The Reinforcement Framework of a Decision Support System for the Localization and Monitoring of Intelligent Remote Bio Robots

This paper analyses the possibilities of the integration of different technological and knowledge representation techniques for the development of reinforcement frameworks for the remote control of multiple agents such as wheelchair-type robots. Some technological solutions are discussed regarding the recognition of localization of moving objects by using mobile technologies. Large-scale multi-dimensional recognitions of emotional diagnoses of disabled persons often generate large amounts of multidimensional data with complex recognition mechanisms, based on the integration of different knowledge representation techniques and complex inference models. The problem is to reveal the main components of a diagnosis as well as to construct flexible decision making models. Sensors can help to record primary data for monitoring objects; however the recognition of abnormal situations, the clustering of emotional stages and resolutions for certain types of diagnoses is an oncoming issue for bio-robot constructors. The prediction criteria of the diagnosis of the emotional situation of disabled persons are described using knowledge based models of neural networks. The research results present the development of a multi-layered framework architecture with the integration of artificial agents and support components for diagnosis recognition and control, or further actions, by using mobile technologies. The method of fuzzy neural network control of the speed of wheelchair-type robots working in real time by providing movement support for disabled individuals is presented. The fuzzy reasoning by using fuzzy logical Petri nets is described in order to define the physiological state of disabled individuals through recognizing their emotions during their different activities. Some new possibilities of the recognition of moving object location are introduced in the system.