The quest to bring industrial robotics research and AI closer to human social needs began earlier than most people realize. From the jet-stream propelled mechanical bird built by Archytas of Tarentum(350 B.C.E) to the dozens of industrial applications in medicine, manufacturing, and construction—robots are fast becoming a common feature in everyday life.
While previous generations of industrial robots were designed for motion control, modern varieties are autonomous. This self-sufficiency is because of advances in Artificial Intelligence Control and social learning paradigms. The evolution has been rapid and exciting. The leap from primarily moving conveyor belts on factory floors to performing such intricate procedures as cardiac valve repair is quite fascinating. We are now looking at a human/robot integration that will revolutionize the industrial world.
Currently, the Industrial robotic arm can collaborate with humans in complex tasks, so what should you expect in the future, and is what is the risk to reward ratio for this development? Here is a brief look into some rarely known facts about industrial automation and its future ramifications.
The State of Industrial Robotics
The rigid nature of traditional industrial robots limited their primary operations to repetitive tasks. These operations were easy to perform and required little to no human supervision. The jobs suited robots because they worked faster and more efficiently on product lines that would not require modification for their entire lifespan. This feature made robots a suitable alternative to an aging labor force and improved the turnaround time.
The biggest problem with these systems was the capital and time-intensive nature caused by this lack of robot flexibility. In principle, reallocation to a new assembly line, or even a slight software update, required a team of integration experts to achieve a successful transition.
Fast forward to modern robotic systems. With consequent improvements in both integration system software and robot flexibility, operational capacity has increased. The evolution of collaborative robots now allows for simpler operations and more user-friendly technologies.
Since the push for advanced manufacturing in Germany (2011), the quest for digitization and full-scale industrial automation is now achievable. This process, labeled industry 4.0, led to the successful integration of Cyber-Physical Systems. CPS now allows manufacturing plants to monitor, control, and coordinate physical and engineering processes simultaneously.
This push for digital transformation has facilitated an industry revolution that has resulted in higher inter-connectedness within factories. The improved organization has also resulted in better operational efficiency and increased productivity. Central to this advanced manufacturing framework was the application of the IoT and other advanced technologies available today.
Human/robotic collaboration yields better results. There are isolated incidents of robots going rogue and injuring humans(Japanese Kawasaki factory, 1981), but this happened before modern technology took over from traditional robotics. The streamlining effect of collaborative robots has realized the dream of humans and robots safely working together within the same space. Here are some emerging technologies that, if fully integrated, will realize the dream of human/robot collaboration.
Internet of Things
The first step toward complete autonomy is the integration of IoT technologies. Think of a scenario where smart-gadget technology and advanced computing can communicate status information between your central database and the entire production line! You’re looking at a system that is entirely self-sufficient, optimizing all your production processes from one central control hub.
The result is complete autonomy; machines working efficiently without human supervision, operating in different directions, and for long periods without fatigue. Highly mobile collaborative robots that are versatile and can be reconfigured and repurposed for various manufacturing functions
Autonomous Guided Vehicles
AGVs are service robots powered by location and mapping-based algorithms. These algorithms help improve organization and control, allowing them to navigate factory floors and move materials with the utmost efficiency. This technology is still in its infancy, with only nine companies in operation. AGV utility derives from streamlining manufacturing functions with layered data scheduling and round-the-clock factory floor logistics.
For large manufacturing companies, especially in the automotive industry, AGVs create an opportunity for improved capacity and efficient production. The only challenge here is that full cloud-based integration is still a risky debate considering the constant threat of cyber-crime. However, with the correct systems in place, companies can effectively mitigate these concerns.
Production Control (The Island problem)
Many industrial factories operate individually, which makes developing a blanket algorithm for entire manufacturing lines challenging. But, what if companies could connect their network of factories through the cloud and coordinate complete production lines? While many experts say this is unachievable, recent developments in the industrial Internet of Things have made this possible. The principle here is the ability to optimize and control production over a company’s entire infrastructure.
Production control is the core focus of most emerging technologies, and if achieved, would change the face of industrial automation for decades to come.
This new technology shows a typical robot’s capacity and how well it fits a particular company’s value stream. The idea behind robot simulation is to see the economic value added by robot integration compared to maintaining the current labor force. Before purchasing robotic assistance, simulation allows the company to pre-determine the ROI and viability of integrating robots and the cost/benefit co-efficient.
Simulation is a complex process; however, involving augmented systems like virtual reality may be subject to variations in real life. The challenge is creating simulation patterns with better predictive capabilities that effectively anticipate variables in complex situations. On paper, simulation technology is a tremendous step in the right direction, but a list of environmental and operational factors makes it difficult to mimic real-life situations.
The course of industrial automation gained shape over 60 years ago. When American engineer George Charles Devol programmed a machine to function like the human arm, the potential of industrial robotics became infinite. Robots now operate within and beyond the six degrees of freedom, something considered impossible 70 years ago. Every development since then has been to improve this functionality and effectively redefine human/robot interactions for years to come.