Robotics technology has applications across industries, namely manufacturing, healthcare, retail, agriculture, defence, and others. Some key factors, including cloud computing, artificial intelligence (AI), automation, and refill workforce shortages have contributed towards unlocking the full potential of robotics today.
Listed below are the key technology trends impacting the robotics theme, as identified by GlobalData.
AI technologies, most notably machine learning (ML), are integral to the development of intelligent industrial robots, which can anticipate and adapt to certain situations based on the interpretation of data derived from an array of sensors. Further advances are needed in certain AI technologies, including computer vision, conversational platforms, and context-aware computing, to take industrial automation and industrial robotics to the next level.
Neuromorphic processors (chips that emulate the structure of the human brain) will become an important part of the next generation of robots. They are trained using basic libraries of relevant data and then taught to think for themselves by processing sensory inputs. Eventually, these chips will use their acquired skills to perform assigned duties using associations and probabilities.
Although much in robotics can be done from the cloud, security and latency issues mean that many robots have to be able to process real-time data about their operational environments and respond immediately. Due to lower latency, edge computing has the potential to improve the performance of robots while, at the same time, improving security, as the edge is safer than the cloud. Edge computing will make cyberattacks more difficult when combined with robotics’ self-contained “sense-decide-act” firmware loops.
One of the major challenges to the widespread implementation of robots is the threat of cyberattacks. Robots, especially those that are internet-connected, are highly vulnerable to hacking. Leaving them unprotected may allow unauthorised access to key applications and systems, which in turn may lead to loss, theft, destruction, or inappropriate use of sensitive information.
Hackers can even gain control of robots and compromise robotic functions to produce defective final products and cause production downtime. As a result, robot manufacturers are compelled to focus on security at the design and development stages and invest in effective security solutions.
The latest industrial cybersecurity management solutions address the risks associated with industrial automation equipment, applications, and plants. These solutions enable enterprises to comply with industry-specific cybersecurity regulations, such as the North American Electric Reliability Corporation Critical Infrastructure Protection (NERC CIP).
Industrial machines and processes have been monitored in real-time for decades, with technologies like supervisory control and data acquisition (SCADA) having been in existence since the 1970s. However, the Industrial Internet implies a greater degree of interconnection between systems and assumes that the monitoring and control data will flow beyond the boundaries of the factory to be consumed and managed by cloud-based services.
While there is much excitement about the factory of the future and Industry 4.0, existing factories, machines, and processes represent the primary opportunity for the Industrial Internet. The biggest short-term gains will come from retrofitting advanced communications and management functionality to today’s industrial infrastructure.
Advances in AI have enabled the development of robots, allowing them to become highly complex products rather than the stand-alone, fixed-function machines they used to be. This, in turn, has increased the number of roles that robots can perform. Central to this development has been cloud computing, which allows sensing, computation, and memory to be managed more rapidly, safely, and at scale.
The leaders in cloud robotics combine infrastructure and AI capabilities, namely Amazon, Google, IBM, and Microsoft. As well as enabling AI implementation, the use of cloud within robotics has the potential to change the way that the technology is consumed.
Robotics centres of excellence (CoEs)
A robotics CoE is responsible for developing and implementing robotic solutions that are efficient, productive, and responsive to the needs of industries. These solutions ensure that a company realises its automation goals. In simple terms, the CoE gathers, assesses, and manages the information that eases the deployment of robotic solutions.
Open process automation (OPA)
Traditionally, robotic components such as controllers were only compatible with products made by the same company. For example, Siemens controllers would only work with Siemens products and ABB controllers with ABB products. Various organisations are now striving to break free from these limitations and establish an open system that would make robotic components universally compatible. These efforts have led to the development of OPA, which allows technology vendors to work together with various organisations to produce standard, secure, and open architecture that can ease robotic integration, giving rise to vendor-neutral solutions.
ExxonMobil launched the initiative that aimed to build a prototype capable of transforming into a commercially viable OPA system. It can be achieved by developing a distributed, modular, and standards-based architecture for robotic components with extensible systems that can accommodate changes.
Lightweight design and doing less with more
The robots of the 2020s will be smaller and lighter. This will make them more flexible, cost-effective, and easy to deploy. The trend towards lightweight design applies to both the bodies and the brains of robots. Several companies are investing in optimised operation systems, software, and programming. Academia is also developing solutions for some of the most complex problems, such as trajectory simplification, which aims to make robots better at navigating their environment.
Although robots have become prevalent across different industries, designing and modelling a robot is tedious, cumbersome, and expensive. Moreover, accommodating a minor change or modification at a later stage can further prolong the process. In response, manufacturers are attempting to create robot prototypes that can be customised. For example, start-up Elephant Robotics developed a low-cost and intelligent robotic arm with six degrees of freedom that can be adapted to multiple scenarios and applications.
Soft and self-healing robots
Soft robots are made of soft materials or polymers instead of conventional metal. These materials give robots organic characteristics, replicating the way muscles work. Research is ongoing on enabling them to self-repair, which would make them more flexible and adaptable. Self-healing robots are still in their infancy stage, but continued research is expected to improve the technology.
This is an edited extract from the Robotics – Thematic Research report produced by GlobalData Thematic Research.