Whether it's a fast-approaching synthetic invasion, or a gadget that cleans your home while you relax on the sofa, robots are something we all have considered at some point or another. And then comes the cloud, an indistinct something that by far most of us have heard about, however, not as many truly are aware of it. Putting it in simple words, think about the cloud as a metaphor for the web that functions by storing data on servers that are managed by cloud computing providers such as Amazon with Amazon Web Services (AWS).
With that covered, let us see what happens when you combine robotics and cloud technology: The emergence of cloud robotics. Just for general facts, "cloud robotics" was first used in 2010 by an American roboticist James Kuffner, also the CEO of the Toyota Research Institute - Advanced Development, back when he worked at Google. From that point forward, we've seen and came across several innovations in cloud robotics development and the overall market for cloud robotics. Before we proceed, let us first understand what precisely is cloud robotics?
What Is Cloud Robotics?
Cloud robotics is a field of robotics that fundamentally determines the use of distributed computing to maintain robotic functionality. It looks up to leverage cloud technologies like distributed computing and cloud storage along with robotics, bringing about a robot that is connected to the cloud by means of the internet. When this takes place, the robot is blessed with everything the cloud brings to the table, for example, storage, powerful computation, and communication resources, resulting in a moderately lightweight, intelligent, and inexpensive robot that is linked to the cloud and all the data it has to offer. In simpler terms, consider an individual whose mind is always linked to the web and has the ability to always pull data from it.
As said previously, a robot whose brain is always connected to the cloud comes with several advantages. Let us see a few of them as mentioned below:
What will this mean for the Capabilities of Cloud Robotics Technology?
The experience that the vast majority have with robotic technology today includes sitting at a work area or working on a computer and using that interface to guide the machine. That is essentially not the same as how we act in our everyday lives. We live on our cell phones, on wearable gadgets, and we're making use of them to converse with one another, message, and many other things. We expect to communicate with them in a predominantly consistent manner, and we expect that from all advancements too. That sort of communication, instead of "program and leave," is the next big objective for engineers working on cloud robotics.
KUKA is a German manufacturer of industrial robots that builds smart machines for some of the largest manufacturers in the world. For instance, KUKA robots power the assembly lines that is a part of every Tesla automobile in the world. To return to the likes of the cell phone, KUKA has developed what is basically an application store for robots, where users can download a wide range of various software updates for their machines.
"KUKA has maintained this database consisting of operational data about their robots, so they can do things like predicting when the robots would require their next maintenance and other operational factors," says Elliot Yama, VP and business leader, ML and AI, with Apttus, a tech giant in the Bay Area whose products control the KUKA application store. "Or when you would require to get done the software upgrades for that robot, for better optimization. You are well aware of an app store for upgrading software and downloading new ones in the smartphones; well, KUKA presents an app store for any industrial robot."
At the most fundamental level, any task that an individual needs to do again and again can be computerized. It's a simple equation of determining what the task is and asking the machine what it needs to do. However, with AI and ML, not only would robots be able to determine the undertakings that most need to be automated, but also can make sense of how to solve them in the best possible manner, and teach different machines what they think about finishing that task. This makes scaling up tasks basically consistent.
"Contemplating it from a mechanical perspective, you can consider things like IoT devices being automatically turned on and off, about printers automatically getting their tasks done, and whatever other situations where, literally, robots are deployed to fix things out," says Allan Leinwand, CTO of ServiceNow. "So you can see how this predictive analysis and problem-solving approach could be really valuable for the enterprise."
Two of the very direct impacts seen on machine development by distributed software, including the Ubuntu Core-powered Robot Operating System (ROS), are that the prices are dropping down and hardware is getting smaller. "There always comes the question of cost-consciousness in terms of the building materials required for machines," says Mike Bell, official VP of devices and IoT at Canonical/Ubuntu, "and this has accelerated by the combination of edge computing and cloud computing.
There are applications that run on the cloud, and connecting it to something already running on the device, can actually be the most ideal approach to do something. In this way, leveraging the cloud in an intelligent way brings about lower costs and faster hardware." It has already shown its prominence on the desktop, smartphones, and portable devices, and is now unfurling in robotics.
Developers are beginning to use the ML frameworks and put them into robotic contexts, utilizing the best of distributing software to handle movement, vision, and object recognition, among others. In any case, this is only the learning and training phase. Finally, having the majority of this data on the "robot cloud" will imply that new machines will probably take advantage of all the learning that has been gathered by a superior group of devices. "Just imagine a swarm of robots and taking every one of the capabilities that each of them has and pushing all that data back to the cloud," says Canonical's Bell. By then, robots will most likely teach other robots what they know and improve each machine that is linked to the cloud. "Along these lines, rather than considering the experience and learning ability of a gadget, we'll be taking a look at a cloud that can improve a gathering of robots, upgrading the learning algorithm over time.”
There is an issue with Big Data: As machines converse with one another at an ever-accelerating rate, sending an ever-increasing amount of data into the cloud, it's getting difficult for people to understand that information. Our minds simply don't work fast enough. Obviously, the amount of data coming at us isn't backing off, but we need an approach to wisely sort and arrange it all. Robotic technology is the perfect answer to this issue, using AI and analytics to organize the heaps of information that exist on the cloud and sorting them on the basis of the applications that they're dealing with.
After a specific time, as a company's robots are matured enough with training and capable of training others, they will show signs of improvement and get better as a group at recognizing the most significant data available on the cloud servers of the company. Therefore, the Big Data that is currently becoming so overpowering will become more convenient and handy.
What Cloud Robotics Development Platforms are Currently Available?
Cloud robotics might be a moderately new technological field, yet we already have access to a few robotic operating systems that meet the criteria for a cloud robotics development platform:
“Our AI and ML service will understand the unpredictable physical world, initiating effective robot automation in highly dynamic environments. The outcome: less storehouses, greater adaptability, and the freedom to innovate. With this platform, developers will be able to access the majority of Google's data and AI skills that range from Cloud Bigtable to Cloud AutoML. Also, with access to Google Cartographer, which gives real-time synchronous localization and mapping in 2D and 3D, robots will most likely process sensor information and localize in a common map.” And as Google stated, "although your environment changes after some time, our spatial intelligence services will analyze your workspaces and can be used to track, query, and respond to situations in the environment."
What Are Examples of Cloud Robotics Developments?
So far, you've probably thought of cloud robotics in accordance to the humanoid robots we've been coming across and reading about in books, movies, and TV shows. All things considered, that may occur later on (or is currently ongoing in select research facilities around the globe), however, for now, the most widely recognized examples of cloud robotics you'll likely encounter include self-driving vehicles and assistive robots.
For instance, consider autonomous cars like those from Tesla or Uber. The truth is that these vehicles use the cloud to accumulate data they have to appropriately and securely move around other cars and objects. Now think about the future, when these kinds of vehicles become conventional. After a few years, they will theoretically have the option to "communicate" with one another and synchronize as per traffic patterns and conditions, thereby minimizing road mishaps and improving road safety.
Well, there are assistive robots like the Roomba that come with certain cloud-enabled features that improve the way in which they function. Despite the fact that they're not really cleaning things with a floor brush, lifting things to sweep the dirt beneath them, or using presence of “mind” not to clean over a liquid spill— few things that a future assistive robot would do—they use cloud robotics to function, and have been using them increasingly more with each model that is released.
What are the Challenges Faced by Cloud Robotics?
The increased use of technologies based on cloud robotics introduces concerns related top privacy and security. Cloud-based services include robotic data to be stored and processes to be performed remotely in the cloud, making these applications susceptible to programmers and hackers. Storing data remotely in the cloud can prompt inappropriate access, control and deletion of significant information by hackers. Remote execution makes it simpler for hackers to access and modify the robotic services in the cloud, in this manner changing the behavior of robot tasks in malicious ways. Hence, researchers have used a term called cryptorobotics as an amalgamation of digital security and robotics. To determine the security and privacy threats related to cloud robotics, appropriate verification techniques with a layered encryption tool are presently used to access cloud services and data by the entity.
Communication between robots and cloud services (in terms of cloud robotic systems) can bring about considerable delays. The heaps of information that normally arise in several robotic applications like SLAM, perception, and navigation worsen communication delays. Truth be told, the major problem in cloud robotics is the delay in terms of computation and communication that continues among robots and the cloud for majority of the applications.
Cloud robotics is no longer the future of robotics; it's the present. Despite the fact that we face a few challenges that hinder their present and future capabilities, for example, privacy and security issues and concerns regarding effective load-balancing, we're soon arriving at a point where theory becomes fact and we have fully-functioning robots strolling around with the cloud embedded securely inside.