Cloud robotics is a field of robotics that attempts to invoke cloud technologies such as cloud computing, cloud storage, and other Internet technologies centered on the benefits of converged infrastructure and shared services for robotics.
When connected to the cloud, robots can benefit from the powerful computation, storage, and communication resources of modern data center in the cloud, which can process and share information from various robots or agent (other machines, smart objects, humans, etc.).
Humans can also delegate tasks to robots remotely through networks. Cloud computing technologies enable robot systems to be endowed with powerful capability whilst reducing costs through cloud technologies.
Thus, it is possible to build lightweight, low-cost, smarter robots with an intelligent “brain” in the cloud. The “brain” consists of data center, knowledge base, task planners, deep learning, information processing, environment models, communication support, etc.
A cloud for robots potentially has at least six significant components:
- Offering a global library of images, maps, and object data, often with geometry and mechanical properties, expert system, knowledge base (i.e. semantic web, data centres)；
- Massively-parallel computation on demand for sample-based statistical modelling and motion planning, task planning, multi-robot collaboration, scheduling and coordination of system；
- Robot sharing of outcomes, trajectories, and dynamic control policies and robot learning support；
- Human sharing of “open-source” code, data, and designs for programming, experimentation, and hardware construction；
- On-demand human guidance and assistance for evaluation, learning, and error recovery;
- Augmented human–robot interaction through various way (Semantics knowledge base, Apple SIRI like service etc.)
Google’s self-driving cars are cloud robots. The cars use the network to access Google’s enormous database of maps and satellite and environment model (like Streetview) and combines it with streaming data from GPS, cameras, and 3D sensors to monitor its own position within centimetres, and with past and current traffic patterns to avoid collisions.
Each car can learn something about environments, roads, or driving, or conditions, and it sends the information to the Google cloud, where it can be used to improve the performance of other cars.
Though robots can benefit from various advantages of cloud computing, cloud is not the solution to all of robotics.
- Controlling a robot’s motion which relies heavily on (real-time) sensors and feedback of controller may not benefit much from the cloud.
- Tasks that involve real-time execution require on-board processing.
- Cloud-based applications can get slow or unavailable due to high-latency responses or network hitch. If a robot relies too much on the cloud, a fault in the network could leave it “brainless.”