Consumer Interest in Intelligent Systems Drives Advances in Learning-Based Robotics.DUBLIN, Ireland -- Research and Markets (http://www.researchandmarkets.com/reports/c49736) announces the addition of Frost & Sullivan's new report Machine Learning-Based Robotics in Unstructured Environments to their offering. This Frost & Sullivan research titled Machine Learning-Based Robotics in Unstructured Environments provides an in-depth analysis of the technical developments surrounding learning and teaching methodologies adoption in service and networked robotics. It looks at its impact on intelligent robots through key drivers, industry challenges, and patent analysis. Technology Sectors Expert Frost & Sullivan analysts thoroughly examine the following technology sectors in this research: - Industrial automation and process control - Industrial robotics - HMI (Human Machine Interface) The user interface in a manufacturing or process control system. It provides a graphics-based visualization of an industrial control and monitoring system. - Sensors Technologies The following technologies are covered in this research: - Sensor technology - Speech and face recognition - Radio frequency identification See RFID. (RFID (Radio Frequency IDentification) A data collection technology that uses electronic tags for storing data. The tag, also known as an "electronic label," "transponder" or "code plate," is made up of an RFID chip attached to an antenna. ) Content Outline: 1. Executive Summary 2. Technology; Applications--Viewpoint and Roadmap 3. Technology Adoption Factor Analysis 4. Assessment of Global Research and Innovations 5. Directory of Patents and Key Contacts 6. Decision Support Database List of Figures Chapter 2 Supervised learning Supervised learning is a machine learning technique for creating a function from training data. The training data consist of pairs of input objects (typically vectors), and desired outputs. Unsupervised learning Unsupervised learning is a method of machine learning where a model is fit to observations. It is distinguished from supervised learning by the fact that there is no a priori output. In unsupervised learning, a data set of input objects is gathered. Reinforcement learning For reinforcement learning in psychology, see . Derived from the psychological theory of the same name, in computer science, reinforcement learning is a sub-area of machine learning concerned with how an agent ought to take actions in an environment Traditional decomposition decomposition /de·com·po·si·tion/ (de-kom?pah-zish´un) the separation of compound bodies into their constituent principles. de·com·po·si·tion n. 1. of an intelligent control system New approach to an intelligent control system Application of networked robotics Roadmap to personal robots Network robotics--An approximate timeline Chapter 3 Technology challenge roadmap--Components Technology challenge roadmap--Advanced behaviors Network robotics research challenges Technical challenges faced in network robots Technology Overview The Growth of Learning-based Robotics is Dependent on Development in Related Hardware The possibility of building learning systems, which can operate on realistic robots, is a challenging task since learning-based robots need clear sensing/perception capabilities and related hardware. "Although hardware is evolving and promising, software mechanisms still dominate in learning," according to according to prep. 1. As stated or indicated by; on the authority of: according to historians. 2. In keeping with: according to instructions. 3. the analyst of the study. "Since machines cannot be made 'ready to go' in a complex environment, they are likely to be improved by implementing on-board learning skills." Service robotics, which includes industrial, service, and personal robots, is a rapidly emerging market. Despite this, the growth rate is restrained by lack of significant capital investment. This market demands high entry fees and investment from every industrial robot An industrial robot is officially defined by ISO[1] as an automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes. manufacturing entrant en·trant n. One that enters, especially one that enters a competition. [French, from present participle of entrer, to enter, from Old French; see enter. , which reduces the participant's profit margin. Ultimately, size, shape, mobility, interaction, and safe operation are crucial for the success of service robotics that is implemented in natural environments. These features, however, depend on developments in related hardware fields. Consumers Interest in Intelligent Systems Drives Advances in Learning-based Robotics The need for flexible production systems that can be applied in non-structured environments and current interest in building intelligent systems have driven research on networked robotics. As the robot network begin to function in an unstructured environment, visualization, mapping, sensing, and information processing information processing: see data processing. information processing Acquisition, recording, organization, retrieval, display, and dissemination of information. Today the term usually refers to computer-based operations. change from the structured to the unstructured environment. Hence, there is a pressing need to address dynamic topology management in networked robots. The current trend demands that robots possess the ability to identify and plan a sequence of action, plan actions independent of their actual execution, and have the capability to modify or abandon plans. "Autonomous acquisition and execution in robotics require robots to learn to operate and undertake decisions autonomously," explains the analyst. "In addition, developments in artificial intelligence (AI) have led to the incorporation of intelligence and common sense in robots to help them work in changing and unstructured environments." For more information visit http://www.researchandmarkets.com/reports/c49736 |
|
Printer friendly
Cite/link
Email
Feedback
Reader Opinion