Industrial robots and their related control methods are constantly developing. In recent years, with the development of artificial intelligence, the control strategy of industrial robots has emerged a new direction of development, and the development prospect of cognitive robots has become increasingly clear. AI-based robotic systems are strongly becoming one of the main areas of concern, as flexibility and a deep understanding of complex manufacturing processes are emerging as key advantages to improve competitiveness. (Arents & Greitans, 2022)
The essence of industrial robots is to automate repetitive, dangerous, onerous, resource-intensive tasks and so on, and eventually realize the automation of tasks requiring high artificial intelligence. An industrial robot is defined as follows: an automatically controlled, reprogrammable, multipurpose manipulator that can be programmed on three or more axes and can be fixed or moved for industrial automation applications. The Third Industrial Revolution has demonstrated to industry and society how the use of industrial robots can help manufacturing companies become more effective and competitive in their fields. Since its introduction, industrial robots have been applied to automate the manufacturing process in many production workshops, thus changing the manufacturing industry. In today’s modern industry in the digital age, where industrial concepts are being applied, similarities can be seen, and industrial robots with more advanced capabilities are one of the core elements of the transformation process and enablers of intelligent manufacturing. (Arents & Greitans, 2022)
Major technological developments, new applications, the trend of rising global labour costs, and an aging population: are just some of how industrial robot shipments are growing each year. In fact, from 2015 to 2020, the annual installation of industrial robots grew by an average of 9% a year. (Arents & Greitans, 2022) However, most industrial robots currently installed in production workshops are traditionally programmed to meet the specific needs of their respective factories and are mostly customized to perform “boring, dirty, or dangerous” fixed jobs. Typical industrial robot systems are neither intelligent nor adaptable to dynamic environments, nor can they learn tasks autonomously. Changes in the market have created uncertainty for manufacturing and end-user mobility services.
Consumer preferences and social trends in changing product requirements will accelerate the need for advanced robotics solutions. Consumers’ desire for faster delivery customization has led to the expansion of robot capacity in personalized manufacturing and logistics applications. At the same time, an aging population will lead to greater demand for mobile service robots to help with personal hygiene, exercise, food delivery and other jobs. With the increasing emphasis on recycling and other sustainability measures, robots will need to take on complex disassembly and sorting tasks. (Lassig, Lorenz, Sissimatos, Wicker & Buchner, 2021)
Industrial robots play an important role in the process of automation. Under the background of industry 4.0 and industry 5.0, intelligent industrial robots play a more significant role.
As 2021 approaches, manufacturing intends to transition to Industry 5.0, a concept that aims to combine robotics and intelligent machine workflows with human capital, a move that is expected to yield significant gains. Automated mobile robots (AMRs), forklifts, and other automated vehicles are being widely deployed to improve the transportation of raw materials and other components from warehouses to factories. The adoption of AMRs at Ford’s Valencia plant in Spain is a testament to this development. Since March 2018, Ford has been deploying Denmark’s Mobile Industrial Robot (MiR), which is equipped with a 17-slot automated holding system to accommodate materials of different sizes and weights (Kaitwade, 2021) robots will increasingly replace traditional low-wage and low-skill intensive jobs. A shortage of manual workers, coupled with rising wages in countries where wages were previously low, will prompt robots to replace humans more quickly. Wages for factory workers in China have doubled since 2007, while wages for workers in India have risen by more than 50% over the same period. (Lassig, Lorenz, Sissimatos, Wicker & Buchner, 2021)
While generating revenue, emphasizing lean manufacturing, and improving production efficiency are also the results that robots can bring. In any typical manufacturing process, there are seven areas of waste: unwanted movement of various products; Overstock, excessive turnover, frequent interruptions in shifts, overproduction, overprocessing, and defects. So industrial robots step in and eliminate these problems. Robots don’t shut down during shifts or disrupt supply chains. Lean manufacturing has produced many positive results in the past. A case in point is Falcon Fastening Solutions Inc., a company specializing in cost reduction services for OEMs, which uses production components so heavily that manufacturers using its services achieved cost savings of more than 19 percent in 2018. (Kaitwade, 2021)
The Future of Industrial Robots?
Demand for robots continues to climb in key manufacturing industries. According to the Robotics Industry Association, the total North American market was valued at over $1 billion in 2020. Notably, the entire life sciences (69 percent), food & Consumer products (56 percent), Plastics & Rubber (51 percent), and automotive (39 percent) industries. It is only natural that the adoption of robots will accelerate as a result of breakthroughs in connectivity technologies across multiple industrial sectors. As the impact of the COVID-19 pandemic continues to reverberate, key industries are seeking to ensure that their manufacturing practices are not disrupted, expanding the scope of robot deployment. Huge app stores in the pharma and automotive sectors respectively. Robotics has been used in vial-filling applications, as well as visual sensing to verify serial number compliance at the time of packaging. In the latter case, electric vehicle manufacturing holds great promise, as cars may deploy technologies such as driverless robots and collaborative robots to assist in the manufacturing process. While this may not be the answer to the existing challenges, it certainly helps streamline the overall production process.
It is estimated that the global robotics market will climb to between $160 billion and $260 billion by 2030, from about $25 billion this year, with a market share of $170 billion for professional service robots and about $80 billion in sales for industrial and logistics robots. (Lassig, Lorenz, Sissimatos, Wicker & Buchner, 2021)
Artificial intelligence and other technological advances will enhance human-robot interactions. Robot capabilities will include the ability to learn. Today, simulation tools are used to teach robots how to solve real-world problems. But this brute-force approach is not satisfactory because the complexity of the environment often prevents robots from being trained to respond flexibly and intelligently to unexpected events. However, new research from OpenAI, a non-profit AI think tank, which focuses on guiding neural networks to progressively navigate more difficult and random environments, appears to yield powerful results. The first application is a human-like robotic hand that can manipulate and solve a Rubik’s cube without human input. This training requires enormous computational power, but over 50 hours, the system using cloud and distributed computing gathers about 100 years of experience. This study illustrates the fundamental difference between the automation technology widely used in factory environments today and the fundamental shift ahead. Driven by advances in computer science, the required automation approach will shift from rule-based to goal-based. The robotic capabilities that this shift brings are especially valuable in single-batch custom manufacturing processes. (Lassig, Lorenz, Sissimatos, Wicker & Buchner, 2021)
The current development trend of intelligent industrial robot control points out the potential and limitations of different learning strategies. Compared with traditional robot control methods, learning-based methods enable the robot to sense the environment, learn from it and adapt to dynamically changing situations. The ability to make decisions and learn is an important step toward achieving more human-like performance. However, the possibility of intelligent industrial robot control in the manufacturing industry has not been fully explored and utilized. Some challenges in the control of intelligent industrial robots, especially in learning strategies, require further research. (Arents& Greitans, 2022)
Arents, J., & Greitans, M. (2022). Smart Industrial Robot Control Trends, challenges and opportunities within manufacturing. Applied Sciences, 12(2), 937. https://doi.org/10.3390/app12020937
Kaitwade, N. (2021, May 19). How industrial robots will transform manufacturing through 2021 & beyond · emsnow. EMSNow. Retrieved August 10, 2022, from https://www.emsnow.com/how-will-industrial-robots-transform-manufacturing-through-2021-beyond/
Lässig, R., Lorenz, M., Sissimatos, E., Wicker, I., & Buchner, T. (2022, August 8). Robotics outlook 2030: How intelligence and mobility will shape the future. BCG Global. Retrieved August 10, 2022, from https://www.bcg.com/publications/2021/how-intelligence-and-mobility-will-shape-the-future-of-the-robotics-industry