从SLAM到情境意识:挑战和调查
Hriday Bavle1, Jose Luis Sanchez-Lopez1, Claudio Cimarelli1
1Interdisciplinary Center for Security Reliability and Trust (SnT), University of Luxembourg, 1855 Luxembourg, Luxembourg.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了移动机器人的情境意识 (SA),将类似人类的理解整合到自主操作中. 它提出了一个情境图 (S-图) 统一碎片化机器人理解算法,用于现实世界的应用.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 人与机器人的交互
- 认知系统 认知系统
背景情况:
- 移动机器人的自主性受到环境理解的限制,称为"情况".
- 现有的机器人研究将感知和SLAM等概念划分为部分,忽视了整体的情境意识 (SA).
- SA是一种关键的人类能力,在心理学,军事,航空航天和教育方面得到了广泛的研究.
研究的目的:
- 弥合跨学科的SA知识与机器人技术之间的差距.
- 开发一个全面的SA系统,以提高移动机器人的自主性.
- 定义机器人SA的组件,并调查当前的算法能力和局限性.
主要方法:
- 调查与SA组件相关的最先进的机器人算法.
- 在不同的环境中分析现有算法的当前局限性.
- 研究人工智能 (AI),特别是深度学习 (DL) 的潜力,以推进SA.
- 提出情境图 (S图) 作为机器人理解的统一机制.
主要成果:
- 机器人SA的基本方面仍然不成熟,算法通常仅限于特定环境.
- 人工智能和DL提供了有希望的方法来克服当前的局限性,并使现实世界的部署.
- 情境图 (S-图) 呈现了一种新的方法,用于相互连接碎片化机器人理解算法.
结论:
- 一个整体的SA系统对于实现真正的移动机器人自主性至关重要.
- 需要取得重大进展,但AI和DL提供了前进的途径.
- 拟议的S图为机器人综合局势理解提供了未来的方向.
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