Parallel Learning Architecture of micROS Powering the Ability of Life-Long Autonomous Learning
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摘要: 作为机器人平台最重要的基础软件，机器人操作系统是提高机器人自主性与智能化水平的核心和关键.围绕实现适应环境的智能机器人系统这一目标，基于已有的micROS研究，提出了可持续自主学习的群体智能机器人操作系统平行学习架构，描述了架构设计、核心概念、实现途径和应用验证.在micROS可扩展分布式层次架构的基础上，提出了支持可持续自主学习的平行学习架构，设计并实现了机器人操作系统的两大核心概念——基于“角色”的控制抽象和基于“语义情境图”的数据抽象，突破了群体智能行为操控、自组织无线网络等群体机器人自主智能协同急需解决的关键技术问题，在此基础上开展了面向多种场景的应用验证.Abstract: As the most important infrastructure of robotic platforms, robot operating system is playing an important role to improve autonomy and intelligence of robots and unmanned systems. In this paper, a parallel learning architecture of micROS supporting life-long autonomous learning is presented. It is built to power a wide variety of robots with the ability of contextual adaptation. In addition, two core concepts guiding the design of micROS are also presented. One concept is the actor, which is the control abstraction of robot behaviors. The other concept is the semantic situation abstracting the dataflow in micROS. Some important techniques including collective behavior control and ad hoc wireless networks, are also described in this paper.
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