What is Cloud Robotics
Cloud robotics is an emerging field of robotics ingrained in cloud computing. It allows robots to benefit from the powerful computational and communications resources of modern data centers.When computational or storage demands exceed the on-board capacity of a robot, where the massive resources of a datacenter can supplement their limited local resource.
So cloud robotics includes other internet related aspects of robotics such as trends towards online sharing of open source hardware and software, crowd sourcing of robotics funding tele presence and human based computation.
Cloud-enabled robots could offload CPU-heavy tasks to remote servers, relying on smaller and less power-hungry onboard computers. Using the cloud, a robot could improve capabilities such as speech recognition, language translation, path planning, and 3D mapping.
Advantages of using cloud computing in Robotics:
- Cloud based robotics provides shared knowledge, database on real time. This makes robots smarter.
- Cloud robotics can transfer heavy computer task to the cloud. This leads to cheaper lighter and easy hardware maintenance. Hardware and software updates can be in real time without losing the data.
- Any old robot which is full on rust and dust can be reuse properly by using hardware and software of cloud infrastructure.
Disadvantages of using cloud computing in Robotics:
Environmental security – The concentration of computing resources and users in a cloud computing environment also represents a concentration of security threats. Because of their size and significance, cloud environments are often targeted by virtual machines and bot malware, brute force attacks, and other attacks.
Data privacy and security – Hosting confidential data with cloud service providers involves the transfer of a considerable amount of an organisation’s control over data security to the provider. For example, every cloud contains a huge information from the clients include personal data. Another problems is once a robot is hacked and controlled by someone else, which may put the user in danger.
how does it affect safety?
Autonomous cars can use the cloud data as they navigate accessing voluminous amounts of navigation data. The Google self-driving car already uses maps and images collected by satellites and stored on the cloud.
The problem is that hackers can also access “vast amounts of computing power” and data. Safety systems in factory robots could be maliciously overridden and cause personal injury or even loss of life.
Cars could be provided erroneous data from the cloud causing crashes that could result in the loss of life. Imagine a hacker with access to cloud computing power!
This is where exida can provide value by performing Safety and Security audits. Safety and security are very much codependent for a complete safety solution. Outcomes of the audits lay the groundwork for a robust security and safety architecture.
Examples of Cloud robotics project:
ASORO labs : Researchers at Singapore ASORO labs have built a cloud computing infrastructure to generate 3D model of environment which allows robots to perform simultaneous localization and mapping. This process is much faster than their computers
LAAS : here scientist are developing robotic objective database for robots to simplify the manipulation task like simple process of opening doors.
Gostai : this French robotic firm has developed a cloud based robotic infrastructure known as GostaiNet which allows robots to perform speech recognition , face detection and other task remotely. Gostai;s robot uses the cloud for video recording and voice synthesis.
Google’s self-driving cars are one type of cloud-connected robot. The autonomous cars access data from Google Maps and images stored in the cloud to recognize their surroundings. They also gather information about road and traffic conditions and send that information back to the cloud.
Rapyuta is an open source cloud robotics framework based on RoboEarth Engine developed by the robotics researcher at ETHZ.
C2RO (C2RO Cloud Robotics) is a platform that processes real-time applications such as collision avoidance and object recognition in the cloud. Previously, high latency times prevented these applications from being processed in the cloud thus requiring on-system computational hardware (e.g. Graphics Processing Unit or GPU).
Cloud Robotics is still very new and has to undergo a long series of designing and developing. There are many problems to deal with before it goes to mainstream.