*3.3. Medical Application*

The da Vinci Surgical System is the best-known robotic manipulator used in surgery applications. Florian Richter et al. [63] presented a Patient Side Manipulator (PSM) arm technology to implement reinforcement learning algorithms for the surgical da Vinci robots. The authors presented the first open-source reinforcement learning environment for surgical robots, called dVRL [63]. This environment allows fast training of da Vinci robots for autonomous assistance, and collaborative or repetitive tasks, during surgery. During the experiments, the dVRL control policy was effectively learned, and it was found that it could be transferred to a realrobot- with minimal efforts. Although the proposed environment resulted in the simple and primitive actions of reaching and picking, it was useful for suction and debris removal in a real surgical setting.

Meanwhile, in their work, Yohannes Kassahun et al. reviewed the role of machine learning techniques in surgery, focusing on surgical robotics [64]. They found that currently, the research community faces many challenges in applying machine learning in surgery and robotic surgery. The main issues are a lack of high-quality medical and surgical data, a lack of reliable metrics that adequately reflect learning characteristics, and a lack of a structured approach to the effective transfer of surgical skills for automated execution [64]. Nevertheless, the application of deep learning in robotics is a very widely studied field. The article by Harry A. Pierson et al. in 2017 provides a recent review emphasising the benefits and challenges vis-à-vis robotics [65]. Similarly to [64], they found that the main limitations preventing deep learning in medical robotics are the huge volume of training data required and a relatively long training time.

Surgery is not the only field in medicine in which robotic manipulators can be used. Another autonomous robotic grasping system, described by John E. Downey et al., introduces shared control of a robotic arm based on the interaction of a brain–machine interface (BMI) and a vision guiding system [66]. A BMI is used to define a user's intent to grasp or transfer an object. Visual guidance is used for low-level control tasks, short-range movements, definition of the optimal grasping position, alignment of the robot end-effector, and grasping. Experiments proved that shared control movements were more accurate, efficient, and less complicated than transfer tasks using BMI alone.

Another case that requires fast robot programming methods and is implemented in medicine is the assessment of functional abilities in functional capacity evaluations (FCEs) [67]. Currently, there is no single rational solution that simulates all or many of the standard work tasks that can be used to improve the assessment and rehabilitation of injured workers. Therefore, the authors proposed that, with the use of the robotic system and machine learning algorithms, it is possible to simulate workplace tasks. Such a system can improve the assessment of functional abilities in FCEs and functional rehabilitation by performing reaching manoeuvres or more complex tasks learned from an experienced therapist. Although this type of research is still in its infancy, robotics with integrated machine learning algorithms can improve the assessment of functional abilities [67].

Although the main task of robotic manipulators is the direct manipulation of objects or tools in medicine, these manipulators can also be used for therapeutic purposes for people with mental or physical disorders. Such applications are often limited by the ability to automatically perceive and respond as needed to maintain an engaging interaction. Ognjen Rudovic et al. presented a personalised deep learning framework that can adapt robot perception [68]. The researchers in the experiment focused on robot perception, for which they developed an individualised deep learning system that could automatically assess a patient's emotional states and level of engagement. This makes it easier to monitor treatment progress and optimise the interaction between the patient and the robot.

Robotic technologies can also be applied in dentistry. To date, there has been a lack of implementation of fundamental ideas. In a comprehensive review of robotics and the application of artificial intelligence, Jasmin Grischke et al. present numerous approaches to apply these technologies [69]. Robotic technologies in dentistry can be used for maxillofacial surgery [70], tooth preparation [71], testing of toothbrushes [72], root canal treatment and plaque removal [73], orthodontics and jaw movement [74], tooth arrangement for full dentures [75], X-ray imaging radiography [76], swab sampling [77], etc.

A summary of research focused on robotics in medical applications is provided in Table 4. It can be seen that robots are still not very popular in this area, and technological and phycological/ethical factors can explain this. From the technical point of view, more active implementation is limited by the lack of fast and reliable robot program preparation methods. Regarding psychological and ethical factors, robots are still unreliable for a large portion of society. Therefore, they are only accepted with significant hesitation.

**Table 4.** Robotic solutions in medical applications.


**Table 4.** *Cont.*


#### *3.4. Path Planning, Path Optimisation*

The process known as robotic navigation aims to achieve accurate positioning and avoiding obstacles in the pathway. It is essential to satisfy constraints such as limited operating space, distance, energy, and time [78]. The path trajectory formation process consists of these four separate modules: perception, when the robot receives the necessary information from the sensors; localisation, when the robot aims to control its position in the environment; path planning; and motion control [79]. The development of autonomous robot path planning and path optimisation algorithms is one of the most challenging current research areas. Nevertheless, any kind of path planning requires information about the initial robot position. In the stationary robot's case, such information is usually easily accessible, contrary to industrial manipulators mounted on mobile platforms. In mobile robots and automatically guided vehicles (AGV), accurate self-localisation in various environments [80,81] is a basis for further trajectory planning and optimisation.

According to the amount of available information, robot path planning can be categorised into two categories, namely, local and global path planning. Through a local path planning strategy, the robot has rather limited knowledge of the navigation environment. The robot has in-depth knowledge of the navigation environment when planning the global path to reach its destination by following a predetermined path. The robotic path planning method has been applied in many fields, such as reconstructive surgery, ocean and space exploration, and vehicle control. In the case of pure industrial robots, path planning refers to finding the best trajectory to transfer a tool or object to the destination in the robot workspace. It is essential to note that typical industrial robots are not feasible for real-time path planning. Usually, trajectories are prepared in advance using online or offline programming methods. One of the possible techniques is the implementation of specialised commercial computer-aided manufacturing (CAM) software such as Mastercam/Robotmaster or Sprutcam. However, the functionality of such software is relatively constrained and does not go beyond the framework of classical tasks, such as welding or milling. The use of CAM software also requires highly qualified professionals. As a result, the application of this software to individual installations is economically disadvantageous. As an alternative to CAM software, methods based on the copying movements of highly skilled specialists using commercially available equipment, such as MIMIC from Nordbo Robotics (Antvorskov, Denmark), may be used. This platform allows using demonstrations to teach robots smooth, complex paths by recording required movements that are smoothed and optimised. To overcome the limitations caused by the lack of real-time path planning features in robot controllers, additional external controllers and real-time communication with the manipulator is required. In the area of path planning and optimisation, experiments have been conducted for automatic object and 3D position detection [82] quasi-static path optimisation [83], image analysis [84], path smoothing [85], BIM [86], and accurate

self-localisation in harsh industrial environments [80,81]. More information about methods and approaches proposed by researchers is listed in Table 5.

**Table 5.** Research focused on path planning and optimisation.



#### **Table 5.** *Cont.*

#### *3.5. Food Industry*

As the world's population grows, the demand for food also continues to grow. Food suppliers are under pressure to work more efficiently, and consumers want more convenient and sustainable food. Robotics and automation are a key part of the solution to this goal. The food production sector has been relatively slowly robotised compared to other industries [97]. Robotics is applied in food manufacture, packaging, delivery, and cookery (cake decoration) [98]. Although the food industry is ranked fourth in terms of the mostautomated sectors, robotic devices capable of processing nutrients of different shapes and materials are in high demand. In addition, these devices help to avoid consequences such as food-borne illness caused directly by the contamination of nutrients by nutrient handlers [99]. For this purpose, a dual-mode soft gripper was developed that can grasp and suck various objects having a weight of up to 1 kg. Soft grippers prevent damage to food [100].

Artificial intelligence-enabled robotic applications are entering the restaurant industry in the food processing and guest service operations. In a review assessing the potential for process innovation in the restaurant sector, an information process for the use of new technologies for process innovation was developed [101]. However, the past year, particularly due to the circumstances of COVID-19, has been a breakthrough year in robotisation in the food industry. A more detailed overview of researches focused on robotising the food industry is provided in Table 6.

**Table 6.** Research focused on the food industry.


#### *3.6. Agricultural Applications*

Agricultural robots are a specialised type of technology capable of assisting farmers with a wide range of operations. Their primary role is to tackle labour intensive, repetitive, and physically demanding tasks. Robots are used in planting, seedling identification, and sorting. Autonomous tractors perform the function of weeding and harvesting. Drones and autonomous ground vehicles are used for crop monitoring and condition assessment. In animal husbandry, robots are used for feeding cattle, milking, collecting and sorting eggs, and autonomous cleaning of pens. Cobots are also used in agriculture. These robots possess mechanical arms and make harvesting much easier for farmers. The agriculture robot market size is expected to reach USD 16,640.4 billion by 2026; however, specific robots, rather than industrial robots, will occupy the majority of the market. A detailed overview of research focused on implementing industrial robots in agricultural applications is provided in Table 7.


**Table 7.** Research focused on agricultural applications.

#### *3.7. Civil Engineering Industry*

In general, the construction industry is relatively inefficient from the perspective of automation. Robotics are seldom applied [107]. The main identified challenges for higher adoption of robotics in the construction industry were grouped into four categories: contractor-side economic factors; client-side economic factors; technical and work-culture factors; and weak business case factors. Technical and work-culture factors include an untrained workforce; unproven effectiveness and immature technology; and the current work culture and aversion to change [108].

The perspective of robotics in civil engineering is significantly better. Here, robotics provides considerable opportunities to increase productivity, efficiency, and flexibility, from automated modular house production to robotic welding, material handling on construction sites, and 3D printing of houses or certain structures. Robots make the industry safer and more economical, increase sustainability, and reduce its environmental impact, while improving quality and reducing waste. The total global value of the construction industry is forecast to grow by 85% to USD 15.5 trillion by 2030 [109]. Robots can make construction safer by handling large and heavy loads, working in hazardous locations, and enabling new, safer construction methods. Transferring repetitive and dangerous tasks that humans are increasingly reluctant to perform to robots means that automation can help address the labour and skills crisis, and make the construction industry more attractive [110,111]. Few classic robots are used in the construction process due to the dynamic and inaccurately described environment; however, work on 3D buildings and their environmental models reduces this limitation. A detailed overview of related references is provided in Table 8.


**Table 8.** Research focused on implementing robots in the construction and civil engineering industry.

#### **4. Discussion**

Implementing an industrial robot in practice is a complex procedure that requires answering many questions about the possibilities of using the robot and the process itself. The situation varies slightly depending on the industry area. Robots have been used in some areas for 30 or more years, whereas, in other areas, the implementation of robots is only beginning. In industrial sectors with a long tradition of robotics, new solutions are relatively more straightforward. These solutions are typically limited to implementing new tools, control algorithms, and robotic action quality control systems. Therefore, our article focuses on areas where traditions of implementing robots do not exist yet, and such solutions are just beginning to be implemented.

Despite the different application areas, some achievements in robotics can be successfully transferred from one industry to another. Furthermore, bypassing limitations in one

area often ensures advances in robotics in other sectors. For example, the implementation of computer vision to localise and manipulate randomly placed mechanical parts on a conveyor fostered the robotisation of sorting processes in all industry fields.

This article provided an overview of the main areas where robots are beginning to be implemented, and identified the main challenges and limitations they face (Figure 4).

**Figure 4.** Relations between robot implementation areas, typical tasks and limitations.

The conclusion is that tasks performed by the robots and actual limitations are closely related to each other regardless of the implementation field. In this paper, the tasks for which robots are most preferred rather than humans were identified. Typically, these tasks are repetitive and extremely precise operations that require evaluating a considerable amount of data. For example, the implementation of robots for object recognition has three main functions in which robots replace humans: (1) extraction of useful information from massive data flow; (2) accurate movements to manipulate with an object or tool; and (3) repetitive action (sorting). In addition, the food, agriculture, and civil engineering industries aim to replace humans involved in repetitive actions. In contrast, medical applications are mainly related to accurate manipulation and hazardous environments.

Preparation of robots for an operation, particularly in dynamic, varying situations, is a time- and resource-consuming activity. Therefore, a large amount of research focuses on enhancing human–robot interaction and path planning/optimisation issues. The goal is to develop faster and more comfortable methods to operate robots in real time, and to create a possibility for the robot to react to the operator's emotional state.

Many different factors limit the implementation of industrial robots in typical tasks. The seven main limitations in the reviewed application fields were identified. In summary, the main limitations are the lack of suitable methods, high recognition accuracy, and performance requirements; varying environmental conditions; an excessive number of possible situations; and lack of reliable equipment (tools). Notably, these limitations are unrelated to the robot's mechanical systems (except the tools). Therefore, most modern robotic solutions are fostered by the development of additional equipment or control algorithms. Computer vision, sensor fusion, and machine learning are becoming major engines driving industrial robots' wider application. They increase robots' flexibility and

enable them to make smart adaptive solutions, although robots were initially designed only to perform repetitive actions.

As a result of the development of robot control systems, robots' internal structures have also been improved. These improvements typically include the implementation of new mathematical methods for robot control or optimisation of energy consumption [118]. For example, a previous study [119] provided a methodology that allows implementation of a non-typical Denavit–Hartenberg method for a delta robot.

Nonetheless, despite the recent improvements and smart solutions realised in industrial robots, their widespread use in non-typical areas remains limited. The main limitations and guidelines for further research are new intuitive control methods, user-friendly interfaces, specialised software, and real-time control methods.

#### **5. Conclusions**

Analysis of robot applications revealed a number of important issues, and showed that the current rare applications of robot implementations are not always limited by technical difficulties.

Some application fields have no tradition in such activities, such as the civil engineering, food, and agriculture industries. Human–robot cooperation in classical industrial robots and in specialised cobot cases still demands an intensive introduction into these industries. However, in this case, the introduction involves non-technical aspects such as human psychology and personal acceptance of the robots in their working place. Another aspect of the subjective attitude to robots is limited by their acceptance by managers and process designers; however, they are also lacking implementation experience and knowledge of cutting-edge achievements in robotic applications.

Many automation cases are still limited by artificial intelligence (AI) issues related to object recognition, object position recognition, and decision generation for object grabbing and manipulating. This issue arises from the process of widening robotic implementation in existing industries, and therefore many technologies should be redesigned. Nevertheless, pressure due to the absence of a skilled labour force has led to new solutions. Many general solutions using machine vision and sensor fusion (camera–lidar scanner, camera–distance sensors, etc.) have been spontaneously implemented in numerous industrial enterprises. These approaches are starting to appear in home appliances, but market penetration of these solutions remains low.

Robot implementations are often subject to systematic difficulties, such as manipulation and orientation of solid objects with non-stable geometrical shapes. These objects are widely used in industry and home appliances, and include textiles, clothes, and cables. At present, this area has few publications and technical solutions, and is in the research stage; presentations of some of the publicly available cases are at the level of scientific publications. Although clamps and templates are currently used for specific industrial cases, general solutions have not yet been achieved. This situation requires rethinking processes and possibly preparing objects for robotic processing, rather than using tremendous computing and multiplying hardware.

The result of this review points to four evident directions in the field of robotics:


**Author Contributions:** Conceptualisation, V.B. and A.D.; methodology, U.S.-B.; formal analysis, U.S.-B. and E.Š.; investigation, J.S.-Ž.; resources, V.B.; writing—original draft preparation, U.S.-B. and J.S.-Ž.; writing—review and editing, V.B. and A.D.; visualisation, A.D.; supervision, V.B.; project administration, J.S.-Ž.; funding acquisition, V.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work is part of the AI4DI project, receiving funding from the Electronic Components and Systems for European Leadership Joint Undertaking in collaboration with the European Union's H2020 Framework Programme (H2020/2014-2020) and National Authorities, under grant agreement No 826060.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the project consortium agreement.

**Conflicts of Interest:** The authors declare no conflict of interest.
