クラウドとオンデマンドDBを用いた人間らしい高次インタラクションとタスク計画を実現するリアルタイム自律ロボットナビゲーションフレームワーク
A Realtime Autonomous Robot Navigation Framework for Human like High-level Interaction and Task Planning in Global Dynamic Environment
ロボットの記憶を長期・短期に分け、空間情報による脳型GPSマッピングと3D視覚意味SLAMを組み合わせ、未知の動的環境で人間らしい対話とタスク計画を可能にする自律ナビゲーションの枠組みを提案し、ROS/Gazeboの屋内環境シミュレーションで有効性を示した。
著者: Sung-Hyeon Joo, Sumaira Manzoor, Yuri Goncalves Rocha, Hyun-Uk Lee, Tae-Yong Kuc
分類: cs.RO
原文アブストラクト
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.