*Review* **Human–Robot Collaboration in Manufacturing Applications: A Review**

**Eloise Matheson 1, Riccardo Minto 2,\*, Emanuele G. G. Zampieri 2, Maurizio Faccio <sup>3</sup> and Giulio Rosati <sup>2</sup>**


Received: 30 September 2019; Accepted: 3 December 2019; Published: 6 December 2019

**Abstract:** This paper provides an overview of collaborative robotics towards manufacturing applications. Over the last decade, the market has seen the introduction of a new category of robots—collaborative robots (or "cobots")—designed to physically interact with humans in a shared environment, without the typical barriers or protective cages used in traditional robotics systems. Their potential is undisputed, especially regarding their flexible ability to make simple, quick, and cheap layout changes; however, it is necessary to have adequate knowledge of their correct uses and characteristics to obtain the advantages of this form of robotics, which can be a barrier for industry uptake. The paper starts with an introduction of human–robot collaboration, presenting the related standards and modes of operation. An extensive literature review of works published in this area is undertaken, with particular attention to the main industrial cases of application. The paper concludes with an analysis of the future trends in human–robot collaboration as determined by the authors.

**Keywords:** collaborative robotics; cobots; human–robot interaction; literature review

#### **1. Introduction**

Traditional industrial robotic systems require heavy fence guarding and peripheral safety equipment that reduce flexibility while increasing costs and required space. The current market, however, asks for reduced lead times and mass customization, thus imposing flexible and multi-purpose assembly systems [1]. These needs are particularly common for small- and medium-sized enterprises (SMEs). Collaborative robots (or cobots [2]) represent a natural evolution that can solve existing challenges in manufacturing and assembly tasks, as they allow for a physical interaction with humans in a shared workspace; moreover, they are designed to be easily reprogrammed even by non-experts in order to be repurposed for different roles in a continuously evolving workflow [3]. Collaboration between humans and cobots is seen as a promising way to achieve increases in productivity while decreasing production costs, as it combines the ability of a human to judge, react, and plan with the repeatability and strength of a robot.

Several years have passed since the introduction of collaborative robots in industry, and cobots have now been applied in several different applications; furthermore, collaboration with traditional robots is considered in research, as it takes advantage of the devices' power and performance. Therefore, we believe that it is the proper time to review the state of the art in this area, with a particular focus on industrial case studies and the economic convenience of these systems. A literature review is considered

a suitable approach to identify the modern approaches towards Human–Robot Collaboration (HRC), in order to better understand the capabilities of the collaborative systems and highlight the possible existing gap on the basis of the presented future works.

The paper is organized as follows: After a brief overview of HRC methods, Section 2 provides an overview of the economic advantages of the collaborative systems, with a brief comparison with traditional systems. Our literature review analysis is presented in Section 3, and Section 4 contains a discussion of the collected data. Lastly, Section 5 concludes the work.

#### *Background*

Despite their relatively recent spread, the concept of cobots was invented in 1996 by J. Edward Colgate and Michael Pashkin [2,4]. These devices were passive and operated by humans, and are quite different from modern cobots that are more represented by the likes of lightweight robots such as KUKA LBR iiwa, developed since the 1990s by KUKA Roboter GmbH and the Institute of Robotics and Mechatronics at the German Aerospace Center (DLR) [5], or the first commercial collaborative robot sold in 2008, which was a UR5 model produced by the Danish company Universal Robots [6].

First of all, we believe that it is important to distinguish the different ways of collaboration, since the term collaboration often generates misunderstandings in its definition.

Müller et al. [7] proposed a classification for the different methodologies in which humans and cobots can work together, as summarized in Figure 1, where the final state shows a collaborative environment.


It should be noted that neither this classification nor the terminology used are unique, and others may be found in the literature [8–11].

**Figure 1.** Types of use of a collaborative robot.

To provide definitions and guidelines for the safe and practical use of cobots in industry, several standards have been proposed. Collaborative applications are part of the general scope of machinery safety regulated by the Machinery Directive, which defines the RESS (Essential Health and Safety Requirements). For further documentation, we refer to [12].

The reference standards as reported in the Machinery Directive are:


• UNI EN ISO 10218-1:2012 "Robots and equipment for robots, Safety requirements for industrial robots, Part 1: Robots".

In an international setting, the technical specification ISO/TS 15066:2016 "Robots and robotic devices, Collaborative Robots" is dedicated to the safety requirements of the collaborative methods envisaged by the Technical Standard UNI EN ISO 10218-2:2011.

According to the international standard UNI EN ISO 10218 1 and 2, and more widely explained in ISO/TS 15066:2016, four classes of safety requirements are defined for collaborative robots:


Collaborative modes can be adopted even when using traditional industrial robots; however, several safety devices, e.g., laser sensors and vision systems, or controller alterations are required. Thus, a commercial cobot that does not require further hardware costs and setup can be a more attractive solution for industry.

Lastly, cobots are designed with particular features that distinguish them considerably from traditional robots, defined by Michalos et al. [13] as technological and ergonomic requirements. Furthermore, they should be equipped with additional features with respect to traditional robots, such as force and torque sensors, force limits, vision systems (cameras), laser systems, anti-collision systems, recognition of voice commands, and/or systems to coordinate the actions of human operators with their motion. For a more complete overview, we refer to [8,13]. Table A1 shows the characteristics of some of the most popular cobots, with a brief overview of some kinematic schemes in Table 2.

#### **2. Convenience of Collaborative Robotics**

The choice towards human–robot collaborative systems is mainly dictated by economic motivations, occupational health (ergonomics and human factors), and efficient use of factory space. Another advantage is the simplification in the robot programming for the actions necessary to perform a task [14]. In addition, learning by demonstration is a popular feature [15].

Furthermore, the greater convenience of collaborative systems is their flexibility: Theoretically, since collaborative cells do not require rigid safety systems, they could be allocated in other parts of plants more easily and more quickly; therefore, they could adapt well to those cases in which the production layout needs to change continuously [16]. However, it should be noted that high-risk applications have to be constrained as in any other traditional system, thus restricting the flexibility.

Collaborative systems can also achieve lower direct unit production costs: [17] observed that a higher degree of collaboration, called *c*%, has a high impact on throughput; moreover, depending on the assembly process considered, the throughput can be higher than in traditional systems.

Table 1 provides a comparison between collaborative and traditional systems for four different jobs: assembly (the act of attaching two or more components), placement (the act of positioning each part in the proper position), handling (the manipulation of the picked part), and picking (the act of taking from the feeding point). In order to adapt to market needs, a manual assembly system could be used, though this can lead to a decrease in productivity due to variations in quality and fluctuations in labor rates [18]. Comparing the human operator capabilities to automated systems, it is clear that the performance of manual assembly is greatly influenced by ergonomic factors, which restrict the product weight and the accuracy of the human operator [19]. Therefore, these restrictions limit the capabilities of human operators in the handling and picking tasks of heavy/bulky parts. These components can be manipulated with handling systems such as jib cranes: These devices could be considered as large workspace-serving robots [20], used for automated transportation of heavy parts. However, to the authors' knowledge, there are no commercial end-effectors that allow these systems to carry out complex tasks, such as assembly or precise placing, since they are quite limited in terms of efficiency and precision [21].

Traditional robotic systems [22] bridge the presented gap, presenting manipulators with both high payload (e.g., FANUC M-2000 series with a payload of 2.3 t [23]) and high repeatability. However, the flexibility and dexterity required for complex assembly tasks could be too expensive, or even impossible, to achieve with traditional robotic systems [24]. This gap can be closed by collaborative systems, since they combine the capabilities of a traditional robot with the dexterity and flexibility of the human operator. Collaborative robots are especially advantageous for assembly tasks, particularly if the task is executed with a human operator. They are also suitable for pick and place applications, though the adoption of a traditional robot or a handling system can offer better results in terms of speed, precision, and payload.


**Table 1.** Qualitative evaluation of the most suitable solutions for the main industry tasks.

#### **3. Literature Review**

This literature review analyses works from 2009–2018 that involved collaborative robots for manufacturing or assembly tasks. Reviewed papers needed to include a practical experiment involving a collaborative robot undertaking a manufacturing or assembly task; we ignored those that only considered the task in simulation. This criterion was implemented as, often, only practical experiments with real hardware can highlight both the challenges and advantages of cobots.

For this literature review, three search engines were used to collect papers over our time period that were selected using the following boolean string: ((collaborative AND robot) OR cobot OR cobotics) AND (manufacturing OR assembly). Our time period of 2009–2018 was chosen as the timeline for this literature review, as it is only in the last 10 years that we have seen the availability of collaborative robots in the market.


Of all these relevant results, 16 were duplicated results, leaving us with 113 papers to analyze. Upon a complete read-through of the papers, 41 papers were found to fully fit our criteria and have been included in this review. It should be noted that in the analysis regarding industry use cases, only 35 papers are referenced, as 6 papers were focused on the same case study as others and did not add extra information to our review.

The following parameters were studied: The robot used, control system, application, objectives, key findings, and suggested future work for all these studies, as summarized in Table 3. These were chosen for the following reasons. The robot choice is important, as it highlights which systems are successfully implemented for collaborative applications. The control system is interesting to analyze, as it dictates both safety and performance considerations of the task. Furthermore, when a human is in the control loop, the control system choice is specific to the manner of human–machine interaction by seeing which methods are more popular and successfully implemented, we can identify trends and future directions. We characterized control systems as vision systems (such as cameras and laser sensors), position systems (such as encoders which are typical of traditional industrial robots), impedance control systems (through haptic interfaces), admittance control (taking advantage of the cobot torque sensors or voltage measurement), audio systems (related to voice command and used for voice/speech recognition), and other systems (that were not easily classified, or that were introduced only in one instance).

The application represents the task given to the cobot, which we believe allows a better understanding to be made regarding the capabilities of collaborative robots. These tasks were divided into assembly (when the cobot collaborates with the operator in an assembly process), human assistance (when the cobot acts as an ergonomic support for the operator, e.g., movable fixtures, quality control, based on vision systems), and lastly, machine tending (when the cobot performs loading/unloading operations).

Furthermore, we divided the objectives into three main topics: Productivity, representing the studies focused on task allocation, quality increase, and reduction of cycle time; safety, which includes not only strictly safety-related topics such as collision avoidance, but also an increase in human ergonomics and reduction of mental stress; and HRI (Human–Robot Interaction), which is focused on the development of new HRI methodologies, e.g., voice recognition. It should be noted that in no way is the proposed subdivision univocal; an interesting example could be [25–27]. These works were considered as safety because, even if the proposed solutions keep a high level of productivity, they operate on HRC safety.

The key findings were not grouped, since we believe they depend on the specific study and are too varied; however, they have been summarized in Table 3. Key findings were useful to present the capabilities of the collaborative systems and what HRC studies have achieved. They were included in our analysis in order to identify common solutions. Future work has been grouped into: HRI (works that focus on increasing HRI knowledge and design), safety (works that focusing on increasing the operator safety when working with the cobot), productivity (works focusing on increasing the task productivity in some manner), task complexity (works that focus on increasing the complexity of the task for a particular application), applicability (works that focus on increasing the scope of the work to be used for other industrial applications), and method (works that focus on enhancing the method of HRI via modeling, using alternative robots, or applying general rules and criteria to the design and evaluation process). From these groupings, we can identify ongoing challenges that still need to be solved in the field; by seeing what researchers identify as future work for industrial uptake, we can find trends across the industry in the direction research on which is focused. Our analysis of these parameters is presented in Section 4.

#### **4. Discussion: Trends and Future Perspective**

#### *4.1. Trends in the Literature*

By examining the literature as summarized in Table 3, we can identify several trends in the use of cobots in industrial settings. It should be noted that, for some of the considered studies in Table 3, we could not identify all of our parameters as specified in Section 3; thus, they are not considered in this specific discussion.

Although early researches utilized traditional industrial robots (Figure 2), the subsequent spread of cobots led to several studies based on the DLR LWR-III (2011), followed by the upgraded KUKA iiwa (from 2016 to 2018), ABB YuMi (also called FRIDA) in 2017 and 2018, and Universal Robots from 2014 to 2018. Several researchers applied the collaborative methods to industrial robots, usually due to their increased performance and widespread availability; however, the disadvantage of this choice is the increase in cost and complexity due to the inclusion of several external sensors and the limited HRC methodologies available. A relationship between the kinematics of the cobot and the application was not explicitly considered, since we believe that other parameters, such as the presence of force sensors in each axis, influenced the cobot choice made in these papers. However, it should be noted that the kinematics—precisely, the number of axes—was a feature considered in [28], whereas future works are focused on verifying their findings with kinematically redundant robots [29] or utilizing the redundancy for achieving better stiffness in hand-guiding [30].
