Abstract: The present disclosure provides a model for semantic navigation for service robots to find out-of-view objects in an indoor environment. Initially, the system receives a target object to be reached by the mobile robot in the indoor environment. Further, a current location of the mobile robot is identified by a localization technique. An embedding corresponding to each of a plurality of visible regions is computed using a pretrained Graph Neural Network GNN. The GNN is pretrained using a trajectory data and a spatial relationship graph associated with the indoor environment. Further, a similarity score is computed for each of the plurality of visible regions based on the corresponding embedding using a scoring technique. An optimal visible region is identified by comparing the similarity score. Finally, a next action to be taken by the mobile robot selected from a plurality of actions based on the optimal visible region.

Abstract: The embodiments of present disclosure herein address unresolved problem of cognitive navigation strategies for a telepresence robotic system. This includes giving instruction remotely over network to go to a point in an indoor space, to go an area, to go to an object. Also, human robot interaction to give and understand interaction is not integrated in a common telepresence framework. The embodiments herein provide a telepresence robotic system empowered with a smart navigation which is based on in situ intelligent visual semantic mapping of the live scene captured by a robot. It further presents an edge-centric software architecture of a teledrive comprising a speech recognition based HRI, a navigation module and a real-time WebRTC based communication framework that holds the entire telepresence robotic system together. Additionally, the disclosure provides a robot independent API calls via device driver ROS, making the offering hardware independent and capable of running in any robot.

Abstract: This disclosure relates generally to detection of inconsistencies in Semantics of Business Vocabulary and Business Rules (SBVR) rules, and, more particularly, to detecting inconsistencies in SBVR using Many-Sorted Logic. Traditional systems and methods implementing graphical and other related techniques are not sufficient to detect anomalies in semantic rules, thereby leading to a different verification approach to detect logical anomalies and errors due to quantification. Embodiment of the present disclosure overcome the limitations faced by the traditional systems and methods by translating a set of SBVR rules into a SBVR eXtensible Markup Language Metadata Interchange (XMI); transforming the SBVR XMI into one or more Satisfiability Modulo Theories Library (SMT-LIB) formulas using at least one logical structure and semantic of Many-Sorted Logic; and detecting, from the one or more SMT-LIB formulas, one or more inconsistencies in semantics of SBVR by using a SMT solver invocation.

Abstract: This disclosure relates generally to detection of inconsistencies in Semantics of Business Vocabulary and Business Rules (SBVR) rules, and, more particularly, to detecting inconsistencies in SBVR using Many-Sorted Logic. Traditional systems and methods implementing graphical and other related techniques are not sufficient to detect anomalies in semantic rules, thereby leading to a different verification approach to detect logical anomalies and errors due to quantification. Embodiment of the present disclosure overcome the limitations faced by the traditional systems and methods by translating a set of SBVR rules into a SBVR eXtensible Markup Language Metadata Interchange (XMI); transforming the SBVR XMI into one or more Satisfiability Modulo Theories Library (SMT-LIB) formulas using at least one logical structure and semantic of Many-Sorted Logic; and detecting, from the one or more SMT-LIB formulas, one or more inconsistencies in semantics of SBVR by using a SMT solver invocation.