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Fog robotics can be defined as an architecture which consists of storage, networking functions, control with fog computing closer to robots. [1] [2]
Fog robotics mainly consists of a fog robot server and the cloud. [3] It acts as a companion to cloud by shoving the data near to the user with the help of a local server. Moreover, these servers are adaptable, consists of processing power for computation, network capability, and secured by sharing the outcomes to other robots for advanced performance with the lowest possible latency. [2]
As cloud robotics is facing issues such as bandwidth limitations, latency issues, quality of service, privacy and security - Fog robotics can be seen as a viable option for the future robotic systems. [4] It is also considered as distributed robot systems of the next generation because robots require much brain power for processing billions of computations while performing its task. [5] For instance, fog robotics can play an essential role in helping a robot to grasp spray bottle. [6]
Chand Gudi first coined the term "Fog Robotics" during the European Space Agency Competition [7] and IEEE/RSJ International Conference on Intelligent Robots and Systems in 2017, pioneering a new concept in the field. [1] [8]
Social robots
A social robot can either connect to the cloud or fog robot server depending upon the availability of information. For instance, it can make a robot working at an airport to communicate with other robots for effective communication with the help of fog robotics. [9]
Node-level systems: FogROS [10]
FogROS is proposed by UCB[ clarification needed ] [10] . FogROS is a framework that allows existing ROS automation applications to gain access to additional computing resources from commercial cloud-based services. This framework is built on the Robot Operating System (ROS), the de facto standard for creating robot automation applications and components. With minimal porting effort, FogROS allows researchers to deploy components of their software to the cloud with high transparency.
Algorithm-level system: ElasticROS [11]
ElasticROS is proposed by HKUST.[ clarification needed ] [11] The present node-level systems are not flexible enough to dynamically adapt to changing conditions. To address this, the authors present ElasticROS, which evolves the present node-level systems into an algorithm-level one. ElasticROS is based on ROS and ROS2. For fog and cloud robotics, it is the first robot operating system with algorithm-level collaborative computing. ElasticROS develops elastic collaborative computing to achieve adaptability to dynamic conditions. The collaborative computing algorithm is the core and challenge of ElasticROS. The authors abstract the problem and then propose an algorithm named ElasAction to address. It is a dynamic action decision algorithm based on online learning, which determines how robots and servers cooperate. The algorithm dynamically updates parameters to adapt to changes of conditions where the robot is currently in. It achieves elastically distributing of computing tasks to robots and servers according to configurations. In addition, the authors prove that the regret upper bound of the ElasAction is sublinear, which guarantees its convergence and thus enables ElasticROS to be stable in its elasticity.
Fog Robotics
This project promotes the applicability of fog robotics with regards to human-robot interaction scenarios. It utilises fog robot servers, cloud, and the robots for evaluation of fog robotics architecture. [2]
Secure Fog Robotics Using the Global Data Plane [12]
To improve the security and performance of robotic/machine-learning applications operating in edge computing environments, this project investigates the use of data capsules. As one of the applications, it also examines the fog robot system to preserve the privacy and security of the data.
5G Coral: A 5G Convergent Virtualised Radio Access Network Living at the Edge [13]
This project particularly targets the field of radio access network at the edge. As part of this project, a real-time application of fog-assisted robotics is explored. Also, remote monitoring of robots and fleet formation for coordinated movement is being investigated. [14]
Fog Computing for Robotics and Industrial Automation [15]
This project focusses on designing novel programming models for Fog applications both hardware and operating system (OS) mechanisms including communication protocols of fog nodes. These fog nodes will be further tested real time on robots and other automation devices. Furthermore, an open-source architecture will be built on open standards, e.g., 5G, OPC Unified Architecture (UA), and Time-Sensitive Networking (TSN).
In computing, load balancing is the process of distributing a set of tasks over a set of resources, with the aim of making their overall processing more efficient. Load balancing can optimize the response time and avoid unevenly overloading some compute nodes while other compute nodes are left idle.
Simultaneous localization and mapping (SLAM) is the computational problem of constructing or updating a map of an unknown environment while simultaneously keeping track of an agent's location within it. While this initially appears to be a chicken or the egg problem, there are several algorithms known to solve it in, at least approximately, tractable time for certain environments. Popular approximate solution methods include the particle filter, extended Kalman filter, covariance intersection, and GraphSLAM. SLAM algorithms are based on concepts in computational geometry and computer vision, and are used in robot navigation, robotic mapping and odometry for virtual reality or augmented reality.
Edge computing is a distributed computing model that brings computation and data storage closer to the sources of data, so that a user is likely to be physically closer to a server than if all servers were in one place. This often makes applications faster. More broadly, it refers to any design that pushes computation physically closer to a user, so as to reduce the latency compared to when an application runs on a single data centre.
A rapidly exploring random tree (RRT) is an algorithm designed to efficiently search nonconvex, high-dimensional spaces by randomly building a space-filling tree. The tree is constructed incrementally from samples drawn randomly from the search space and is inherently biased to grow towards large unsearched areas of the problem. RRTs were developed by Steven M. LaValle and James J. Kuffner Jr. They easily handle problems with obstacles and differential constraints and have been widely used in autonomous robotic motion planning.
RHex is an autonomous robot design, based on hexapod with compliant legs and one actuator per leg. A number of US universities have participated, with funding grants also coming from DARPA.
In robotics and motion planning, a velocity obstacle, commonly abbreviated VO, is the set of all velocities of a robot that will result in a collision with another robot at some moment in time, assuming that the other robot maintains its current velocity. If the robot chooses a velocity inside the velocity obstacle then the two robots will eventually collide, if it chooses a velocity outside the velocity obstacle, such a collision is guaranteed not to occur.
Any-angle path planning algorithms are pathfinding algorithms that search for a Euclidean shortest path between two points on a grid map while allowing the turns in the path to have any angle. The result is a path that cuts directly through open areas and has relatively few turns. More traditional pathfinding algorithms such as A* either lack in performance or produce jagged, indirect paths.
Cloud load balancing is a type of load balancing that is performed in cloud computing. Cloud load balancing is the process of distributing workloads across multiple computing resources. Cloud load balancing reduces costs associated with document management systems and maximizes availability of resources. It is a type of load balancing and not to be confused with Domain Name System (DNS) load balancing. While DNS load balancing uses software or hardware to perform the function, cloud load balancing uses services offered by various computer network companies.
A behavior tree is a mathematical model of plan execution used in computer science, robotics, control systems and video games. They describe switchings between a finite set of tasks in a modular fashion. Their strength comes from their ability to create very complex tasks composed of simple tasks, without worrying how the simple tasks are implemented. Behavior trees present some similarities to hierarchical state machines with the key difference that the main building block of a behavior is a task rather than a state. Its ease of human understanding make behavior trees less error prone and very popular in the game developer community. Behavior trees have been shown to generalize several other control architectures.
Fog computing or fog networking, also known as fogging, is an architecture that uses edge devices to carry out a substantial amount of computation, storage, and communication locally and routed over the Internet backbone.
Multi-access edge computing (MEC), formerly mobile edge computing, is an ETSI-defined network architecture concept that enables cloud computing capabilities and an IT service environment at the edge of the cellular network and, more in general at the edge of any network. The basic idea behind MEC is that by running applications and performing related processing tasks closer to the cellular customer, network congestion is reduced and applications perform better. MEC technology is designed to be implemented at the cellular base stations or other edge nodes, and enables flexible and rapid deployment of new applications and services for customers. Combining elements of information technology and telecommunications networking, MEC also allows cellular operators to open their radio access network (RAN) to authorized third parties, such as application developers and content providers.
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. 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 cloudlet is a mobility-enhanced small-scale cloud datacenter that is located at the edge of the Internet. The main purpose of the cloudlet is supporting resource-intensive and interactive mobile applications by providing powerful computing resources to mobile devices with lower latency. It is a new architectural element that extends today's cloud computing infrastructure. It represents the middle tier of a 3-tier hierarchy: mobile device - cloudlet - cloud. A cloudlet can be viewed as a data center in a box whose goal is to bring the cloud closer. The cloudlet term was first coined by M. Satyanarayanan, Victor Bahl, Ramón Cáceres, and Nigel Davies, and a prototype implementation is developed by Carnegie Mellon University as a research project. The concept of cloudlet is also known as follow me cloud, and mobile micro-cloud.
Gregory D. Hager is the Mandell Bellmore Professor of Computer Science and founding director of the Johns Hopkins Malone Center for Engineering in Healthcare at Johns Hopkins University.
Federated learning is a sub-field of machine learning focusing on settings in which multiple entities collaboratively train a model while ensuring that their data remains decentralized. This stands in contrast to machine learning settings in which data is centrally stored. One of the primary defining characteristics of federated learning is data heterogeneity. Due to the decentralized nature of the clients' data, there is no guarantee that data samples held by each client are independently and identically distributed.
Margarita Chli is an assistant professor and leader of the Vision for Robotics Lab at ETH Zürich in Switzerland. Chli is a leader in the field of computer vision and robotics and was on the team of researchers to develop the first fully autonomous helicopter with onboard localization and mapping. Chli is also the Vice Director of the Institute of Robotics and Intelligent Systems and an Honorary Fellow of the University of Edinburgh in the United Kingdom. Her research currently focuses on developing visual perception and intelligence in flying autonomous robotic systems.
Asynchronous multi-body framework (AMBF) is an open-source 3D versatile simulator for robots developed in April 2019. This multi-body framework provides a real-time dynamic simulation of multi-bodies such as robots, free bodies, and multi-link puzzles, paired with real-time haptic interaction with various input devices. The framework integrates a real surgeon master console, haptic or not, to control simulated robots in real-time. This feature results in the simulator being used in real-time training applications for surgical and non-surgical tasks. It offers the possibility to interact with soft bodies to simulate surgical tasks where tissues are subject to deformations. It also provides a Python Client to interact easily with the simulated bodies and train neural networks on real-time data with in-loop simulation. It includes a wide range of robots, grippers, sensors, puzzles, and soft bodies. Each simulated object is represented as an afObject; likewise, the simulation world is represented as an afWorld. Both utilize two communication interfaces: state and command. Through the State command, the object can send data outside the simulation environment, while the Command allows to apply commands to the underlying afObject.
A continuum robot is a type of robot that is characterised by infinite degrees of freedom and number of joints. These characteristics allow continuum manipulators to adjust and modify their shape at any point along their length, granting them the possibility to work in confined spaces and complex environments where standard rigid-link robots cannot operate. In particular, we can define a continuum robot as an actuatable structure whose constitutive material forms curves with continuous tangent vectors. This is a fundamental definition that allows to distinguish between continuum robots and snake-arm robots or hyper-redundant manipulators: the presence of rigid links and joints allows them to only approximately perform curves with continuous tangent vectors.
Saverio Mascolo is an Italian information engineer, academic and researcher. He is the former Head of the Department of Electrical Engineering and Information Science and the professor of Automatic Control at Department of Ingegneria Elettrica e dell'Informazione (DEI) at Politecnico di Bari, Italy.
Jürgen Sturm is a German software engineer, entrepreneur and academic. He is a Senior Staff Software Engineering Manager at Intrinsic, where he works on developing a robot SDK aimed at facilitating and reducing the cost of integrating AI-/ML-powered robots into industrial manufacturing processes.
