The anatomy of a new robot
The schematic below shows the hardware anatomy of a new robot. Here, we have assumed a mobile robot. Its intelligence unit consists of computing elements (e.g., transistors and memory), as well as sensing ones (e.g., GPS, camera, IMS, ultrasound, IR, LIDAR, wheel encoder, etc). Its mobility functions include electric motors and energy storage. At a higher level, there is a supporting infrastructure such as the internet, cloud computing and storage and GPS network.
In the remainder of this article we will consider how all of these hardware components have evolved over time. This drastic improvement in price-performance has been the wealth creation engine of our time, boosting global productivity. It is a trend that we all intuitively appreciate, as we have all lived through this rapid transformation over the past decades.
Exponential trends have brought us here
Consider the chart below. First focus on the 'computing' line. It shows the cost of computing as a function of time from around 1940 to about now, divided by the cost at the initial point in the series. The unit of measurement is the cost of a device capable of calculating 1600 million instruction per second. We can see that there has been a phenomenal 12 orders of magnitude fall in the cost of computing. This is just incredible.
This has of course been accompanied with increase in performance. Indeed, Moore's Law is well known. Interesting, at 1971, Intel 4004 chip packed only 2250 transistors in a 12mm2 chip. Fast forward to 2017. The Apple A11 packs around 4.3 billion transistors in a 89mm2 chip and the Centriq2400 packs around 18 billion in a 398mm2 chip. This too is phenomenal and has of course entailed an incremental year-on-year shrinkage of device size. This exponential growth has helped make new robotics a commercial possibility.
Now focus on all the memory related lines in the chart. The unit of cost here is $/Mbit. Here too there has been a phenomenal cost reduction in hard drives, RAMs and solid-state memories. In fact, the rate of change in this industry has been so rapid that it has turned it into an academic case study for all those wishing to study the phenomena of disruption.
Now let us consider cameras (CMOS image sensors). Here too the cost has exponentially fallen. Other technologies showing rapid cost falls include MEMS and other sensors. We speculate the LIDARs will also start to rapidly fall in price as the transition towards improved mechanical control and solid-state versions take place. It is therefore evident that the cost of sensing (data acquisition) and computing (data processing) has dramatically fallen over the year. The size and power consumption of these devices have also dramatically fallen. All these exponential trends have operated for multiple decades, bringing us to a point today that suddenly complex new robots (collaborative, mobile and intelligent) are becoming commercially viable.