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The article describes a description of a fundamentally new design, mathematical model and experimental research of a flexible arm with an anthropomorphic gripper for an industrial robot. The advantage of the proposed design of the robot arm in comparison with the known traditional technical solutions is achieved as close as possible to the functions of the human arm. This property significantly increases the versatility of the robot arm when performing various technological operations. Another difference from the known models of industrial robots is the presence of an anthropomorphic gripping device in the flexible arm, which allows you to service products with different shapes and arbitrary coordinates in space. In addition, the article for the first time proposes a method for calculating the parameters of a new hand and experimental studies of its functioning, which will allow engineers in the field of robotics to create similar designs. The economic effect of the proposed design is that the implementation of the movements of the proposed robot arm does not require separate electromechanical drives for each joint of the kinematic chain of the manipulator. This effect significantly reduces the cost of a robot arm while expanding its technological capabilities.

Structural Intervention Cardiology (SIC) is a miniinvasive intervention with a catheter based approach for cardiac surgery. Although SIC procedures are becoming increasingly popular, procedures are not ergonomic and technically demanding and, at the same time, high precision and accuracy in reaching target locations inside the human body are necessary for the success these procedures. Thus, there is therefore a need to develop a robust path planner framework to improve the accuracy in target reaching while minimizing interaction with anatomical structures. In this work a pre-operative path-planning method able to guide the catheter from the peripheral access to the desired target position with the needed orientation is proposed. The method exploits an Inverse Reinforcement Learning algorithm based on a combination of Behavioral Cloning (BC) and Generative Adversarial Imitation Learning (GAIL). The method was in-silico tested performing 50 intra-vascular and 70 intra-cardiac paths where the ratio between attempts in which the catheter reaches the target and total number of attempts, computation time, the difference between desired pose and the reached one were considered as validation metrics. Results show that the proposed method computes optimal path enabling the catheter to reach the target with an average error in position below 2 mm in the intra-vascular phase and below 1 mm in position and 6° in orientation in the intra-cardiac phase

The ability to navigate complex unstructured environments and carry out inspection tasks requires robots to be capable of climbing inclined surfaces and to be equipped with a sensor payload. These features are desirable for robots that are used to inspect and monitor offshore energy platforms. Existing climbing robots mostly use rigid actuators, and robots that use soft actuators are not fully untethered yet. Another major problem with current climbing robots is that they are not built in a modular fashion, which makes it harder to adapt the system to new tasks, to repair the system, and to replace and reconfigure modules. This work presents a 450 g and a 250 × 250 × 140 mm modular, untethered hybrid hard/soft robot-Limpet II. The Limpet II uses a hybrid electromagnetic module as its core module to allow adhesion and locomotion capabilities. The adhesion capability is based on negative pressure adhesion utilizing suction cups. The locomotion capability is based on slip-stick locomotion. The Limpet II also has a sensor payload with nine different sensing modalities, which can be used to inspect and monitor offshore structures and the conditions surrounding them. Since the Limpet II is designed as a modular system, the modules can be reconfigured to achieve multiple tasks. To demonstrate its potential for inspection of offshore platforms, we show that the Limpet II is capable of responding to different sensory inputs, repositioning itself within its environment, adhering to structures made of different materials, and climbing inclined surfaces.

This paper presents the use of a laser tracker for programming paths of industrial robots. The idea of operation, application, and the offered accuracy of the laser trackers are discussed. Then, the developed method of using a tracker for determining the path points of an industrial robot is presented. The requirements related to the developed method are indicated. The parameters of the industrial robot related to the repeatability and accuracy of the robots and the Absolute Accuracy option—increasing the accuracy—are characterized. The developed method is based on the accuracy of robots and the conversion of the position and orientation of individual coordinate systems. In the developed method, the robot is not used during programming and the programmer indicates the path points with the help of a retroreflector. The algorithm, which is the most important part of the developed method, has been implemented in the Rapid programming language to automate the programming process. This article provides an example of the use of the developed method to program the robot path during the grinding process. The advantages of the developed method are characterized and compared to existing solutions. In addition, the limitations and achieved accuracies are indicated.

In this article, the results of a nonlinear model predictive controller used to generate optimized trajectories for an omnidirectional industrial robot equipped with a spraying unit is presented. Results are provided for various trajectories along with consideration of controller performance.

The paper deals with a lead-through method of programming for industrial robots. The goal is to automatically reproduce 6DoF trajectories of a tool wielded by a human operator demonstrating a motion task. We present a novel motion-tracking system built around the HTC Vive pose estimation system. Our solution allows complete automation of the robot teaching process. Specific algorithmic issues of system calibration and motion data post-processing are also discussed, constituting the paper’s theoretical contribution. The motion tracking system is successfully deployed in a pilot application of robot-assisted spray painting.

Industry 4.0 is the term used to represent the fourth industrial revolution. In this, several technologies are used to improve results in production systems, including simulation, automation and connectivity. This article presents a feasibility study of automation of operational activities, in a group of CNC lathes, for gears machining operations. Four different systems have been proposed, using collaborative robots and a connected three-dimensional measuring machine. These were evaluated by discrete event simulations, with the Flexsim® software, which confirmed the superiority of the proposed system with one operator, robot and a measuring machine, for three lathes. The results present gains in productivity and cost reduction, using industry 4.0 technologies. http://dx.doi.org/10.18226/23185279.v8iss2p206

Purpose: the main purpose of this article is to analyze the main trends and directions of development of industrial robots, as well as the problems associated with their distribution. To achieve these goals, the following tasks were solved: analysis of the dynamics of the stock of industrial robots, the structure of the stock of robots by region (Europe, America, Asia / Australia), as well as the annual volumes and structure of world sales of robots by key industries; analysis of the main tasks of industrial robots, performed by them in these industries, and the directions of their use; analyze the dynamics of the robot fleet by industry in different countries (Japan, USA, South Korea, China, Germany, etc.); analysis of indicators and problems of using industrial robots in Russia.Methods: the research methodology consists in a comparative analysis of the use of industrial robots in different industries (automotive, food, chemical, electronic, etc.) based on statistical data by country. A systematic approach, tabular and graphical interpretation of information was applied, analysis of the dynamics of the levels of the time series, the calculation of growth indices of indicators.Results: the analysis showed that the use of industrial robots reduces injuries at the workplace, production costs and improves the quality of the final product, productivity, flexibility and safety, which contributes to a significant increase in their use in both developed and developing countries.Conclusions and Relevance: recently, robotization has become available even in non-industrial countries. The introduction of robotization into production processes increases the competitiveness of the economy. The acceleration of digitalization and automation, as well as the ease of use of industrial robots, are driving their proliferation. In Russia, the wider use of industrial robots, the development of the industrial Internet of things and the implementation of digitalization are possible only on the basis of the restoration and further development of mechanical engineering, electronic and other manufacturing industries.

International audience; This paper presents a robust hybrid fault-tolerant optimal predictive control (HFTPC), for an industrial robot arm under hybrid (electric and pneumatic) actuator faults and/or varying time-delays. Based on the error dynamics, estimated states, and a predictive controller, a new state feedback control law is proposed and implemented for the reformulation of the optimal control problem of a nonlinear faulty hybrid actuator system based on predictive control via linear matrix inequalities (LMIs). First, a robust MPC scheme is performed in which the future control sequence is used to compensate the varying time-delays. Then, a robust stable hybrid fault tolerant predivtive control is implemented to handle actuators faults to effect robust trajectory tracking. In fact, the stability of hybrid systems based on the proposed control scheme is a very sensitive criterion. Therefore, stability conditions are required for controlling the industrial arm under faulty hybrid (electric and pneumatic) actuator, based on the Lyapunov-Krasovskii (L-K) theory, less conservative stable conditions in terms of LMIs are given and used to ensure the asymptotically robust stability of closed-loop constrained system that dependent delay-range. To highlight the robustness and effectiveness of the proposed approach, a simulation study of an industrial robot arm example is proved, where the results showed the prompt and the accuracy of the proposed scheme.

One of the most pressing issues in the field of mobile robotics is that of power delivery. By being fundamentally unattached to the environment, these systems often employ batteries. However, this can have some limitations related to cost, battery life, etc. A promising approach is that of powered floors, where a specially designed contact array is connected to the robot, and slides on a surface in which conducting bands are laid out, providing a continuous and uninterrupted electrical connection. In this article, we provide a complete framework for the analysis and synthesis of these systems to be used in the field of mobile robotics. We study the problem both in terms of feasibility and in quantitative terms; in particular, we illustrate a methodology related to n -sided polygons, which takes into account a fuzzy representation of the bands, useful to analyze tolerances in the bands boundaries or in the position of the individual brushes. We support the theoretical framework with both numerical and experimental campaigns, and compare the results with existing state-of-the art solutions. Finally, we discuss possible further developments.

We present a numerical algorithm based on invariant set theory to evaluate the worst-case performance of PD-controlled mechanical systems affected by bounded disturbances. By performing a specific coordinate transformation, the search and computation of positive invariant sets is simplified. It is shown that, thanks to the preservation of problem structure, the proposed method allows to obtain tight, component-wise bounds on the states, which are especially useful for performance evaluation and tuning of a PD controller. The bounds are formally guaranteed and can be used for safety certification. The method is compared to ultimate boundedness, and the superior results are shown via numerical simulations.

Machine and manufacturing migrant workers in the new era are side by side cooperation and interaction of the new labor force form. Based on the dynamic survey of China’s floating population in 2011 and the data collected by the International Federation of Robotics, the Bartik instrument variable method is used to analyze the impact of industrial robots on the employment quality of the floating population in manufacturing industry at the city level. As the city scale expands, industrial robots have an inverted U-shaped effect on the employment quality of manufacturing migrant workers. Industrial robots have a positive U-shaped influence on the number of hours that migrant workers in manufacturing work, with an inflexion point of 1.3721 units per 10,000 workers. The influence of industrial robots on migrant workers’ working conditions in the manufacturing sector was U-shaped, and 1.668 units per 10,000 workers marked the tipping point. Nevertheless, industrial robots have an inverse influence on the occupation stability of migrant workers in the manufacturing industry. Precisely, the installation density of industrial robots in the manufacturing industry has a detrimental impact on the occupational stability of migrant employees. Industrial robots are negatively associated with the working conditions of migrant workers employed in manufacturing. There were detrimental effects on the employment quality of manufacturing migrant workers in cities with higher and lower population densities. In the end, for every manufacturing farmer using an industrial robot, the likelihood of being miserable and almost happy went up by 2.64 percent and 5.59 percent, respectively, while the likelihood of being happy went down by 7.62 percent.

Background: Human–robot collaboration is essential for efficient manufacturing and logistics as robots are increasingly used. Using industrial robots as part of an automation system results in many competitive benefits, including improved quality, efficiency, productivity, and reduced waste and errors. When robots are used in production, human coworkers’ psychological factors can disrupt operations. This study aims to examine the effect of employees’ negative attitudes toward robots on their acceptance of robot technology in manufacturing workplaces. Methods: A survey was conducted with employees in manufacturing companies to collect data on their attitudes towards robots and their willingness to work with them. Data was collected from 499 factory workers in Istanbul using a convenience sampling method, which allowed for the measurement of variables and the analysis of their effects on each other. To analyze the data, structural equation modeling was used. Results: The results indicate that negative attitudes towards robots have a significant negative effect on the acceptance of robot technology in manufacturing workplaces. However, trust in robots was found to be a positive predictor of acceptance. Conclusions: These findings have important implications for manufacturing companies seeking to integrate robot technology into their operations. Addressing employees’ negative attitudes towards robots and building trust in robot technology can increase the acceptance of robots in manufacturing workplaces, leading to improved efficiency and productivity.

Accurate kinematic modelling is pivotal in the safe and reliable execution of both contact and non-contact robotic applications. The kinematic models provided by robot manufacturers are valid only under ideal conditions and it is necessary to account for the manufacturing errors, particularly the joint offsets introduced during the assembling stages, which is identified as the underlying problem for position inaccuracy in more than 90% of the situations. This work was motivated by a very practical need, namely the discrepancy in terms of end-effector kinematics as computed by factory-calibrated internal controller and the nominal kinematic model as per robot datasheet. Even though the problem of robot calibration is not new, the focus is generally on the deployment of external measurement devices (for open loop calibration) or mechanical fixtures (for closed loop calibration). On the other hand, we use the factory-calibrated controller as an 'oracle' for our fast-recalibration approach. This allows extracting calibrated intrinsic parameters (e.g., link lengths) otherwise not directly available from the 'oracle', for use in ad-hoc control strategies. In this process, we minimize the kinematic mismatch between the ideal and the factory-calibrated robot models for a Kinova Gen3 ultra-lightweight robot by compensating for the joint zero position error and the possible variations in the link lengths. Experimental analysis has been presented to validate the proposed method, followed by the error comparison between the calibrated and un-calibrated models over training and test sets. National Research Foundation (NRF) Published version The work was conducted within the Delta-NTU Corporate Lab for Cyber-Physical Systems with funding supporting from Delta Electronics Inc. and the National Research Foundation (NRF) Singapore under the Corp Lab @ University Scheme and partly supported by the National Research Foundation, Singapore, under the NRF Medium Sized Centre scheme (CARTIN).

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. We investigated the use of impact sounds generated during exploratory behaviors in a robotic manipulation setup as cues for predicting object surface material and for recognizing individual objects. We collected and make available the YCB-impact sounds dataset which includes over 3,000 impact sounds for the YCB set of everyday objects lying on a table. Impact sounds were generated in three modes: (i) human holding a gripper and hitting, scratching, or dropping the object; (ii) gripper attached to a teleoperated robot hitting the object from the top; (iii) autonomously operated robot hitting the objects from the side with two different speeds. A convolutional neural network is trained from scratch to recognize the object material (steel, aluminium, hard plastic, soft plastic, other plastic, ceramic, wood, paper/cardboard, foam, glass, rubber) from a single impact sound. On the manually collected dataset with more variability in the speed of the action, nearly 60% accuracy for the test set (not presented objects) was achieved. On a robot setup and a stereotypical poking action from top, accuracy of 85% was achieved. This performance drops to 79% if multiple exploratory actions are combined. Individual objects from the set of 75 objects can be recognized with a 79% accuracy. This work demonstrates promising results regarding the possibility of using impact sound for recognition in tasks like single-stream recycling where objects have to be sorted based on their material composition. This work was supported by the project Interactive Perception-Action-Learning for Modelling Objects (IPALM) (H2020 – FET – ERA-NET Cofund – CHIST-ERA III / Technology Agency of the Czech Republic, EPSILON, no. TH05020001) and partially supported by the project MDM2016-0656 funded by MCIN/ AEI /10.13039/501100011033. M.D. was supported by grant RYC-2017-22563 funded by MCIN/ AEI /10.13039/501100011033 and by “ESF Investing in your future”. S.P. and M.H. were additionally supported by OP VVV MEYS funded project CZ.02.1.01/0.0/0.0/16 019/0000765 “Research Center for Informatics”. We thank Bedrich Himmel for assistance with sound setup, Antonio Miranda and Andrej Kruzliak for data collection, and Lukas Rustler for video preparation. This work was supported by the project Interactive Perception-Action-Learning for Modelling Objects (IPALM) (H2020 – FET – ERA-NET Cofund – CHIST-ERA III / Technology Agency of the Czech Republic, EPSILON, no. TH05020001) and partially supported by the project MDM2016-0656 funded by MCIN/ AEI /10.13039/501100011033. M.D. was supported by grant RYC-2017-22563 funded by MCIN/ AEI /10.13039/501100011033 and by “ESF Investing in your future”. S.P. and M.H. were additionally supported by OP VVV MEYS funded project CZ.02.1.01/0.0/0.0/16 019/0000765 “Research Center for Informatics”. We thank Bedrich Himmel for assistance with sound setup, Antonio Miranda and Andrej Kruzliak for data collection, and Lukas Rustler for video preparation.

Nowadays, manufacturing plants are required to be flexible to respond quickly to customer demands, adapting production and processes without affecting their efficiency. In this context, Industrial Robots (IRs) are a primary resource for modern factories due to their versatility which allows the execution of flexible, reconfigurable, and zero-defect manufacturing tasks. Even so, the control and programming of the commercially available IRs are limiting factors for their effective implementation, especially for dynamic production environments or when complex applications are required. These issues have stimulated the development of new technologies that support more efficient methods for robot control and programming. The goal of this research is to identify and evaluate the main approaches proposed in scientific papers and by the robotics industry in the last decades. After a critical review of the standard IR control schematic, the paper discusses the available control alternatives and summarizes their characteristics, range of applications, and remaining limitations.

The winding process of carbon fiber is not a new concept, but until now only a few studies have been made in carbon fiber tensioning and winding on complex shapes. The main purpose of the article is to analyze the possibility to conduct the carbon fiber winding using two industrial robots connected as master slave, one to realize the winding and the other to hold and rotate the mandrel, using an own design of an automatic fiber tensioning tool. To have control of the system a force transducer was implemented. The measured force was used to make trajectory adjustments for the robots, having a predefined trajectory. The results obtained from the experimental tests showed that the winding process with two robots and a tensioning system can realize fiber windings with variable tension in molds with complex configurations (3D) of the winding paths. In this way, compact structures of the constantly tensioned fiber bundle can be obtained, according to product requirements.

The sine eccentric shaft is one of the most important parts for industrial robot RV reducer, and its machining quality has immediate effect on the overall device performance. The eccentric shaft X-C linkage grinding model is built based on the tangential point tracing machining principle, and the method of calculating the roundness error for eccentric circle grinding is proposed here. The research is performed on non-uniform rational B-spline (NURBS) interpolation for the coordinates’ densification of eccentric shaft X-C linkage grinding, and the algorithm process is put forward. The process of eccentric grinding is simulated and analysed by wheel inversion envelope method. The results show that the eccentric tangent point tracking model proposed is correct. Compared with the linear interpolation, NURBS interpolation can remarkably improve the accuracy of eccentric shaft tangent point tracking grinding.

This paper presents a method to include unmodeled dynamics of load or a robot’s end-effector into algorithms for collision detection or general understanding of a robot’s operation context. The approach relies on the application of a previously developed modification of the Dynamic Time Warping algorithm, as well as a universally applicable algorithm for identifying kinematic parameters. The entire process can be applied to arbitrary robot configuration, and it does not require identification of dynamic parameters. The paper addresses the two main categories of contact tasks with unmodelled dynamics, which are determined based on whether the external contact force has a consistent profile in the end effector or base coordinate. Conclusions for representative examples analysed in the paper are applicable to tasks such as load manipulation, press bending, and crimping for the first type of forces and applications such as drilling, screwdriving, snap-fit, bolting, and riveting assembly for the latter category. The results presented in the paper are based on realistic testing with measurements obtained from an industrial robot.

The aviation industry is associated with high precision and accuracy standards of the manufactured components, and thus the need to ensure precise quality control. Measurement processes, depending on the manufactured components, take place before, during and after the processing stage. Optical scanners can be used for these measurements, the measurement results of which can be displayed on the operator panel or used to prepare a report. The innovative approach is to measure, compare the results with a pattern, send the deviations to a neural decision-making system, select the forces and send the results to a robot controller for adaptive machining. The presented proprietary solution includes a data acquisition system, a neural decision-making system and a robot that carries out the machining process via force control. The proposed solution was verified on aviation components. During the process parameter optimization stage for the diffuser and ADT gearbox, the points describing the change in width of the chamfer being performed and the blade thickness in the control sections were approximated.

We address the problem of insufficient quality of robot simulators to produce precise sensor readings for joint positions, velocities and torques. Realistic simulations of sensor readings are particularly important for real time robot control laws and for data intensive Reinforcement Learning of robot movements in simulation. We systematically construct two architectures based on Long Short-Term Memory to model the difference between simulated and real sensor readings for online and offline application. Our solution is easy to integrate into existing Robot Operating System frameworks and its formulation is neither robot nor task specific. We demonstrate robust behavior and transferability of the learned model between individual Franka Emika Panda robots. Our experiments show a reduction in torque mean squared error of at least one order of magnitude. The collected data set, the plug-and-play Realeasy model for the Panda robot and a reproducible real-time docker setup are shared alongside the code.22https://sites.google.com/ulund.org/realeasy

Orientador: Edson de Paula Ferreira Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica Resumo: Este trabalho apresenta contribuições no sentido de agilizar e otimizar a modelagem geométrica e dinâmica de robôs. A finalidade principal na utilização destes modelos é o desenvolvimento de estratégias de controle mais eficientes, que consigam compensar efeitos indesejáveis, quando é exigido um desempenho superior dos robôs. Estes modelos são de grande complexidade e sua obtenção manual, além de demorada, é extremamente árida e bastante sujeita a erros. Por este motivo, implementamos um sistema para a geração automática de modelos geométricos e modelos dinâmicos com base no formalismo de Lagrange, utilizando recursos para otimização destes modelos. É proposto um algoritmo eficiente para modelagem dinâmica, o qual elimina automaticamente um grande número de redundâncias. Este algoritmo é apresentado à nível de implementação Abstract: This work presents contributions aiming at time saving and model improvement in the generation of geometric and dynamic robot models. The main purpose is to enable the generation of models suited for use in the development of more efficient control strategies, 50 as to compensa te effects that become undesirable when a better robot performance is required. These are quite complex models and the manual derivation of them is tedious, costly (time-consuming) and often error-prone. 50, it was implemented a system for automatic generation of symbolic geometric and dynamic robot models based in the Lagrange formulation and that also cares about model optimization. It is also proposed an efficient algorithm for dynamic modelling, which automatically eliminates a great number of redundancies. This algorithm is presented in the implementation level Mestrado Automação Mestre em Engenharia Elétrica

Robots equipped with vision systems at the end-effector provide a powerful combination in industrial contexts. While much attention is dedicated to machine vision algorithms, the optimization of the vision system pose is not properly addressed (to increase object detection performance). A complete pipeline for such optimization is proposed. To this aim, Bayesian Optimization is employed. A Franka EMIKA Panda robot has been used as a robotic platform, equipped at its end-effector with an Intel© RealSense D400. Achieved results show the high-fidelity reconstruction of the real working environment for the offline optimization (i.e., performed simulations), together with capabilities of the proposed Bayesian Optimization-based approach to defining the sensor pose in a limited number of experimental trials (50 maximum iterations has been considered).

Presentation at the Hackathon organized by students of the Hackathon Technoise (IPB), Portugal.

One of the biggest challenges for the fault diagnosis research of industrial robots is that the normal data is far more than the fault data; that is, the data is imbalanced. The traditional diagnosis approaches of industrial robots are more biased toward the majority categories, which makes the diagnosis accuracy of the minority categories decrease. To solve the imbalanced problem, the traditional algorithm is improved by using cost-sensitive learning, single-class learning and other approaches. However, these algorithms also have a series of problems. For instance, it is difficult to estimate the true misclassification cost, overfitting, and long computation time. Therefore, a fault diagnosis approach for industrial robots, based on the Multiclass Mahalanobis-Taguchi system (MMTS), is proposed in this article. It can be classified the categories by measuring the deviation degree from the sample to the reference space, which is more suitable for classifying imbalanced data. The accuracy, G-mean and F-measure are used to verify the effectiveness of the proposed approach on an industrial robot platform. The experimental results show that the proposed approach’s accuracy, F-measure and G-mean improves by an average of 20.74%, 12.85% and 21.68%, compared with the other five traditional approaches when the imbalance ratio is 9. With the increase in the imbalance ratio, the proposed approach has better stability than the traditional algorithms.

This paper presents an optimal sensor placement method for vibration signal acquisition in the field of industrial robot health monitoring and fault diagnosis. Based on the general formula of Bayes and relative entropy, the evaluation function of sensor placement is deduced, and the modal confidence matrix is used to express the redundancy of sensor placement. The optimal placement of vibration sensors is described as a discrete variable optimization problem, which is defined as whether the existing sensor layout can obtain joint state information efficiently. The initial layout of the sensor was obtained from the structural simulation results of the industrial robots, and the initial layout was optimized by the derived objective function. The efficiency of the optimized layout in capturing joint state information is proven by the validation experiment with a simulation model. The problem of popularizing the optimization method in engineering is solved by a verification experiment without a simulation model. The optimal sensor placement method provides a theoretical basis for industrial robots to acquire vibration data effectively.

Industrial collaborative robots will be used in unstructured scenarios and a large variety of tasks in the near future. These robots shall collaborate with humans, who will add uncertainty and safety constraints to the execution of industrial robotic tasks. Hence, trustworthy collaborative robots must be able to reason about their collaboration's requirements (e.g., safety), as well as the adaptation of their plans due to unexpected situations. A common approach to reasoning is to represent the knowledge of interest using logic-based formalisms, such as ontologies. However, there is not an established ontology defining notions such as collaboration or adaptation yet. In this article, we propose an Ontology for Collaborative Robotics and Adaptation (OCRA), which is built around two main notions: collaboration, and plan adaptation. OCRA ensures a reliable human-robot collaboration, since robots can formalize, and reason about their plan adaptations and collaborations in unstructured collaborative robotic scenarios. Furthermore, our ontology enhances the reusability of the domain's terminology, allowing robots to represent their knowledge about different collaborative and adaptive situations. We validate our formal model, first, by demonstrating that a robot may answer a set of competency questions using OCRA. Second, by studying the formalization's performance in limit cases that include instances with incongruent and incomplete axioms. For both validations, the example use case consists in a human and a robot collaborating on the filling of a tray., This work has been partially funded by MCIN/ AEI /10.13039/501100011033, Spain, under the project CHLOE-GRAPH (PID2020-119244GB-I00); by MCIN/ AEI /10.13039/501100011033, Spain, and by the “European Union NextGenerationEU/PRTR, Spain, under the project COHERENT (PCI2020-120718-2); and by the European Commission- NextGenerationEU, through CSIC’s Thematic Platforms (PTI+ Neuro-Aging), Spain. S. Borgo is partially supported by the European project OntoCommons (GA 958371), Italy. A. Olivares-Alarcos is supported by the European Social Fund and the Ministry of Business and Knowledge of Catalonia, Spain, through the FI 2020 grant.

Tests of the power supply system of "SpeedLine magnetic" ultra-fast industrial robot for IML labeling were carried out to analyze the effect of the active power filter (APF) parameters on the power quality. at the target duty cycle. Tests were performed with the target duty cycle. Linear synchronous motors with permanent magnets were used to drive the main axis of the robot. The Sinexcel 15A active filter was used in the power supply system.

In the context of human–robot collaborative shared environments, there has been an increase in the use of optical motion capture (OMC) systems for human motion tracking. The accuracy and precision of OMC technology need to be assessed in order to ensure safe human–robot interactions, but the accuracy specifications provided by manufacturers are easily influenced by various factors affecting the measurements. This article describes a new methodology for the metrological evaluation of a human–robot collaborative environment based on optical motion capture (OMC) systems. Inspired by the ASTM E3064 test guide, and taking advantage of an existing industrial robot in the production cell, the system is evaluated for mean error, error spread, and repeatability. A detailed statistical study of the error distribution across the capture area is carried out, supported by a Mann–Whitney U-test for median comparisons. Based on the results, optimal capture areas for the use of the capture system are suggested. The results of the proposed method show that the metrological characteristics obtained are compatible and comparable in quality to other methods that do not require the intervention of an industrial robot.

The mechanical system tests of the high-speed IML labeling robot "SpeedLine Magnetic" were carried out in order to analyze the effects of the drive system's operating parameters on vibration and noise.

Tests of the drive system of the ultra-fast robot "SpeedLine magnetic" for labeling with the IML method were carried out in order to analyze the influence of the operating parameters of the drive system on the correctness and quality of the drive system.

The present paper describes an algorithm for the identification of the dynamic parameters of an industrial robot. This approach is based on the possibility to write robot dynamics in a linear form with respect to a specific set of dynamic parameters. To properly detect them, the coefficients of a 5th order Fast Fourier Series (FFS) trajectory have been optimized using a genetic algorithm. Such identification trajectory has been then commanded to a UR5 collaborative robot from Universal Robots and experimental joints torques have been recorded at a frequency of 125 Hz. Base dynamic parameters were identified using least square errors optimization reaching low standard deviations. The algorithm has been validated with a second persistent trajectory with good results. Temperature effects on friction coefficients have been analyzed by running two identification processes: one just after the first power-up of the robot and the other one after a half an hour warm-up.

Robots are increasingly exploited in production plants, with the need to learn and to adapt themselves to new tasks. This paper focuses on the investigation of machine learning techniques to make a sensorless robot able to learn and optimize an industrial assembly task. Relying on sensorless Cartesian impedance control, a task-trajectory learning algorithm exploiting a limited number of human’s demonstrations (based on Hidden Markov Model), and an autonomous optimization procedure (based on Bayesian Optimization) are proposed to learn and optimize the assembly task. To validate the proposed methodology, an assembly task of a gear into its square-section shaft has been considered. A Franka EMIKA Panda manipulator has been used as a test platform. The experiments show the effectiveness of the proposed strategy, making the robot able to learn and optimize its behaviour to accomplish the assembly task, even in the presence of uncertainties.

Learning algorithms are becoming popular in industrial manufacturing thanks to their promise to make a robot conscious of its surroundings and capable of human-like abilities, gaining greater flexibility with respect to traditional robotic systems. The aim is to operate also complex tasks without the need for explicit instructions, permitting the creation of fully autonomous systems where human operators are not included. A panoramic of the current state of the art in industrial fields is presented, starting from object recognition and grasping pose detection, to task planning and applications based on demonstrations by the operator.

Industrial robotics is a branch of robotics that gained paramount importance in the last century. The presence of robots totally revolutionized the industrial environment in just a few decades. In this paper, a brief history of industrial robotics in the 20th century will be presented, and a proposal for classifying the evolution of industrial robots into four generations is set forward. The characteristics of the robots belonging to each generation are mentioned, and the evolution of their features is described. The most significant milestones of the history of industrial robots, from the 1950’s to the end of the century, are mentioned, together with a description of the most representative industrial robots that were designed and manufactured in those decades.

This work proposes a Kinect V2-based visual method to solve the human dependence on the yarn bobbin robot in the grabbing operation. In this new method, a Kinect V2 camera is used to produce three-dimensional (3D) yarn-bobbin point cloud data for the robot in a work scenario. After removing the noise point cloud through a proper filtering process, the M-estimator sample consensus (MSAC) algorithm is employed to find the fitting plane of the 3D cloud data; then, the principal component analysis (PCA) is adopted to roughly register the template point cloud and the yarn-bobbin point cloud to define the initial position of the yarn bobbin. Lastly, the iterative closest point (ICP) algorithm is used to achieve precise registration of the 3D cloud data to determine the precise pose of the yarn bobbin. To evaluate the performance of the proposed method, an experimental platform is developed to validate the grabbing operation of the yarn bobbin robot in different scenarios. The analysis results show that the average working time of the robot system is within 10 s, and the grasping success rate is above 80%, which meets the industrial production requirements.

An optimal and smooth trajectory for industrial robots has a positive impact on reducing the execution time in an operation and the vibration in their joints. In this paper, a methodology for the time-optimal and jerk-continuous trajectory planning of industrial robots is proposed. The entire trajectory is interpolated in the joint space utilizing fifth-order B-splines and then optimized by a hybrid whale optimization algorithm and genetic algorithm (WOA-GA). Two objective functions, including the integral of the squared jerk along the entire trajectory and the total execution time, are minimized to obtain the optimal entire trajectory. A fifth-order B-spline interpolation technique enables the achievement of a jerk-continuous trajectory, while respecting the kinematic limits of jerk, acceleration and velocity. WOA-GA is utilized to solve the time-jerk optimal trajectory planning problem with nonlinear constraints. The proposed hybrid optimization algorithm yielded good results and achieved the time-jerk optimal trajectory better under kinematic constraints compared to the genetic algorithm, whale optimization algorithm, improved whale optimization algorithm with particle swarm optimization and adaptive cuckoo search algorithm. The numerical results show the competent performances of the proposed methodology to generate trajectories with high smooth curves and short total execution time.

Bin picking is a challenging problem that involves using a robotic manipulator to remove, one-by-one, a set of objects randomly stacked in a container. In order to provide ground truth data for evaluating heuristic or machine learning perception systems, this paper proposes using simulation to create bin picking environments in which a procedural generation method builds entangled tubes that can have curvatures throughout their length. The output of the simulation is an annotated point cloud, generated by a virtual 3D depth camera, in which the tubes are assigned with unique colors. A general metric based on micro-recall is proposed to compare the accuracy of point cloud annotations with the ground truth. The synthetic data is representative of a high quality 3D scanner, given that the performance of a tube modeling system when given 640 simulated point clouds was similar to the results achieved with real sensor data. Therefore, simulation is a promising technique for the automated evaluation of solutions for bin picking tasks.

Software reuse has potential for educational purposes since it uses decomposition and abstraction, two necessary skills to learn programming. Software reuse techniques require abstractions that are not obvious to students or even to professionals. Taking advantage of these techniques, students can learn computer programming in a productive and organized way. This paper proposes to use the Software Product Line (SPL) reuse technique as a strategy for learning to program industrial robots with the Arduino platform. First, the paper explains SPL construction and application with first-year university students. The SPL proposes abstractions close to the industrial robots domain with a simplified variability. The paper uses the case study method to show the feasibility of using the SPL approach in a learning environment. In this evaluation, students reused 38% to 43% of the total code needed to program the robot. It represents an improvement in the time it takes students to program industrial robotics solutions facilitating their learning. In addition, the paper unveils some limitations related to usability, specific knowledge, and some exploitable technologies.

International audience; We propose novel haptic guidance methods for a dual-arm telerobotic manipulation system, which are able to deal with several different constraints, such as collisions, joint limits, and singularities. We combine the haptic guidance with shared-control algorithms for autonomous orientation control and collision avoidance meant to further simplify the execution of grasping tasks. The stability of the overall system in various control modalities is presented and analyzed via passivity arguments. In addition, a human subject study is carried out to assess the effectiveness and applicability of the proposed control approaches both in simulated and real scenarios. Results show that the proposed haptic-enabled shared-control methods significantly improve the performance of grasping tasks with respect to the use of classic teleoperation with neither haptic guidance nor shared control.

This review is dedicated to the advanced applications of robotic technologies in the industrial field. Robotic solutions in areas with non-intensive applications are presented, and their implementations are analysed. We also provide an overview of survey publications and technical reports, classified by application criteria, and the development of the structure of existing solutions, and identify recent research gaps. The analysis results reveal the background to the existing obstacles and problems. These issues relate to the areas of psychology, human nature, special artificial intelligence (AI) implementation, and the robot-oriented object design paradigm. Analysis of robot applications shows that the existing emerging applications in robotics face technical and psychological obstacles. The results of this review revealed four directions of required advancement in robotics: development of intelligent companions; improved implementation of AI-based solutions; robot-oriented design of objects; and psychological solutions for robot–human collaboration.

To make the human-robot collaboration effective, it may be necessary to provide robots with &ldquo, senses&rdquo, like vision and hearing. Task-oriented man-machine speech communication often relies on the use of abstract terms to describe objects. Therefore it is necessary to correctly map those terms into images of proper objects in a camera&rsquo, s field of view. This paper presents the results of our research in this field. A novel method for contour identification, based on flexible editable contour templates (FECT), has been developed. We demonstrate that existing methods are not appropriate for this purpose because it is difficult to formulate general rules that humans employ to rank shapes into proper classes. Therefore, the rules for shape classification should be individually formulated by the users for each application. Our aim was to create appropriate tool facilitating formulation of those rules as it could potentially be a very labor-intensive task. The core of our solution is FCD (flexible contour description) format for description of flexible templates. Users will be able to create and edit flexible contour templates, and thus, adjust image recognition systems to their needs, in order to provide task-oriented communication between humans and robots.

This manuscript focuses on methodological and technological advances in the field of health assessment and predictive maintenance for industrial robots. We propose a non-intrusive methodology for industrial robot joint health assessment. Torque sensor data is used to create a digital signature given a defined trajectory and load combination. The signature of each individual robot is later used to diagnose mechanical deterioration. We prove the robustness and reliability of the methodology in a real industrial use case scenario. Then, an in depth mechanical inspection is carried out in order to identify the root cause of the failure diagnosed in this article. The proposed methodology is useful for medium and long term health assessment for industrial robots working in assembly lines, where years of almost uninterrupted work can cause irreversible damage.

Trabajo presentado en la 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), celebrada en Nápoles (Italia) del 31 de agosto al 4 de septiembre de 2020, Industrial robots are evolving to work closely with humans in shared spaces. Hence, robotic tasks are increasingly shared between humans and robots in collaborative settings. To enable a fluent human robot collaboration, robots need to predict and respond in real-time to worker's intentions. We present a method for early decision using force infor-mation. Forces are provided naturally by the user through the manipulation of a shared object in a collaborative task. The proposed algorithm uses a recurrent neural network to recognize operator's intentions. The algorithm is evaluated in terms of action recognition on a force dataset. It excels at detecting intentions when partial data is provided, enabling early detection and facilitating a quick robot reaction.

Con el proyecto objeto de la presente propuesta se pretende mejorar el desempeño de los sistemas utilizados actualmente para la fabricación de prótesis activas mediante impresión 3D. Este trabajo se encuadra en la linea de cooperación en Aprendizaje-Servicio (ApS) iniciada con la Fundación Rafa Puede en el entorno de dispositivos de tecnología asistiva para la atención a la diversidad funcional. Los actuadores utilizados en los dispositivos protésicos impresos en 3D presentan serias limitaciones en relación con el par máximo que limitan su rendimiento en escenarios reales de la vida cotidiana. Por ello se pretende estudiar la viabilidad de sustituir los servos tradicionalmente utilizados en estos sistemas por actuadores lineales de reducido tamaña basados en motores de paso. Escuela Técnica Superior de Ingeniería Industrial Universidad Politécnica de Cartagena

Статья содержит описание основных понятий и тенденций развития промышленных роботов. Рассматриваются основные принципы разработки и применения роботов в каждом поколении, при которых промышленные роботы оказывают наибольшую эффективность, когда необходимо обеспечить стабильность производственных процессов.The article contains a description of the basic concepts and development trends of industrial robots. The basic principles of the development and use of robots in each generation are considered, in which industrial robots are most effective when it is necessary to ensure the stability of production processes.

International audience; Redundant manipulators are usually required to perform tasks in the operational space, but collision-free path planning is computed in the configuration space. Limiting the deviation with respect to the collision-free configuration-space trajectory may allow the robot to avoid collisions without modifying the primary task. This paper proposes a method to guarantee that the solution of the inverse kinematic problem deviates from the nominal joint-space trajectory less than a desired threshold. The excursion limitation is ensured by means of linear constraints and the automatic regulation of the weights of secondary tasks. Numerical and experimental results prove the validness of the proposed approach.