110 IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.9, September 2016 A Study on Cloud Robotics Architecture, Challenges and Applications G.Arunajyothi Vignana Bharathi Institute Of Technology,Hyderabad Abstract exchanging sensing data and information via the We extend the computation and information sharing capabilities communication network. Examples include cooperative of networked robotics by proposing a cloud robotic architecture. robot manipulators, a team of net-worked robots The cloud robotic architecture leverages the combination of an performing search and rescue missions, and a group of ad-hoc cloud formed by machine-to-machine (M2M) micro satellites working cooperatively in a desired communications among participating robots, and an formation. infrastructure cloud enabled by machine-to-cloud (M2C) communications. Cloud robotics utilizes an elastic computing Networked robotics, similar to standalone robots, faces model, in which resources are dynamically allocated from a inherent physical constraints as all computations are shared resource pool in the ubiquitous cloud, to support task conducted onboard the robots, which have limited offloading and information sharing in robotic applications. We computing capabilities. Information access is also propose and evaluate communication protocols, and several restricted to the collective storage of the network. With the elastic computing models to handle different applications. We rapid advancement of wire-less communications and discuss the technical challenges in computation, communications recent innovations in cloud computing technologies, some and security, and illustrate the potential benefits of cloud robotics of these constraints can be overcome through the concept in different applications. of cloud robotics, leading to more intelligent, efficient and Keyword yet cheaper robotic networks. In this article, we describe a Cloud, robotics architecture cloud robotics architecture, some of the technical challenges, and its potential applications. Some 1. Introduction preliminary results on the optimal operation of cloud robotics are also presented. Robotic systems have brought significant socio economic The rest of the article is organized as follows. First, we impacts to human lives over the past few decades [1]. For outline various challenges and constraints in networked example, industrial robots (especially robot manipulators) robotics. Next, we describe the cloud robotics architecture, have been widely deployed in factories to do tedious, and elaborate on two key enabling sub-systems. Following repetitive, or dangerous tasks, such as assembly, painting, that we address technical challenges in designing and packaging, and welding. These preprogrammed robots operating the cloud robotics architecture. We will also have been very successful in industrial applications due to highlight a few important robotic applications that will their high endurance, speed, and precision in structured benefit from the cloud robotics. Finally, we conclude and factory environments. To extend the functional range of summarize this article. these robots or to deploy them in unstructured environments, robot-ic technologies are integrated with network technologies to foster the emergence of 2. Challenges in Networked Robotics networked robotics. Networked robotics, especially the multi-robot system as A networked robotic system refers to a group of robotic illustrated in Fig. 1, distributes the workload of sensing, devices that are connected via a wired and/or wireless actuating, communication, and computation among a communication network [2]. Networked robotics group of participating robots. It has achieved great success applications can be classified as either tele operated robots in industrial applications, intelligent transportation systems, or multi-robot systems. In the former case, a human and security applications. However, the advancement of operator controls or manipulates a robot at a distance by networked robotics is restricted by resource, information, sending commands and receiving measurements via the and communication constraints inherent in the existing communication net-work. Application examples include framework. We dis-cuss these constraints in detail in this remote control of a planetary rover and remote medical section. surgery. In the latter case, a team of networked robots complete a task cooperatively in a distributed fashion by Manuscript received September 5, 2016 Manuscript revised September 20, 2016 IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.9, September 2016 111 2.1 Resource Constraints computation and memory resources in the route discovery and maintenance process. Ad-hoc routing protocols suffer from a Although a robot can share its computation workload with high latency as a route has to be established before a message can other units in the formation, the overall effectiveness of be sent, and are not practical if the network topology is highly the robotic network is limited by the collection of each dynamic. These drawbacks are significant in mobile robotic networks, and may lead to severe performance degradation. robot’s resources, including onboard computers or embedded computing units, memories, and storage space. 2.4 From Networked Robotics to Cloud Robotics Physically, these onboard computing devices are restricted by the robots’ size, shape, power supply, motion mode, Networked robotics can be considered as an evolutionary step and working environment. Once the robots are designed, towards cloud robotics, i.e., cloud-enabled networked robotics, built and deployed, it is technically challenging, if not which leverages emerging cloud computing technologies to infeasible, to change or upgrade their resource transform networked robotics. The design objective is to over- configurations. come the limitations of networked robotics with elastic resources offered by a ubiquitous cloud infrastructure. Cloud computing provides a natural venue to extend the capabilities of networked robotics. NIST [5] defines cloud computing as “a model for enabling ubiquitous, convenient, on- demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction.” Through its three service models (i.e., software, platform and infrastructure), it enables tremendous flexibility in designing and implementing new applications for net-worked robotics. Several research groups have started to explore the use of cloud technologies in robotic applications. For example, research groups at Google have developed smart-phone driven robots that can learn from each other via the cloud [6]. A research group at Figure 1. Networked Robotics: a team of robots are interconnected with a Singapore’s ASORO laboratory has built a cloud computing communication net-work to collaboratively accomplish tasks. infrastructure to generate 3-D models of environments, allowing robots to perform simultaneous localization and mapping 2.2 Information and Learning Constraints (SLAM) much faster than by relying on their onboard computers [7]. The amount of information a robot has access to is con-strained by its processing power, storage space, and the number and type of sensors it carries. Networked robotics allows the sharing of 3. Cloud Robotics information amongst robots connected by a communication network so that a global task can be solved or computed In this section, we first describe a system architecture for cooperatively using the whole network. However, networked cloud robotics, and then focus on the two key enabling robotics is constrained by the information observed or computed subsystems: the M2M/M2C communication framework by robots in the network, and by the examples or scenarios that the network encounters, and hence limiting its ability to learn. A and the elastic computing architecture. Our cloud robotics robotic team learning to navigate may per-form very well in a differentiates from existing solutions in that it leverages static environment, where all obstacles can be mapped out with two complementary clouds (i.e., an ad-hoc cloud and an increasing accuracy over time. On the other hand, the learning infrastructure cloud). process has to be repeated once the environment changes or the robotic team is placed in a new unfamiliar environment. The map 3.1. System Architecture databases maintained by the robotic formation is also limited by the collective amount of storage space (including memory and In Fig. 2, we illustrate the system architecture for our pro- disk) the formation has. posed cloud robotics. The architecture is organized into two complementary tiers: a machine-to-machine (M2M) 2.3 Communication Constraints level and a machine-to-cloud (M2C) level. On the M2M level, a group of robots communicate via Common protocols for machine-to-machine (M2M) communications include proactive routing, which involves the periodic exchange of messages so that routes to every possible destination in the network are maintained [3], and ad-hoc routing, which forms a dynamic route to a destination node only when there is a message to be sent [4]. Proactive routing incurs high 112 IJCSNS International Journal of Computer Science and Network Security, VOL.16 No.9, September 2016 Several standards like Zigbee, Bluetooth, and Wi-Fi Direct have been developed for