Our goal is to put every robot in orbit around the cloud to help accelerate the adoption of robotics across industries. Today we are one important step closer to that goal.

At RoboBusiness, the premier commercial robotics trade show for business executives, we announced that the Qualcomm® Robotics RB3 development kit will have pre-integrated support for InOrbit’s cloud platform. This advanced development kit is based on the powerful Qualcomm SDA845 SoC. This allows robotics developers to create autonomous robots for the most challenging applications.

In Part 1 of this article, we discussed in some detail some of the limitations of AI and autonomous systems. The key takeaway is that autonomy is relative, and there continues to be a need for human interaction and direction.

At InOrbit, we are harnessing the power of the cloud and the edge to bring automation and efficiency to the operation of distributed robot fleets. It consists of four O’s, which of course we think of as concentric orbits.

Imagine that you need to fly to a distant location, a busy airport you’ve never visited. You are the nervous kind, so you want to make sure it will be safe. You talk to the airplane manufacturer, and walk away with confidence that the machine has been well designed. You verify the maintenance schedule and are satisfied that it is thoroughly tested.

Then you talk to the management at the airport you’re flying into, and they tell you that they cobbled together their air traffic control system with some old PCs that nobody was using and installed some software for a taxi dispatch. Would you get on that plane?

Sadly, this is often the case in robotics. Since before we started InOrbit, and throughout the last 2 years, we have engaged with over 75 robotics companies and hundreds of people in the robotics space. We have talked to the C-suite setting strategy and to front line operators doing triage, to robotics Ph.D.’s and to self-identified robot baby-sitters. Across all these conversations, we had an overarching question: how do you manage robots after they leave the lab?

Robots are everywhere. They can be found in hospitals and hotels. In farms and construction sites. Brick and mortar retailers and e-commerce distribution centers. In the air, in the sea, on the ground and even underground, as we saw recently in the DARPA challenge.

But what is a robot? There have been plenty of philosophical discussions on this, and probably no shortage of flame wars. We like this definition from IEEE:

A robot is an autonomous machine capable of sensing its environment,
carrying out computations to make decisions, and performing actions
in the real world.

So right there in the definition is the A-word: autonomy. Since nomos is Greek for “law”, something autonomous makes its own laws. Pretty cool, right?

However, autonomy is relative. We’re not just talking about being constrained by the laws of physics, but by the limits of AI, sensing technology, computing power, servos, etc. In essence, the guts and brains of robots can only go so far with current technology.

50 years ago this month, the first human set foot on the moon (unless you choose to believe it never happened.) At InOrbit, we work on scaling autonomous solutions, not spaceships, but this milestone got us thinking about what the next 50 years may have in store for us, and the impact of automation.

As I’ve shared here before, we do 3-4 big product pushes per year and give them a codename based on some of the greatest inventors and scientists. It seems appropriate that for our next one, which will be starting soon, we would pick Margaret Hamilton.

While the picture of Neil Armstrong’s first footprint on the moon has been repeated over and over, it’s not as often that we hear about all the people who made that possible. Hamilton led the software development efforts for Apollo 11; she was the first person to use the term “software engineering”. She was awarded the Medal of Freedom in 2016. If you’d like to learn more about Margaret Hamilton, there’s a great profile on Makers.com.

Over the last year, we have been talking with robotics companies and operators in Silicon Valley and around the world. We have learned a few things about the challenges of operating robots at scale. This has mostly confirmed that, compared to the great advances in software and hardware components that are helping robots enter every industry, robot operations and infrastructure are still immature.

Another day, another Demo Day. Last week we had the privilege of being among a select group of startups to show the latest and greatest in the IoT world, ranging from photonics to wireless power transmission, at the latest Plug and Play Demo Day for Internet of Things. This was a big event, with many corporate partners and investors in attendance.

In the last couple of years, it seems that robots have jumped from science fiction to reality and are on their way to becoming commonplace. This process continues to accelerate, driven by macro-economic trends such as labor shortage and advancements in technology.

But as I like to say, it usually takes years of preparation to become an overnight sensation. In the case of robotics, it has taken decades of contributions from people around the world, from scientists to entrepreneurs and investors.

Few things are more devastating for a startup than to be working on the wrong problem. That’s why we spent time early on, before we had even formed our company or written the first line of code, to make sure we were working on a real problem that has a significant impact for a large number of people. We met with dozens of people across the robotics landscape to learn, asked lots of open-ended questions and listened carefully, honing our pattern-recognition.

It seems that every week there’s news about a new amazing application of robotics, from e-commerce, to growing food or building houses. While for many people this may seem to be something for the distant future, it is happening now and will continue to scale rapidly over the next couple of years.

This rapid acceleration results from a convergence of factors, including advances in sensors, deep learning and computer vision algorithms, chipsets that can handle AI tasks and software standards such as the Robot Operating System (ROS).

We’ve seen this before