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A cognitive robotic system based on the Soar cognitive architecture for mobile robot navigation, search, and mapping missions.

机译:基于Soar认知架构的认知机器人系统,用于移动机器人导航,搜索和地图绘制任务。

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摘要

Most unmanned vehicles used for civilian and military applications are remotely operated or are designed for specific applications. As these vehicles are used to perform more difficult missions or a larger number of missions in remote environments, there will be a great need for these vehicles to behave intelligently and autonomously. Cognitive architectures, computer programs that define mechanisms that are important for modeling and generating domain-independent intelligent behavior, have the potential for generating intelligent and autonomous behavior in unmanned vehicles. The research described in this presentation explored the use of the Soar cognitive architecture for cognitive robotics.;The Cognitive Robotic System (CRS) has been developed to integrate software systems for motor control and sensor processing with Soar for unmanned vehicle control. The CRS has been tested using two mobile robot missions: outdoor navigation and search in an indoor environment. The use of the CRS for the outdoor navigation mission demonstrated that a Soar agent could autonomously navigate to a specified location while avoiding obstacles, including cul-de-sacs, with only a minimal amount of knowledge about the environment. While most systems use information from maps or long-range perceptual capabilities to avoid cul-de-sacs, a Soar agent in the CRS was able to recognize when a simple approach to avoiding obstacles was unsuccessful and switch to a different strategy for avoiding complex obstacles.;During the indoor search mission, the CRS autonomously and intelligently searches a building for an object of interest and common intersection types. While searching the building, the Soar agent builds a topological map of the environment using information about the intersections the CRS detects. The agent uses this topological model (along with Soar's reasoning, planning, and learning mechanisms) to make intelligent decisions about how to effectively search the building. Once the object of interest has been detected, the Soar agent uses the topological map to make decisions about how to efficiently return to the location where the mission began. Additionally, the CRS can send an email containing step-by-step directions using the intersections in the environment as landmarks that describe a direct path from the mission's start location to the object of interest.;The CRS has displayed several characteristics of intelligent behavior, including reasoning, planning, learning, and communication of learned knowledge, while autonomously performing two missions. The CRS has also demonstrated how Soar can be integrated with common robotic motor and perceptual systems that complement the strengths of Soar for unmanned vehicles and is one of the few systems that use perceptual systems such as occupancy grid, computer vision, and fuzzy logic algorithms with cognitive architectures for robotics. The use of these perceptual systems to generate symbolic information about the environment during the indoor search mission allowed the CRS to use Soar's planning and learning mechanisms, which have rarely been used by agents to control mobile robots in real environments. Additionally, the system developed for the indoor search mission represents the first known use of a topological map with a cognitive architecture on a mobile robot. The ability to learn both a topological map and production rules allowed the Soar agent used during the indoor search mission to make intelligent decisions and behave more efficiently as it learned about its environment. While the CRS has been applied to two different missions, it has been developed with the intention that it be extended in the future so it can be used as a general system for mobile robot control. The CRS can be expanded through the addition of new sensors and sensor processing algorithms, development of Soar agents with more production rules, and the use of new architectural mechanisms in Soar.
机译:用于民用和军事用途的大多数无人驾驶车辆都是远程操作的或针对特定应用而设计的。由于这些车辆用于在远程环境中执行更困难的任务或执行大量任务,因此,这些车辆将非常需要智能且自主地运行。认知架构,计算机程序定义了对建模和生成与领域无关的智能行为至关重要的机制,它们有可能在无人驾驶车辆中生成智能和自主行为。本演示文稿中描述的研究探索了将Soar认知体系结构用于认知机器人的方法。认知机器人系统(CRS)已开发为将用于电机控制和传感器处理的软件系统与用于无人驾驶车辆控制的Soar集成在一起。已使用两个移动机器人任务对CRS进行了测试:室外导航和室内环境中的搜索。在户外导航任务中使用CRS证明,Soar代理可以自动导航到指定位置,同时仅需很少的环境知识即可避开包括死胡同的障碍。尽管大多数系统使用来自地图的信息或远距离感知能力来避免死角,但CRS中的Soar代理能够识别何时无法通过简单的避障方法并转而采用其他策略来避开复杂的障碍。;在室内搜索任务期间,CRS会自动并智能地搜索建筑物中的目标物体和常见的路口类型。在搜索建筑物时,Soar代理使用有关CRS检测到的交叉路口的信息来构建环境的拓扑图。代理使用此拓扑模型(以及Soar的推理,规划和学习机制)就如何有效搜索建筑物做出明智的决策。一旦检测到感兴趣的对象,Soar代理便会使用拓扑图来决定如何有效地返回任务开始的位置。此外,CRS可以使用环境中的交叉路口作为地标来发送包含分步指导的电子邮件,这些路标描述从任务开始位置到感兴趣对象的直接路径。; CRS显示了智能行为的几个特征,包括推理,计划,学习和交流所学知识,同时自主执行两项任务。 CRS还展示了Soar如何与常见的机器人电机和感知系统集成在一起,以补充Soar在无人驾驶汽车方面的优势,并且是少数使用感知系统(例如,占用网格,计算机视觉和模糊逻辑算法)的系统之一。机器人技术的认知架构。在室内搜索任务期间,使用这些感知系统生成有关环境的符号信息,使CRS可以使用Soar的计划和学习机制,代理商很少使用Soar的计划和学习机制来控制实际环境中的移动机器人。另外,为室内搜索任务开发的系统代表了在移动机器人上首次使用具有认知架构的拓扑图。了解拓扑图和生产规则的能力使在室内搜索任务中使用的Soar代理能够明智地做出决定,并在了解周围环境时表现得更加高效。尽管CRS已应用于两个不同的任务,但其开发目的是将来扩展它,因此它可以用作移动机器人控制的通用系统。可以通过添加新的传感器和传感器处理算法,开发具有更多生产规则的Soar代理以及在Soar中使用新的体系结构来扩展CRS。

著录项

  • 作者

    Hanford, Scott D.;

  • 作者单位

    The Pennsylvania State University.;

  • 授予单位 The Pennsylvania State University.;
  • 学科 Engineering Aerospace.;Engineering Robotics.;Artificial Intelligence.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 236 p.
  • 总页数 236
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

  • 入库时间 2022-08-17 11:45:23

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