Learning the structure and dynamics of human environments to support intelligent mobile robot behaviour

学习人类环境的结构和动态以支持智能移动机器人的行为

• 批准号: EP/K014293/1
• 负责人: Nick Hawes
• 金额: $ 12.02万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2013
• 资助国家: 英国
• 起止时间: 2013 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FK014293%2F1
• 关键词: Learning; structure; dynamics; human; environments

Advances in mobile robot technology over the last 10 years has made the use of service robots (i.e. robots performing tasks for, or with, humans) in the workplace increasingly possible. The problem of building maps of environments that do not change over time has been solved for many application environments, enabling robots to move around in these places for ever-increasing durations. However, real environments do change over time as their inhabitants move around and move furniture and objects as they do so. The resulting changes to a robot's world make it difficult for it to run reliably, and thus there is a danger that we will not be able to create service robots that are able to perform useful tasks in realistic situations. The proposed research treats the fact that a robot's environment changes regularly as an opportunity rather than a challenge. We will develop systems that are able to extract reliable, significant patterns from the changes observed by an existing intelligent robot (the Dora the Explorer robot from the CogX project). In particular we will develop two approaches. The first will capture how easy or difficult it is for a robot to move through particular parts of its map at particular times of day (e.g. due to humans getting in the way). The second approach will capture how the positions of objects in rooms change over time (e.g. the desk in my office never moves, but my chair tends to move around in front of the desk, but never near the door). Taken together these approaches will allow a robot to improve its performance on typical service robot tasks such as searching for an object in a building, whilst avoiding certain areas at certain times of day (e.g the corridor by a canteen during lunchtime), all in dynamic environments.Our research will be informed by an advisory board of experts in reasoning about space and robot learning, and also by the security company G4S who are interested in using mobile robots to assist security guards. Our results could help them by allowing robots to choose better patrol routes through buildings, and to learn how the objects in a room are typically arranged (allowing them to spot when things change due to a burglary or other incidents). As well as presenting our results through the usual scientific channels, we will also demonstrate our finished robot system at the Thinktank science museum in Birmingham, giving the public a chance to learn more about state-of-the-art robots and AI, whilst also testing our systems in a challenging environment.

在过去十年中，移动机器人技术的进步使得在工作场所使用服务机器人（即为人类执行任务或与人类一起执行任务的机器人）越来越有可能。对于许多应用程序环境，构建不随时间变化的环境地图的问题已经得到了解决，使机器人能够在这些地方移动，持续时间不断增加。然而，真实的环境确实会随着时间的推移而改变，因为它们的居民会四处走动，并移动家具和物体。由此产生的机器人世界的变化使其难以可靠地运行，因此存在一种危险，即我们将无法创造出能够在现实情况下执行有用任务的服务机器人。拟议中的研究将机器人所处的环境定期变化这一事实视为机遇而非挑战。我们将开发能够从现有智能机器人（CogX项目中的Dora the Explorer机器人）观察到的变化中提取可靠、重要模式的系统。我们将特别发展两种方法。第一个将捕获机器人在一天中的特定时间（例如由于人类挡道）通过地图的特定部分的容易程度或困难程度。第二种方法将捕捉房间中物体的位置如何随时间变化（例如，我办公室的桌子从不移动，但我的椅子倾向于在桌子前面移动，但从不靠近门）。综上所述，这些方法将允许机器人在动态环境中提高其典型服务机器人任务的性能，例如在建筑物中搜索物体，同时在一天中的特定时间避开特定区域（例如午餐时间食堂旁边的走廊）。我们的研究将由一个关于空间推理和机器人学习的专家顾问委员会，以及对使用移动机器人协助保安人员感兴趣的安全公司G4S提供信息。我们的研究结果可以帮助机器人在建筑物中选择更好的巡逻路线，并了解房间里的物品通常是如何摆放的（允许它们发现由于入室盗窃或其他事件而导致的物品变化）。除了通过常规的科学渠道展示我们的成果，我们还将在伯明翰的智库科学博物馆展示我们完成的机器人系统，让公众有机会了解更多关于最先进的机器人和人工智能，同时也在具有挑战性的环境中测试我们的系统。

项目成果

Combining top-down spatial reasoning and bottom-up object class recognition for scene understanding

• DOI: 10.1109/iros.2014.6942963
• 发表时间: 2014-09
• 作者: Lars Kunze;Christopher Burbridge;Marina Alberti;Akshaya Thippur;J. Folkesson;P. Jensfelt;Nick Hawes

Bootstrapping Probabilistic Models of Qualitative Spatial Relations for Active Visual Object Search

用于主动视觉对象搜索的定性空间关系的引导概率模型

• 发表时间: 2014
• 作者: Kunze L

On the Notion of Uncontrollable Marking in Supervisory Control of Petri Nets

• DOI: 10.1109/tac.2014.2320801
• 发表时间: 2014-04
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Bruno Lacerda;P. Lima

The Role of Context in Spatial Region Identification

上下文在空间区域识别中的作用

• 发表时间: 2013
• 作者: Lockwood K

Effects of Training Data Variation and Temporal Representation in a QSR-Based Action Prediction System

• 发表时间: 2014-03
• 作者: Jay Young;Nick Hawes