Robot Selection Research Articles

A Statistical Analysis of Commercial Articulated Industrial Robots and Cobots

This paper aims to elucidate the state-of-the-art, prevailing priorities, and the focus of the industry, and identify both limitations and potential gaps regarding industrial robots and collaborative robots (cobots). Additionally, it outlines the advantages and disadvantages of cobots compared to traditional industrial robots. Furthermore, three novel factors are introduced in this survey as metrics to evaluate the efficiency and performance of industrial robots and cobots. To achieve these purposes, a statistical analysis and review of commercial articulated industrial robots and cobots are conducted based on their documented specifications, such as maximum payload, weight, reach, repeatability, average maximum angular speed, and degrees of freedom (DOF). Additionally, the statistical distributions of the efficiency factors are investigated to develop a systematic method for robot selection. Finally, specifications exhibiting strong correlations are compared in pairs using regressions to find out trends and relations between them, within each company and across them all. The investigation of the distribution of specifications demonstrates that the focus of the industry and robot makers is mostly on articulated industrial robots and cobots with higher reach, lower payload capacity, lower weight, better repeatability, lower angular speed, and six degrees of freedom. The regressions reveal that the weight of robots increases exponentially as the reach increases, primarily due to the added weight and torque resulting from the extended reach. They also indicate that the angular speed of robots linearly decreases with increasing reach, as robot manufacturers intentionally reduce the angular speed through reductive gearboxes to compensate for the additional torque required as the reach extends. The trends obtained from the regressions explain the reasons behind these interrelationships, the design purpose of robot makers, and the limitations of industrial robots and cobots. Additionally, they help industries predict the dependent specifications of articulated robots based on the specifications they require. Moreover, an accompanying program has been developed and uploaded on to GitHub, taking the required specifications and returning a list of proper and efficient robots sourced from different companies according to the aforementioned selection method.

Reliable and Energy-Efficient Communications in Mobile Robotic Networks by Collaborative Beamforming

For mobile robotic networks in industrial scenarios, reliable and energy-efficient communications are crucial yet challenging. Fortunately, collaborative beamforming (CB) emerges as a promising solution, which can increase the transmission gain and reduce the transmit power of robots by constructing a mobile robot-enabled virtual antenna array (MRVAA). The performance of CB is tightly related to robot positions, necessitating proper robot selection. However, robot selection may expose the network to the risk of unbalanced energy distribution, reducing network lifetime. Additionally, the mobility and variable numbers of robots require flexible and scalable robot selection algorithms. To tackle these challenges, we first formulate a multi-objective optimization problem to reduce the maximum sidelobe level (MSLL) of MRVAA while minimizing the standard deviation of the network energy distribution (SDNED) by selecting robots for CB. Then, based on distributed multi-agent learning (MARL), we propose an effective and scalable robot selection algorithm with energy considered (RoSE) to solve the problem, where difference-rewards function (DRF) and policy sharing are designed for enhancing convergence rate and policy stability. Simulation results show that the RoSE has the scalability to positions and numbers of robots. Furthermore, RoSE surpasses existing selection algorithms in network lifetime and time efficiency, while still maintaining comparable MSLL.

Predictors and Outcomes Associated with Bariatric Robotic Delivery: An MBSAQIP Analysis of 318,151 Patients.

Background: The adoption of robotic bariatric surgery has increased dramatically over the last decade. While outcomes comparing bariatric and laparoscopic approaches are debated, little is known about patient factors responsible for the growing delivery of robotic surgery. A better understanding of these factors will help guide the planning of bariatric delivery and resource allocation. Methods: Data were extracted from the MBSAQIP registry from 2020 to 2021. The patient population was organized into primary robot-assisted sleeve gastrectomy or Roux-en-Y gastric bypass (RYGB) versus those who underwent laparoscopic procedures. Bivariate analysis and multivariable logistic regression modeling were conducted to characterize cohort differences and identify independent patient predictors of robotic selection. Results: Of 318,151, 65,951 (20.7%) underwent robot-assisted surgery. Patients undergoing robotic procedures were older (43.4 ± 11.8 vs. 43.1 ± 11.8; p < 0.001) and had higher body mass index (BMI; 45.4 ± 7.9 vs. 45.0 ± 7.6; p < 0.001). Robotic cases had higher rates of medical comorbidities, including sleep apnea, hyperlipidemia, gastroesophageal reflux disease (GERD), and diabetes mellitus. Robotic cases were more likely to undergo RYGB (27.4% vs. 26.4%; p < 0.001). Robotic patients had higher rates of numerous complications, including bleed, reoperation, and reintervention, resulting in higher serious complication rates on multivariate analysis. Independent predictors of robotic selection included increased BMI (aOR 1.02), female sex (aOR 1.04), GERD (aOR 1.12), metabolic dysfunction, RYGB (aOR 1.08), black racial status (aOR 1.11), and lower albumin (aOR 0.84). Conclusions: After adjusting for comorbidities, patients with greater metabolic comorbidities, black racial status, and those undergoing RYGB were more likely to receive robotic surgery. A more comprehensive understanding of patient factors fueling the adoption of robotic delivery, as well as those expected to benefit most, is needed to better guide healthcare resources as the landscape of bariatric surgery continues to evolve.

A Multi-Sensor-Based Terrain Perception Model for Locomotion Selection of Hybrid Mobile Robots

A Multi-Sensor-Based Terrain Perception Model for Locomotion Selection of Hybrid Mobile Robots

Comprehensive Performance Evaluation of Earthquake Search and Rescue Robots Based on Improved FAHP and Radar Chart

To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal overall capabilities. In this paper, twenty-two evaluation criteria are proposed based on a comprehensive review of existing evaluation criteria for rescue robots across various domains. The evaluation criteria are tested using the test modules developed by the National Earthquake Response support service, obtaining the corresponding values for each criterion. Then, the weights of the criterion layer and comprehensive evaluation index are determined based on the analytical hierarch process and trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed. The qualitative evaluation and quantitative analysis are effectively combined to overcome the subjective influence of expert decision-making. Additionally, the performance of three earthquake search and rescue robots is comprehensively evaluated and ranked using the improved radar chart method as an empirical example. Finally, the robustness of the ranking results is examined using a weight sensitivity analysis. The results of the sensitivity analysis demonstrate the effectiveness and feasibility of the proposed method, thereby providing valuable insights for developing multi-objective optimization control strategies and structural designs for earthquake search and rescue robots.

An enhanced decision making model for industrial robotic selection using three factors: Positive, abstained, and negative grades of membership

Traditional packaging industries that lack automation often grapple with a spectrum of challenges that impede operational efficiency, productivity and overall competitiveness. To maintain quality and safety, the food industry must transition from manual to robotic packaging processes. The most suitable robot for executing such task is identified via a new hybrid fuzzy Stratified Multi-Attribute Decision-Making (S-MADM). This model comprises Fuzzy-PIvot Pairwise RElative Criteria Importance Assessment (F-PIPRECIA) and Stratified Network Mapping (SNM). The Hesitant T-Spherical Fuzzy (HT-SF) set effectively tackles the uncertainty associated with the selection process by introducing three factors, namely, positive, abstained, and negative grades of membership. A case study is presented to demonstrate the novel fusion model and determine the optimal food packaging robot. A total of twelve criteria are selected from the literature based on the decision-maker’s judgment. Among the seven industrial robots, the ”Delta robot” gained the highest ranking (71%), followed by the ”Scara robot” (56%), and the ”Multi-Axis Gantry” (49%). Delta robots, with spider-like limbs, can move gently and accurately at fast speeds, with heavy motors fixed on the frame, allowing for lightweight moving parts. The proposed SNM technique helps to enhance the effectiveness of these alternatives by analyzing the possible, inefficient, and highly influential states of the system. The sensitivity study confirms this analysis and the system’s durability while the comparative study verifies the effectiveness and feasibility of the proposed technique than existing MADM models. This paradigm enables company stakeholders to invest in an industrial robot that not only delivers better results but also efficiently overcome barriers associated with manual packaging procedures.

Centroid and Graded Mean Ranking Methods for Intuitionistic Trapezoidal Dense Fuzzy Set to Solve MCDM Problems of Robot Selection

Centroid and Graded Mean Ranking Methods for Intuitionistic Trapezoidal Dense Fuzzy Set to Solve MCDM Problems of Robot Selection

Submillimeter-Accurate Markerless Hand-Eye Calibration Based on a Robot's Flange Features.

An accurate and reliable estimation of the transformation matrix between an optical sensor and a robot is a key aspect of the hand-eye system calibration process in vision-guided robotic applications. This paper presents a novel approach to markerless hand-eye calibration that achieves streamlined, flexible, and highly accurate results, even without error compensation. The calibration procedure is mainly based on using the robot's tool center point (TCP) as the reference point. The TCP coordinate estimation is based on the robot's flange point cloud, considering its geometrical features. A mathematical model streamlining the conventional marker-based hand-eye calibration is derived. Furthermore, a novel algorithm for the automatic estimation of the flange's geometric features from its point cloud, based on a 3D circle fitting, the least square method, and a nearest neighbor (NN) approach, is proposed. The accuracy of the proposed algorithm is validated using a calibration setting ring as the ground truth. Furthermore, to establish the minimal required number and configuration of calibration points, the impact of the number and the selection of the unique robot's flange positions on the calibration accuracy is investigated and validated by real-world experiments. Our experimental findings strongly indicate that our hand-eye system, employing the proposed algorithm, enables the estimation of the transformation between the robot and the 3D scanner with submillimeter accuracy, even when using the minimum of four non-coplanar points for calibration. Our approach improves the calibration accuracy by approximately four times compared to the state of the art, while eliminating the need for error compensation. Moreover, our calibration approach reduces the required number of the robot's flange positions by approximately 40%, and even more if the calibration procedure utilizes just four properly selected flange positions. The presented findings introduce a more efficient hand-eye calibration procedure, offering a superior simplicity of implementation and increased precision in various robotic applications.

Development of a knowledge base for an expert system supporting the decision of industrial mobile robot selection

With the growth of industrial mobile robots, more and more manufacturing companies are producing such robots. As a result, there is a growing market variety for companies wanting to adopt these robots. It is an economic advantage if a robot selected for a specific task can be operated optimally. To this end, the selection process must already be carried out with optimal operation in mind. In my research, I develop such an industrial mobile robot selection support system, which generates a knowledge base from a database and then supports the selection decision with an expert system. In this paper, I describe the structure, interrelationship and test results of this database and knowledge base.

Selection of Robot for Contour Crafting Using Analytical Hierarchy Process

The use of robotic equipment and a new technique called contour crafting allows for the construction of buildings at lower labor and material costs. The selection of the type of robot is an important factor that affects the overall performance of the contour crafting (CC) system. Various robot configurations, such as gantry, cylindrical, and SCARA, may be employed for contour crafting. There are benefits and drawbacks to using different types of robots for various tasks, including cost, work volume, material compatibility, and precision. Identifying a proper robot using the multi-criterion decision-making (MCDM) technique is crucial for successful building automation. This article uses the analytical hierarchy process (AHP) method to rank the best robots according to several characteristics. Cartesian robots, cylindrical robots, and SCARA robots were evaluated based on cost, accuracy, work volume, surface finish, type of profile, and speed. The results showed that the gantry-type robot is the most suitable option, while the cylindrical robot is unsuitable for building construction due to lower accuracy.

Selected Issues, Methods, and Trends in the Energy Consumption of Industrial Robots

Industrial robots, like all machines, require energy to operate, which is why energy efficiency in industrial robotics has been a subject of consideration in recent years in many scientific and industrial centers. Interest in the topic is especially noticeable in Industry 4.0. Research on energy efficiency stems from the emergence of new possibilities in terms of making strategic decisions related to manufacturing processes. As energy-efficient production is an essential part of sustainable development, the energy efficiency of industrial robots must be considered. The need to reduce costs while maintaining quality and increasing production efficiency has necessitated the implementation of modern solutions aimed at reducing electricity consumption. The rational use of electrical energy, especially in the industrial sector, significantly reduces production costs and, consequently, contributes to a company’s profits and competitiveness. This article aims to provide an overview of energy efficiency issues based on recently published articles. This article discusses the appropriate selection of robots, their programming, energy-efficient trajectory planning, and the monitoring of the operation of the robotic system to minimize energy consumption. Typical industrial applications of robots are also mentioned and discussed.

A Multi-Criteria Group Decision-Making Approach For Robot Selection Using Interval-Valued Intuitionistic Fuzzy Information And Aczel-Alsina Bonferroni Means

The process of identifying the most appropriate robot for a particular industrial task has grown challenging and more difficult in the fast-paced environment. It is merely driven by the complex evolution and continuous integration of modern characteristics and advanced features by various suppliers. Industrial robots are now widely available in the marketplace, each possessing a distinctive collection of skills, attributes, and requirements. However, the selection of optimal robots is heavily influenced by factors such as the manufacturing environment, product design, production system, and overall cost considerations. These factors directly impact the decision-making process. The ultimate goal for the decision maker is to pinpoint and choose the most suitable robot, capable of delivering the desired output while minimizing costs and catering to the specific requirements of the industry. So, to consider it, in this paper, the hybrid structure of the Aczel-Alsina (AA) and Bonferroni mean (BM) operators for interval-valued intuitionistic fuzzy (IVIF) environment has been proposed, which can show the interrelationship between the multiple criteria and assist the expertise in decision-making (DM) process. Moreover, the algorithm and methodology for the multi-criteria group decision-making (MCGDM) problem have been defined which is further utilized by solving a real-world problem to demonstrate the effectiveness and validity of the proposed method. At last, the comparison analysis between prior and proposed studies has been displayed, and then followed by the conclusion of the result.

Decision support methodology for the selection of industrial robots using BWM and TOPSIS methods

This paper presents a methodology for assisting the decision of selecting an industrial robot from a set of alternatives. The approach is based on the use of a minimal set of robot characteristic parameters, as selection criteria. For the selection of the solution from the set of alternatives the BWM method is used as a first step to calculate the weights of the criteria. The weights resulting from the BWM application are then used to rank the alternatives by means of a simple additive weighting approach. The same weights calculated with BWM are then used to achieve the hierarchy of alternatives using the TOPSIS method. Both approaches ultimately showed the same option as the best of the set of alternatives.

On the use of fuzzy preorders and asymmetric distances for multi-robot communication

&lt;abstract&gt;&lt;p&gt;One of the main problems to be addressed in a multi-robot system is the selection of the best robot, or group of them, to carry out a specific task. Among the large number of solutions provided to allocate tasks to a group of robots, this work focuses on swarm-like approaches, and more specifically on response-threshold algorithms, where each robot selects the next task to perform by following a Markov process. To the best of our knowledge, the current response-threshold algorithms do not provide any formal method to generate new transition functions between tasks. Thus, this paper provides, for the first time, a mathematical model, as based on the so-called fuzzy preorders, for the allocation of tasks to a collective of robots with communication capabilities. In our previous work, we proved that transitions in the aforementioned process can be modeled as fuzzy preorders, constructed through the aggregation of asymmetric distances, in such a way that each robot makes its decision without taking into account the decisions of its teammates. Now, we extend this model in such a way that each robot will take into account the number of robots previously allocated for each task. To implement this method, a very simple communication mechanism has been considered. Several simulations have been carried out in order to validate our approach. The results confirm that fuzzy preorders are able to model the evolution of the system when this type of communication is considered and show when and how the communication process improves the system's performance. Experimental results show the existence of a set of good values for the maximum communication distance between robots and that these values depend on the distribution of the tasks in the environment. Thus, in some cases, a better communication mechanism does not imply better results.&lt;/p&gt;&lt;/abstract&gt;

Robots selection for sine trigonometric (l1, l2, l3) neutrosophic sets using different aggregation operators

This article discusses a new approach to multiple attribute decision-making (MADM) based on sine trigonometric (ST) (l1, l2, l3) neutrosophic sets (NS). We discuss the concept of ST (l1, l2, l3) neutrosophic weighted averaging (NWA), ST (l1, l2, l3) neutrosophic weighted geometric (NWG), ST (l1, l2, l3) generalized neutrosophic weighted averaging (GNWA) and ST (l1, l2, l3) generalized neutrosophic weighted geometric (GNWG). We presented during our discussion showed an algorithm that used these operators. Extensive Hamming distances are illustrated numerically. Also included in this communication are discussions of idempotency, boundness, commutativity, and monotonicity for ST (l1, l2, l3) neutrosophic sets. By using them, you can find the best option faster, easier, and more conveniently. As a result, ST (l1, l2, l3) and more precise conclusions are more closely related. A comparison is made between some current models and those proposed to demonstrate the dependability and utility of the current models. Furthermore, fascinating findings were revealed in the study.

Analysis and Applications of Bipolar Complex Fuzzy Soft Power Dombi Aggregation Operators for Robot Selection in Artificial Intelligence

Analysis and Applications of Bipolar Complex Fuzzy Soft Power Dombi Aggregation Operators for Robot Selection in Artificial Intelligence

Multiple attribute group decision making approach for selection of robot under induced bipolar neutrosophic aggregation operators

In current piece of writing, we bring in the new notion of induced bipolar neutrosophic (BN) AOs by utilizing Einstein operations as the foundation for aggregation operators (AOs), as well as to endow having a real-world problem-related application. The neutrosophic set can rapidly and more efficiently bring out the partial, inconsistent, and ambiguous information. The fundamental definitions and procedures linked to the basic bipolar neutrosophic (BN) set as well as the neutrosophic set (NS), are presented first. Our primary concern is the induced Einstein AOs, like, induced bipolar neutrosophic Einstein weighted average (I-BNEWA), induced bipolar neutrosophic Einstein weighted geometric (I-BNEWG), as well as their different types and required properties. The main advantage of employing the offered methods is that they give decision-makers a more thorough analysis of the problem. These strategies whenever compare to on hand methods, present complete, progressively precise, and accurate result. Finally, utilizing a numerical representation of an example for selection of robot, for a problem involving multi-criteria community decision making, we propose a novel solution. The suitability ratings are then ranked to select the most suitable robot. This demonstrates the practicality as well as usefulness of these novel approaches.

A new approach to neural network via double hierarchy linguistic information: Application in robot selection

Robotization is necessary to keep up with the constant changes in production, which calls for a staff with robotics expertise. A manufacturing business must also have the ability to swiftly change its production method. But today the procedure is drawn-out and complicated. In this study, inverse kinematics functionality and a machine learning model have been used to simulate an industrial robot’s movement in a digital environment. By using machine learning, less time and money must be invested in developing the procedure and determining the robot’s route. In this article, feed-forward double hierarchy linguistic neural networks with estimation information for double hierarchy linguistic term sets are proposed. First defined were the Yager operational rules and Yager aggregation operators for the double hierarchy linguistic terms set. Following that, we’ll discuss fuzzy neurons, feed-forward neural networks, simple neural networks, hybrid neural networks, and the sigmoid function. After that, explain feed-forward, double-hierarchy linguistic neural networks, including how their output is calculated. The weight vector of expert’s information is calculated by using the entropy measure with the help of Yager aggregation operators. Finally, we use the Yager t-norms to determine the output date of feed-forward double hierarchy linguistic neural networks and also find the output data. Linguistic neural network with Yager T-norms apply to the Robot selection for manufacturing bussing. The proposed approach of linguistic neural network are compared with Extended TOPSIS methods and GRA method for ranking.

A visual imitation learning algorithm for the selection of robots’ grasping points

The short-run production and customization are increasingly common in the manufacturing industry, which results in the frequent adjustments of production lines. Industrial robots in these production lines are also required to quickly learn to perform new grasping tasks. However, the traditional approaches in grasping points selection, which rely on the geometric envelope models of objects in preset task space, can no longer meet the demands of fast-shifting production. Therefore, the present paper tackled the problem of grasping points selection with a novel CNN-based imitating learning framework. Our imitating model learns the correct grasping posture for objects from human grasping operations captured on camera. The experiments showed that this imitating learning algorithm can help a dual-arm robot master the correct grasping posture of an object within only 20 min. Compared to traditional geometric modeling-based methods, such as the pick-and-place module available in the Robot Operating System (ROS), this new approach can increase grasping planning efficiency by 26.1%.

Exploration–Exploitation Tradeoff in the Adaptive Information Sampling of Unknown Spatial Fields with Mobile Robots

Adaptive information-sampling approaches enable efficient selection of mobile robots’ waypoints through which the accurate sensing and mapping of a physical process, such as the radiation or field intensity, can be obtained. A key parameter in the informative sampling objective function could be optimized balance the need to explore new information where the uncertainty is very high and to exploit the data sampled so far, with which a great deal of the underlying spatial fields can be obtained, such as the source locations or modalities of the physical process. However, works in the literature have either assumed the robot’s energy is unconstrained or used a homogeneous availability of energy capacity among different robots. Therefore, this paper analyzes the impact of the adaptive information-sampling algorithm’s information function used in exploration and exploitation to achieve a tradeoff between balancing the mapping, localization, and energy efficiency objectives. We use Gaussian process regression (GPR) to predict and estimate confidence bounds, thereby determining each point’s informativeness. Through extensive experimental data, we provide a deeper and holistic perspective on the effect of information function parameters on the prediction map’s accuracy (RMSE), confidence bound (variance), energy consumption (distance), and time spent (sample count) in both single- and multi-robot scenarios. The results provide meaningful insights into choosing the appropriate energy-aware information function parameters based on sensing objectives (e.g., source localization or mapping). Based on our analysis, we can conclude that it would be detrimental to give importance only to the uncertainty of the information function (which would explode the energy needs) or to the predictive mean of the information (which would jeopardize the mapping accuracy). By assigning more importance to the information uncertainly with some non-zero importance to the information value (e.g., 75:25 ratio), it is possible to achieve an optimal tradeoff between exploration and exploitation objectives while keeping the energy requirements manageable.