A Realtime Autonomous Robot Navigation Framework for Human like High-level Interaction and Task Planning in Global Dynamic Environment
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In this paper, we present a framework for real-time autonomous robot navigation based on cloud and on-demand databases to address two major issues of human-like robot interaction and task planning in global dynamic environment, which is not known a priori. Our framework contributes to make human-like brain GPS mapping system for robot using spatial information and performs 3D visual semantic SLAM for independent robot navigation. We accomplish the feat by separating robot's memory system into Long-Term Memory (LTM) and Short-Term Memory (STM). We also form robot's behavior and knowledge system by linking these memories to Autonomous Navigation Module (ANM), Learning Module (LM), and Behavior Planner Module (BPM). The proposed framework is assessed through simulation using ROS-based Gazebo-simulated mobile robot, RGB-D camera (3D sensor) and a laser range finder (2D sensor) in 3D model of realistic indoor environment. Simulation corroborates the substantial practical merit of our proposed framework.
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