Nvidia has released a new fascinating case study about the use of the Percent-Scale Proportional-Integral (PNF) technique in their robotics development.
Nvidia is a world-leading manufacturer of graphics cards, processors, and other computer components. With more recent groundbreaking research and development in robotics, they have decided to delve deeper into the realm of AI and robotics to capture the consumer and productivity markets.
One of the major advances in robotics and AI is the Percent-Scale Proportional-Integral (PNF) technique. This technique is based on the concept of adjusting the system “proportional,” or according to a set percentage of an input signal. It uses two main parameters: a proportional gain (Kp) and an integral gain (Ki). By using these, the system can make adjustments with more accuracy.
Nvidia, when developing its robotics, found that traditional PID constants did not allow for enough flexibility in terms of programming and regulating the robotic’s speed and motion control. That’s why they shifted to PNF technique, which allowed them to add greater precision and control to the system.
According to their case study, using PNF allowed Nvidia to successsfully program its robots to quickly move from slow to medium speeds and vice versa in a smooth, controlled manner. Additionally, since PNF allows its parameters to be adjusted in real-time, it is now possible to apply acceleration and deceleration rates or steady speeds with greater accuracy.
What’s more, PNF also has a longer lifespan than regular PID techniques, making them ideal for Nvidia’s robotics which require a longer lifetime for their applications. Ultimately, it allowed their robots to move more accurately in a greater range of motion.
As a result of PNF’s numerous advantages in robotics, Nvidia has now integrated it into its robotics systems. This case study serves as a great example of how using the right technique can improve the performance of robotics and AI drastically.
