Cloud Robotics

 

The nineteenth century accelerated the adoption of wide industrial processes, thus, the Industrial revolution. In the twentieth century, the rate of these processes was stimulated due to the Technology Revolution, thus, allowing access to labs and research institutes from home. With the new realms of electronics, automation, and computation, we have landed at the apex of a new technological shift, the Robotics Revolution. This phase is bound to bring a huge impact on human lives.  

The Robotics Revolution introduced robots that could perform any level of tasks. Although, the space and memory required, along with the cost was a dilemma yet to be tackled. That’s when cloud robots made a debut!  

What is Cloud Robotics?  

The term “Cloud Robotics” was coined by James Kuffner of Google in 2010. Cloud robotics is an intersection between robotics, cloud computing, deep learning, big data, and internet of things, and other emerging technologies. It is a field of robotics where robots rely on the internet network to implement their functions. More like, a robot whose sensing and computation are not integrated into a single system, thus robot having “an extended or a shared brain”. As a result, robots are getting not only smarter by connecting to the cloud, but also cheaper and smaller!  

Why Cloud Robotics?  

People have presumed this “pre-defined concept” of robots being able to do everything automatically. This vague concept was encouraged by several movies, where a robot can perform any task, but it’s not true. Until and unless the task is pre-programmed in the robot, it cannot perform the same. But this limit can be exceeded by cloud robots!  

Before cloud computing came into the picture, an entire team of experts was required to optimize the tasks. The resources, the servers, everything was pretty limited. But, with cloud robotics, a common pool of networks and servers is shared.  

These services can be accessed anytime without any hand-holding from the experts. Cloud robotics also introduced rapid elasticity because of which the resources can be scaled up and down based on demand. Also, even though compared to industrial robots, pre-programmed robots have high real-time performance efficiency and accuracy but, when it comes to facing an unknown extreme environment, pre-programmed robots cannot make the ends meet. Guess what can? That’s right, Cloud Robots!  

Let’s dig deeper!  

Instead of executing tasks using on-device computation, cloud robots execute computationally intensive tasks and sends them to the cloud. This is a very notable characteristic of Cloud Robotics.  

The architecture of cloud robotics comprises of two main parts:  

The cloud platform along with its equipment.

The bottom facility.

The cloud platform is composed of servers with high performance and wide databases. The bottom facility comprises all the machinery, mobile robots, and equipment. Cloud robotics is implemented in several multi-robotics operations that have proven to be standard projects of cloud robotics. In the field of multi-robot operations, the integration of cloud computing and robotics in fields like image or video analysis, data mining, and many more have stimulated the advancement of cloud robotics. Therefore, the key features of the architecture are:

The computing tasks of the cloud infrastructure are dynamic, and the resources are available as per demand.

The “brain” of cloud robotics is in the cloud. Even though the tasks are processed individually, the results of these processes can be obtained through networking technologies.

The computing task can be entrusted to the cloud, which can, in turn, result in better battery life and less robot load.

Did you know?  

Are you familiar with the movie- “Matrix”?  When Neo asks Trinity about the helicopter on the rooftop- “Can you fly that thing?” She replies: “Not yet”. But then a “pilot program” is uploaded to her brain and they both fly away.  

SLAM: There has been a cloud computing infrastructure built by the researchers at ASORO laboratory in Singapore. This infrastructure intends to generate 3D models of the environment, modifying the location of the environment as well as the agent. This process can be performed much faster on the cloud than using onboard computers.

GostaiNet: A French robotics firm known as Gostai has built this cloud robotics infrastructure- GostaiNet. This infrastructure allows a robot to perform remote tasks like speech recognition and face detection. The cloud is used for video recording and voice synthesis.

iCub: Giulio Sandini, a robotics professor at the Italian Institute of Technology said, “This project is a “precursor” of the idea of cloud robotics”. The iCub is a humanoid platform that works as a “container of behaviors”. Using cloud technology, a lot of behaviors could be developed like the behavior of making pizzas or the behavior of making crepes. All we would be doing is adding a “behavior app” to the robot and it would make pizzas and crepes for you!

Word to spread!  

Cloud robotics is different from general automation because of its use of remote operation technology as well as its reliance on cloud technologies and upcoming cloud-based business models that use cloud robots as a service. Mobile Edge Computing (MEC) Technology and 5GNR (New Radio) Technology which is based on millimeter-wave frequencies is expected to benefit Cloud Robotics at a commendable level. Obviously, the attention towards the cloud robotics market will be first drawn by the government and industrial clients, but later on, it is believed to catch the attention of consumers too.  

Challenges and open issues:  

Cloud robotics is a developing technology. Therefore, there are many open issues and challenges faced. As the concept of cloud robotics is based on real-time requirements, maintaining a balance between the real-time requirements of different situations and performance accuracy is a difficult task, even for robot memory. There is a demand for increased cloud security because cloud storage means remote storage of data. Hence, be it business-related or scientific research related privacy, cloud security requirement is mandatorily imposed. Also, maintaining the network flow for a particular bandwidth is needed to increase real-time performance efficiency.  

There are a few more technical challenges that tend to stand in the way of the efficient performance of cloud robots:

Rapidity: People are very accustomed to using applications that rapidly display the results. But, when it comes to cloud robotics, rapidity tends to go on a whole another level. For example, a robot being fast is expected to move his arm at the speed of the brain synapses, landing it at the right time and the right place. To deal with this issue, the open-source community is working on two Linux Foundation projects namely, ACRN and Zephyr. ACRN focuses on low dormancy and a good response period. Zephyr aims to build a “safe, secure and flexible real-time operating system” for the robots.

Remoteness: There could be times when the robots are unable to connect to the network, due to security reasons or physiographical reasons. During such times, the robot must predict nearby results, instead of completely relying on the cloud. This is where Edge Computing comes into the picture as a solution.

Network: Imagine a robot losing its connection to the network or cloud when on a war-like mission or a surgical one. The situation might turn deadly. The solution to this dilemma is introduced in the form of the emerging technology known as 5G. 5G has improved connectivity and a dramatic dormancy due to which, connectivity and communication issues will be taken care of. Another solution would be to create a web of mini-clouds so that even if the robot loses connectivity to the network, it can run through these clusters of resources. Swarm robots also use this same technique and it is very interesting to learn.

Ever tried using a phone without an internet connection? If you notice, the phone can still be used, but it performs only a particular set of pre-programmed tasks. Whereas with network connectivity, the mobile can do wonders, you surely know it!

The industry has been continuing to build more solutions to upcoming issues and challenges that come across. But cloud robotics can be expected to transform a range of industries and introduce even more capable robots in the near future. 

Cloud robotics is the use of cloud computing, cloud storage, and other internet technologies in the field of robotics. One of the main advantages of cloud robotics is its ability to provide vast amounts of data to robotic devices without having to incorporate it directly via onboard memory.

What is cloud robotics?

The term “cloud robotics” was created in 2010 by a research scientist at Google named James Kuffner. The concept was simple: bulk of processing for devices would take place in the cloud, enabling robot manufacturers to create lighter, cheaper devices and leverage the power of cloud infrastructure.

In 1994, the first industrial robot was connected to the internet via an intuitive graphical user interface. This interface allowed human operators to teleoperate the robot via any internet browser around the world. These advancements in robotics and networking technology led to the creation of the IEEE Robotics and Automation Society’s Technical Committee on Networked Robots in May 2001.

There are many advantages to cloud robotics. The first is its ability to provide vast amounts of data to robotic devices without having to incorporate it directly via onboard memory. The data used for operation, maintenance, and more are stored in a cloud-based database system that can be accessed remotely. Next is the ability for robots and systems to share information across the entire system to support collective learning. And finally, the cloud-based system and open-source software structure make it easy to share information between human operators to help improve the robotic devices and operational software.

The Six Components of Cloud Robotics

A cloud robotics platform is comprised of secure servers that host vast databases of information. The data stored in servers controls every aspect of the robotics machinery, from operations to analysis. Cloud robotics typically includes the following six components:

A global library of images, maps, and object data. It often includes geometry and mechanical properties, expert systems, and knowledge base;

Massively-parallel computation on-demand to allow sample-based statistical modeling and motion planning, task planning, multi-robot collaboration, scheduling, and coordination;

Shared outcomes, trajectories, and dynamic control policies as well as robot learning support;

“Open-source” code, data, and designs for easy programming, experimentation, and hardware construction;

On-demand human guidance and assistance for evaluation, learning, and error recovery;

Augmented human-robot interaction

How does cloud robotics differ from general automation?

The main differentiator between cloud robotics and general automation is the reliance on cloud technologies. Cloud robotics also lends itself to the Robots-as-a-Service business model. The cloud-based infrastructure is designed for remote access of robotic devices, and robotics companies can lease their technology via the cloud to others for a recurring fee.

Applications of Cloud Robotics

Cloud robotics is increasingly used across a variety of industries that benefit from robotics, including:

Healthcare

The medical cloud infrastructure includes services like disease archives, electronic medical records, patient health management systems, practice services, analytics services, and clinic solutions. For example, the healthcare robotic device accesses the medical cloud infrastructure to provide clinical services to patients and assist surgeons in live surgeries.

Industry/Manufacturing

As industrial and manufacturing robotic devices become increasingly complex, the data required for operating the robotic machines exceeds the limited space available in the onboard memory. Cloud-based robot systems are capable of collaborative tasks. For example, a series of industrial robotic devices can process a custom order, manufacture the order, and deliver it all on its own—without human operators.