The market for intelligent robots is growing, with industries including aerospace, defense, healthcare, manufacturing, freight, and others seeking ways to leverage robots to improve their services and products. However, artificial intelligence is impacting everything on a macro level.
Key Drivers of Using AI in Robotics Applications
A fundamental driving force behind the use of AI in robotics is improving our own conditions. We are constantly looking for better phones, TVs, cars, or smart devices to improve our quality of life. Sometimes, we consider improving our physical environment through innovations like wind power and solar panels. Other times, we are looking for ways to reduce exposure to hazardous jobs, or we simply want to find ways to avoid tedious and repetitive work.
Industry recognizes humanity's desire to improve its quality of life. They respond through innovation, creating new technologies, and optimizing processes. However, these innovations drive the market to continue innovating and creating the next, newer, better, and faster AI solutions.
Challenges brought by emerging artificial intelligence robots
This cycle of continuous innovation presents several challenges. IDC predicts that global data volume will increase tenfold to 163 zettabytes by 2025, up from 16.1 zettabytes generated in 2016. Today, robots are generating massive amounts of data, primarily from sensory input required for operation. Higher levels of machine awareness have created a wealth of sensor-derived data in industrial environments, but this may be too weak for processing and analysis. Traditional computing strategies and frameworks may be overwhelmed.
Edge solutions
Pushing all this data elsewhere for processing (into the cloud) is no longer practical or meaningful. We've increased productivity because, with artificial intelligence and access to so much data, robots can make decisions faster than humans, and statistically, they always make the best decisions.
With so many machines, sensors, and data combined, computation will increasingly occur at the edge. Robots themselves will be better able to perform more activities and make more autonomous decisions.
Robots driven by data collected and processed at the edge can detect the likelihood of their own malfunctions, or at least the possibility of failing to maintain quality standards. While communicating with other robots on the assembly line, a machine in danger shuts down, and the others adjust their workflows in real time to compensate for the missing worker. The production line slows down but doesn't stop; technicians intervene to make the necessary adjustments or repairs, and then the system resumes full speed. The only way to achieve this and related functionality is through the edge.
Securely bind data and connections
As robots become more mobile, collaborative, and edge-based, and connect to internal and external sensors and IoT devices, this data-rich ecosystem opens multiple access points for potential hackers. Companies may find themselves vulnerable to malware, cyber ransomware attacks, production delays, and business disruptions. Furthermore, cyberattacks targeting highly flexible and powerful robotic systems also pose significant physical security challenges.
Security should not be a matter of hindsight. Some basic measures to take include enabling secure bootstrapping, using container technology to more effectively manage deployed software, leveraging concepts such as time partitioning to minimize the possibility of denial-of-service (DoS) attacks, or using mandatory access control (MAC) to better isolate software components.
Systems integrators also need to understand the machines they are installing on and the entire environment, focusing on identifying potential access points and hardening vulnerable targets. Finally, the operator's IT team needs to be actively involved, monitoring threats and updating security measures.
What to look for in the real-time technology stack
If 75% of all data will be consumed at the edge, then 25% will be pushed out. When data moves from point A to point B, we must choose the correct stack for information transmission because not all stacks are equal. Companies want to adapt protocol stacks to use cases while considering interoperability. TSN and OPC-UA are two examples worth considering.
Time-Sensitive Networking (or TSN) is an evolution of what was formerly known as Audio Video Bridging (AVB). TSN ensures that if you send data from point A to point B, the data will arrive within a certain time frame and with accuracy.
OPC-UA allows users to have a vendor-agnostic communication system with the aim of enabling machines to communicate with each other.
in conclusion
To help AI machines become more autonomous, all the knowledge available throughout the system is essential. Edge cloud is now a machine ecosystem, providing optimal connectivity for AI machines and data. The business impact of the new era of AI and autonomy means that the dynamics of robotic system decision-making are rapidly evolving. What we once considered incremental steps have now become transformative opportunities.
