TheoverallarchitectureofmyproposedmethodologyispresentedinFigure3.1. Iproposecloudsupported large-scale distributed learning based on lightweight robots. I assume the robots participating in the distributed system will have a single Kinect and communicate over Wi-Fi. The infrastructure systemwillconsistoffivemaincomponents: commoninterface,mesagemanager,registryoperation, resource manager and the cooperative learning model. The common interface provides a standard framework for communication and messaging across robots. The message manager works like a master control node for handling the message exchange between system and robots. The registry manages information about the robots that are availbale on the distributed system. The resource manager is responsible for handling requests for information from robots. Finally, the cooperative learningmodelsupportslearningandsearchingforknowledgeofgraspingstrategiesforobjects. The platform works with a robot registry database and a repository for grasping point information.
Mymethodologycanbedividedintofourmainparts: objectrecognitionandgraspingpointdetection, development of the environment for cloud robotics, cooperative learning via cloud robotics, and experimentalevaluation. IwillevaluatethesystemusingtheRoboticsAutomationVirtualEnvironment provided by OpenRAVE (2013) software for quantitative data. I will also perform a limited qualitative real-world evaluation using a Turtlebot 2 and lightweight manipulator, as shown in Figure 3.2. This evaluation will focus on elder care assistance in fetching small objects.
TheoverallarchitectureofmyproposedmethodologyispresentedinFigure3.1. Iproposecloudsupported large-scale distributed learning based on lightweight robots. I assume the robots participating in the distributed system will have a single Kinect and communicate over Wi-Fi. The infrastructure systemwillconsistoffivemaincomponents: commoninterface,mesagemanager,registryoperation, resource manager and the cooperative learning model. The common interface provides a standard framework for communication and messaging across robots. The message manager works like a master control node for handling the message exchange between system and robots. The registry manages information about the robots that are availbale on the distributed system. The resource manager is responsible for handling requests for information from robots. Finally, the cooperative learningmodelsupportslearningandsearchingforknowledgeofgraspingstrategiesforobjects. The platform works with a robot registry database and a repository for grasping point information.
Mymethodologycanbedividedintofourmainparts: objectrecognitionandgraspingpointdetection, development of the environment for cloud robotics, cooperative learning via cloud robotics, and experimentalevaluation. IwillevaluatethesystemusingtheRoboticsAutomationVirtualEnvironment provided by OpenRAVE (2013) software for quantitative data. I will also perform a limited qualitative real-world evaluation using a Turtlebot 2 and lightweight manipulator, as shown in Figure 3.2. This evaluation will focus on elder care assistance in fetching small objects.
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