Intelligent and Collaborative Robots

With the advent of the Industry 4.0 era, the application of collaborative robots (cobots) in industrial fields has greatly increased. Intelligent collaborative robots or smart cobots enable high flexibility by combining the human ability to adapt to new tasks with automated robots (precision, repeatability) performance. This paper will analyze the current application status and usage of collaborative robots in the various industrial fields, and its benefits and disadvantages from the discussion. Firstly, this paper the defines collaborative robots and explains the difference between cobots and traditional industrial robots. Then, it analyzes the specific application cases of collaborative robots in automotive manufacturing, electronics and electrical appliances, food and beverage, pharmaceutical and chemical industries. Subsequently, this paper introduces the main technologies linked to collaborative robots, including sensors, Human-Robot Interaction (HRI), machine learning and safety technologies. Through the study of actual application cases, this paper summarizes the significant advantages of collaborative robots to improve productivity, reduce costs, and optimize the working environment. Meanwhile, this paper also discusses the current technical challenges, regulatory and standard issues as well as marketing difficulties for collaborative robots, and looks at future development trends and research directions. The results demonstrate that the utilization of collaborative robots in industry has a promising future and research and practical significance as well

This paper describes design and implementation of remote lab with industrial robot accessible through the web based on Moodle portal, Easy Java Simulations (EJS) and Arduino Sw & Hw. The main purpose of this lab is to improve study, training and programming knowledge in industrial and service robotics for students, teachers of secondary vocational schools and company workers that deal with problems that arise on real robotic workplaces. This lab allows the user to work from their homes and operates with industrial robot at real workplace. Such remote lab can also enable users to use expensive lab equipment, which is not usually available to all persons. Practical example of application of the lab with industrial robot on Department of Robotics, Technical University of Kosice, Slovakia is presented.

In recent years, collaborative robots have been introduced into production systems as a new generation of industrial robots, sharing work with workers. Unlike industrial robots and workers in the past to work in isolation, collaborative robots and workers can often share work tasks and space. The introduction of these collaborative robots makes human-robot collaboration (HRC) more and more important, which is an emerging technology in the field of production systems. According to the characteristics of workers and collaborative robots, a measurement model of assembly complexity is first proposed. The assembly tasks are analyzed from the three aspects of process depth, operation process, and cognitive process, and the complexity of each assembly task is evaluated. The indicator of the machine-machine collaboration attribute measures each task to determine whether the task can be completed by a collaborative robot; finally, an assembly task allocation evaluation model is established, to ensure the assembly complexity of each worker’s task is balanced, so that the cognitive load assigned to the robot task is lower than the worker’s task, to maximize production efficiency and reduce the assembly system cost.

PurposeSuccessful sensorless collision detection by a robot depends on the accuracy with which the external force/torque can be estimated. Compared with collaborative robots, industrial robots often have larger parameter values of their dynamic models and larger errors in parameter identification. In addition, the friction inside a reducer affects the accuracy of external force estimation. The purpose of this paper is to propose a collision detection method for industrial robots. The proposed method does not require additional equipment, such as sensors, and enables highly sensitive collision detection while guaranteeing a zero false alarm rate.Design/methodology/approachThe error on the calculated torque for a robot in stable motion is analyzed, and a typical torque error curve is presented. The variational characteristics of the joint torque error during a collision are analyzed, and collisions are classified into two types: hard and soft. A pair of envelope-like lines with an effect similar to that of the true envelope lines is designed. By using these envelope-like lines, some components of the torque calculation error can be eliminated, and the sensitivity of collision detection can be improved.FindingsThe proposed collision detection method based on envelope-like lines can detect hard and soft collisions during the motion of industrial robots. In repeated experiments without collisions, the false alarm rate was 0 per cent, and in repeated experiments with collisions, the rate of successful detection was 100 per cent. Compared with collision detection method based on symmetric thresholds, the proposed method has a smaller detection delay and the same detection sensitivity for different joint rotation directions.Originality/valueA collision detection method for industrial robots based on envelope-like lines is proposed in this paper. The proposed method does not require additional equipment or complex algorithms, and highly sensitive collision detection can be achieved with zero false alarms. The proposed method is low in cost and highly practical and can be widely used in applications involving industrial robots.

The notion of work goes through major changes caused by the development of technology, and it is assumed that the development of sophisticated robotization and artificial intelligence will undermine the existence of work. Artificial and robotic intelligence will create more jobs, not mass unemployment, as long as innovation is guided responsibly. Cobots, or collaborative robots, are typically intended for physical interaction with people in a common workplace. There is no doubt that the world of collaborative robots is on the rise, so labour law will have to distinguish between non-human workers (dwarfs, industrial robots, etc.) and human workers. Regulations in the field will evolve, meaning that provisions will be needed which will determine, at a minimum, what the relationship between the two categories of workers will be according to the specificity of the activity as well as other aspects. Romania still has a low density of 15 robots per 10,000 employees, with a national interest in the topic, which is a result of the adoption in 2015 of the National Strategy on the Digital Agenda Romania 2020. Replacing human labour with robots is no longer just a discussion, it is a reality; it is not just a sci-fi issue, it is something society should contemplate and anticipate by updating legislation and social protection.

Robotics standardization can be broadly divided into industrial robots and service robots, primarily driven by safety and performance considerations. However, as robotic functions become more advanced, and the application fields are becoming more segmented. Recently, with advancements in cloud computing and physical AI, the intelligence of robots is being enhanced, leading to applications that integrate robotic technology with standards related to cloud, IoT, and artificial intelligence. This paper reports on the international standardization trends promoted by ISO, IEC, ITU-T, IEEE, and OMG, and the domestic standardization activities of KOROS and KS, and analyzes the current status and issues of the modularity standards for service robots being promoted by ISO TC299 Robotics. ISO is engaged in the standardization of both industrial and service robots, while IEC is focusing on cleaning robots, household mobile robots, and medical robots. IEEE has published several standards related to 2D/3D map, ontology, and robot ethics, and has recently initiated the standardization of humanoid robots. OMG is working towards interoperability of robots, while standards linking robotics with IT are being published through ITU-T. The information model for service robot modules will enable the reuse of modules produced by applying modular design principles and the development of robots by combining modules, and will serve as the basis for creating a robot ecosystem in the form of subscription services in the future.

Implementation of industrial robots worldwide -not only typical caged robots, manipulating heavy machinery and performing repetitive tasks, but also collaborative robots-, comes with the challenge of guaranteeing the safety of human operators, whether they work aside from the robot or in its vicinity. The UNE-EN ISO10218 safety standards for industrial robots set that robots and people can work in common spaces if robots have safety devices or are supported by them, to avoid hurt human operators. This paper introduces an approach that allows estimating the orientation and movement trend of a person in the vicinity of a robotized industrial task, aiming to avoid the collision between human and robot. As a previous requirement of the approach, a PointNet architecture was trained with the point clouds obtained from Depth images of a proprietary RGB-D image dataset. This task, in turn, required detecting people in the corresponding RGB images, through the application of a pre-trained saliency algorithm. To estimate the orientation of the detected person, a modified Biternion network was trained with the resized images from the same proprietary database. At evaluating the system, depth images captured by a Kinect sensor were used as inputs, then, for a set of four iterations (four frames), the movement trend was calculated since the orientation of the person was known for every frame. The prediction capability of the proposed approach was evaluated with three groups of images and resulted in a general precision greater than 0.5.KeywordsConvolutional neural networksOrientation estimationIndustrial robot safetyRGB-D imagesCollision detection

As full automation is often not achievable in flexible production, collaborative robot systems offer potential for an increase in productivity for manual systems. These systems however pose great challenges concerning planning and engineering. We define main requirements for industrial human robot collaboration simulation. It is found that current simulation systems in research and industry cannot sufficiently support the digital planning of such systems. We introduce a framework for a human-in-the-loop system capable of simulation and virtual commissioning of industrial collaborative robot systems. For this, we implement a combined virtual reality, motion capturing and object tracking architecture into an industry standard robot simulation software. We validate our system on real use cases from power electronics production implementing real safety controllers and evaluate the fulfillment of the defined requirements.

Much of the technology that makes robots reliable, human friendly, and industrial robotics adaptable for numerous applications has emerged from manufacturers of industrial robots. With an estimated installation base in 2014 of about 1.5 million units, some 171000 new installations in that year and an annual turnover of the robotics industry estimated to be US$ 32 billion, industrial robots are by far the largest commercial application of robotics technology today. The foundations for robot motion planning and control were initially developed with industrial applications in mind. These applications deserve special attention in order to understand the origin of robotics science and to appreciate the many unsolved problems that still prevent the wider use of robots in today’s agile manufacturing agile manufacturing environments. In this chapter, we present a brief history and descriptions of typical industrial robotics applications and at the same time we address current critical state-of-the-art technological developments. We show how robots with different mechanisms fit different applications and how applications are further enabled by latest technologies, often adopted from technological fields outside manufacturing automation. We will first present a brief historical introduction to industrial robotics with a selection of contemporary application examples which at the same time refer to a critical key technology. Then, the basic principles that are used in industrial robotics and a review of programming methods will be outlined. We will also introduce the topic of system integration particularly from a data integration point of view. The chapter will be closed with an outlook based on a presentation of some unsolved problems that currently inhibit wider use of industrial robots.

Precision component displacement, processing, and manipulation in an industrial environment require the high-precision positioning and orientation of industrial robots. However, industrial robots’ positioning includes uncertainties due to assembly and manufacturing tolerances. It is therefore required to use calibration techniques for industrial robot parameters. One of the major sources of uncertainty is the one associated with industrial robot geometrical parameter values. In this paper, using multi-objective meta-heuristic optimization approaches and optical metrology measurements, more accurate Denavit–Hartenberg (DH) geometrical parameters of an industrial robot are estimated. The sensor data used to perform this calibration are the absolute 3D position readings using a highly accurate laser tracker (LT) and industrial robot joint angle readings. Other than position accuracy, the mean absolute deviation of the DH parameters from the manufacturer’s given parameters is considered as the second objective function. Therefore, the optimization problem investigated in this paper is a multi-objective one. The solution to the multi-objective optimization problem is obtained using different evolutionary and swarm optimization approaches. The evolutionary optimization approaches are nondominated sorting genetic algorithms and a multi-objective evolutionary algorithm based on decomposition. The swarm optimization approach considered in this paper is multi-objective particle swarm optimization. It is observed that NSGAII outperforms the other two optimization algorithms in terms of a more diverse Pareto front and the function corresponding to the positional accuracy. It is further observed that through using NSGAII for calibration purposes, the root mean squared for positional error has been improved significantly compared with nominal values.

Over the years industrial robots had been used for numerous of repeatable tasks in different fields of industry, especially in automotive. Due to the safety reasons, robotized working stations had to be isolated by safety cell. Nowadays we can observe a strong trend of automation and robotization of production processes, which is considered as the fourth industrial revolution. Industry 4.0 has brought new opportunities and challenges. One of the key directions of Industry 4.0 is Human-Robot Collaboration (HRC). According to this concept, collaborative robot, which is equipped with appropriate safety and collision avoidance systems, can assist the human in the most effortable and monotonic tasks in the same workspace without additional safety guard. In this paper the characteristic of Human-Robot Collaboration (HRC), safety regulations, as well as application examples of collaborative robots are presented.

The integration of intention recognition systems in industrial collaborative robotics is crucial for improving safety and efficiency in modern manufacturing environments. This review paper looks at frameworks that enable collaborative robots to understand human intentions. This ability is essential for providing effective robotic assistance and promoting seamless human–robot collaboration, particularly in enhancing safety, improving operational efficiency, and enabling natural interactions. The paper discusses learning techniques such as rule-based, probabilistic, machine learning, and deep learning models. These technologies empower robots with human-like adaptability and decision-making skills. It also explores cues for intention recognition, categorising them into physical, physiological, and contextual cues. It highlights how implementing these various sensory inputs sharpen the interpretation of human intentions. Additionally, the discussion assesses the limitations of current research, including the need for usability, robustness, industrial readiness, real-time processing, and generalisability across various industrial applications. This evaluation identifies future research gaps that could improve the effectiveness of these systems in industrial settings. This work contributes to the ongoing conversation about the future of collaborative robotics, laying the foundation for advancements that can bridge the gap between human and robotic interactions. The key findings point out the significance of predictive understanding in promoting safer and more efficient human–robot interactions in industrial environments and provide recommendations for its use.

Industrial Robots (IR) has dominant functional position in the Robotized Manufacturing Systems (RMS). Knowing the features of IR determines success and effectivity of work RMS. The working reliability of IR explains in complex the quality of function of IR in the concrete working conditions. For testing IR, the standard ISO 9283 was made, the standard has positive results in praxis. Our experience insist on the need of testing IR in wider range mainly if the prototype or difficult IR application is considered. Several years experience obtained within testing IR on the workplaces of authors could be characterised by this contribution.

Measuring performances of collaborative robots in Industry 4.0 applications is an open research area since the emergence of collaborative and mobile robots as a support for semi-automatic manufacturing processes. A compelling management problem is the definition of convenient performance measures on which to assess the new generation of robots, to improve process performances both at the robotic cell design stage and at the production stage. A consequent problem is to gather the required data to measure performances. Data must be obtained automatically and in real time. Different levels of communication protocols have to be harmonized in order to transfer data from robots and other factory machines to the cloud on the internet and eventually to the production control system. A case study allows to demonstrate the operation of data acquisition system for collaborative and mobile robots and the real–time monitoring dashboard. The outcome of the study is the gathering of data at field level, the evaluation of robot performances at machine level in order to execute the real time production control at factory level.

This paper describes the design of a force measurement and communication system for the force measuring system in industrial robots. The force measurement and communication system is composed of a multi-axis force sensor and a controller for measuring the forces (x-direction force, y-direction force and z-direction force) and sending the measured forces to the robot's controller (PLC: Programmable Logic Controller). In this paper, the force measurement and communication system was designed and fabricated by using a DSP (Digital Signal Processor). An environment test and a grinding and deburring test using an industrial robot with the force measurement and communication system with three-axis force sensor were carried out to characterize the system. The tests showed that the system could safely measure the forces from the three-axis force sensor and send the measured forces to the industrial robot's controller while the grinding and deburring test was performed. Thus, it is thought that the fabricated force measurement and communication system could be used for controlling the force for an industrial robot's grinding and deburring.