techniques

Purpose – The purpose of this paper is to present a technique for assessing and comparing the static and dynamic performance of three different models of small six-axis industrial robots using a Renishaw XL80 laser interferometer system, a FARO ION laser tracker and a Renishaw QC20-W telescoping ballbar. Design/methodology/approach – Specific test methods are proposed in this work, and each robot has been measured in a similar area of its working envelope. The laser interferometer measurement instrument is used to assess the static positioning performance along three linear and orthogonal paths. The laser tracker is used to assess the contouring performance at different tool center point (TCP) speeds along a triangular tool path, whereas the telescoping ballbar is used to assess the dynamic positioning performance for circular paths at different TCP speeds and trajectory radii. Findings – It is found that the tested robots behave differently, and that the static accuracy of these non-calibrated robots varies between 0.5 and 2.3 mm. On the other hand, results show that these three robots can provide acceptable corner tracking at low TCP speeds. However, a significant overshoot at the corner is observed at high TCP speed for all the robots tested. It was also found that the smallest increment of Cartesian displacement (Cartesian resolution) that can be taken by the tested robots is approximately 50 μm. Practical implications – The technique used in this paper allows extremely accurate diagnosis of the robot performance, which makes it possible for the robot user to determine whether the robot is in good or bad condition. It can also help the decision-maker to select the most suitable industrial robot to achieve the desired task with minimum cost and specific application ability. Originality/value – This paper proposed a new method based on the performance verification approach for solving the robot selection problem for flexible manufacturing systems. Furthermore, despite their importance, bidirectional repeatability and Cartesian resolution are never specified by the manufacturers of industrial robots nor are they described in the ISO 9283:1998 guide, and they are rarely the object of performance assessments. In this work, specific tests are performed to check and quantify the bidirectional repeatability and the Cartesian resolution of each robot.