面向多机器人编队的基于视觉定位系统研究
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摘要
在过去的十年里,移动机器人的群体协作因具有如高容错性,高有效性以及可扩展性等优点得到了广泛的关注。多机器人系统在未知环境下的许多应用具有广泛的前景,如协调操作,建筑测量,以及协作制图等等。在这些应用中,一组机器人以一定编队的形式来完成任务,效果较之单机器人更为优越。
定位是机器人领域的一个关键技术,它主要解决机器人如何获知其在所属环境的位姿,以及机器人之间相对位姿等问题。目前,大部分定位方法都只针对于局部定位,很少人考虑多机器人编队的全局定位问题。为了简单化,这些方法,往往假设机器人具有全局定位能力。本文旨在提出一种新的用于解决未知环境下多机器人编队全局定位问题的方案,在该方案中同时考虑了全局和局部定位。本文的两个主要工作包括定位系统和定位策略。
第一,本文提出了一种可用于多机器人编队系统的有效的基于天花板视觉的定位方案。每个机器人上都装备有朝着天花板看的摄像头,而没有必要使用昂贵且较难标定的传感设备(如激光传感器和全景摄像头等)。目前大部分天花板视觉研究工作都局限于单机器人运用。本文将天花板视觉系统扩展多机器人编队应用中,并使用新的特征检测和数据相关方法。基于匹配的局部定位用于计算机器人之间的相对位姿,而同步定位与制图法(SLAM)用于全局定位。对于数据相关这一SLAM的关键步骤,本文也给出了解决方法。为了平衡鲁棒性和复杂度,一种改良型的JCBB(联合相容性分支界定法)用于快速且准确的获得优化的特征匹配假设
第二,本文提出了三种定位策略用于解决多机器人编队系统的全局定位,以满足不同任务和环境要求。第一种策略只全局定位领航机器人,而其他机器人则通过局部定位获知全局信息,这种方法简单易行,只需要全局定位一个机器人,但是机器人之间得相互靠近,以保证视场重叠;第二种则在每个机器人上使用同步定位与制图法来实现全局定位,这种策略不需要多个机器人足够靠近,但因为每个机器人都有自身的定位坐标系,所以需要将所有机器人的定位结果归整到一个世界坐标系下;第三种使用公用的SLAM服务器(可装在某一台机器人上)来实现机器人的全局同步定位,并共享同一幅全局地图。这种方法每个机器人的硬件要求较低,但定位服务器的硬件要求非常高。策略的选择取决于实际环境状况和任务要求。最后,一组移动机器人的实验结果表明了所有这三种策略都具有很好的定位性能。
本文提出了一种基于视觉的方案,用于解决室内多机器人编队的定位问题。这种定位方法不仅能用于多机器人编队中,而且在其他一些多智能体任务中也具有很好的应用前景。本文研究成果能有效的应用于室内环境下(如医院,机场,仓库和购物大厅等等)的机器人自动化,从而帮助人们从繁重的体力劳动中解脱出来。
Over the past decade,cooperation of swarm of mobile robots has been widely studied for high error tolerance,efficiency,and scalar extendibility.A multi-robot system has exhibited advantages for various services and surveillances in an unknown environment.Examples include cooperative manipulation,building measurement,and cooperative mapping.In many of these applications,the robots in a team are controlled as a whole to follow required formations to accomplish tasks with satisfied overall performance.
Localization is a key technology to address how the robots localize themselves in the operating environment and how they obtain their individual poses with respect to the team.Most of the present approaches put the focus on the local localization only, and few of them discuss how to globally localize the robots in the multi-robot formations.For simplicity,many works just assume that the robots employ absolute positioning capability.This thesis study aims to develop a new framework for global localization of a multi-robot formation system in an unknown indoor environment. Both global and local localizations are addressed in this research.Two main works reported in this thesis are localization system and localization strategies.
First,an efficient ceiling vision based localization framework is developed for multi-robot formations.A camera pointing upward to ceiling is installed on each robot such that there is no need to use expensive and hardly calibrated sensing equipment (i.e.,laser sensors and omni-directional cameras).The majority of existing works using the ceiling vision have concentrated on the single robot applications only.This thesis makes an extension of applying the ceiling vision system to multi-robot formations,using newly developed feature detection and data association technologies. Match-based local localization is used to calculate the relative poses amongst the robots,and simultaneous localization and mapping(SLAM) is utilized for global localization.Data association,a key issue of SLAM process,is well considered in this study.For balance of robustness and implementation simplicity,a modified JCBB (Joint Compatibility Branch and Bound) is developed to be used to obtain an optimistic feature match hypothesis quickly and accurately.
Second,three localization strategies are proposed for global localization of a multi-robot formation system,to meet different task and environment requirements. The first strategy is to globally localize one robot only(i.e.,leader) and then localize the others based on relative poses amongst the robots.This strategy is relatively easy to implement since only one robot needs to be localized globally.The robots must work physically close enough such that they have overlapped ceiling observations for local localization.The second strategy is that each robot localizes itself individually by implementing SLAM.Since all the robots are facilitated to process SLAM independently,they are not required to work close physically.With this strategy,each robot has its own localization frame,and it is necessary to unify localization results of all the robots in the common world frame.The third strategy is to utilize a common SLAM server,which may be installed on one robot,to globally localize all the robots simultaneously,based on a shared global map.This strategy does not require every robot to have SALM processing capability,but has relative high demand for the common SLAM server to accomplish the complex task.Selection of strategy will be based on actual environmental situation and task requirement.Experiments are finally performed on a group of mobile robots to show that all the three strategies exhibit good localization performance.
This research provides a vision-based solution for the localization of indoor multi-robot formations.The proposed visual localization methodologies have potential prospects not only in multi-robot formations,but also in other general multi-agent tasks.The research outputs will eventually benefit the automatic applications in indoor environment,such as hospital,airport,depository and shopping mall,and release people from laborious works.
定位是机器人领域的一个关键技术,它主要解决机器人如何获知其在所属环境的位姿,以及机器人之间相对位姿等问题。目前,大部分定位方法都只针对于局部定位,很少人考虑多机器人编队的全局定位问题。为了简单化,这些方法,往往假设机器人具有全局定位能力。本文旨在提出一种新的用于解决未知环境下多机器人编队全局定位问题的方案,在该方案中同时考虑了全局和局部定位。本文的两个主要工作包括定位系统和定位策略。
第一,本文提出了一种可用于多机器人编队系统的有效的基于天花板视觉的定位方案。每个机器人上都装备有朝着天花板看的摄像头,而没有必要使用昂贵且较难标定的传感设备(如激光传感器和全景摄像头等)。目前大部分天花板视觉研究工作都局限于单机器人运用。本文将天花板视觉系统扩展多机器人编队应用中,并使用新的特征检测和数据相关方法。基于匹配的局部定位用于计算机器人之间的相对位姿,而同步定位与制图法(SLAM)用于全局定位。对于数据相关这一SLAM的关键步骤,本文也给出了解决方法。为了平衡鲁棒性和复杂度,一种改良型的JCBB(联合相容性分支界定法)用于快速且准确的获得优化的特征匹配假设
第二,本文提出了三种定位策略用于解决多机器人编队系统的全局定位,以满足不同任务和环境要求。第一种策略只全局定位领航机器人,而其他机器人则通过局部定位获知全局信息,这种方法简单易行,只需要全局定位一个机器人,但是机器人之间得相互靠近,以保证视场重叠;第二种则在每个机器人上使用同步定位与制图法来实现全局定位,这种策略不需要多个机器人足够靠近,但因为每个机器人都有自身的定位坐标系,所以需要将所有机器人的定位结果归整到一个世界坐标系下;第三种使用公用的SLAM服务器(可装在某一台机器人上)来实现机器人的全局同步定位,并共享同一幅全局地图。这种方法每个机器人的硬件要求较低,但定位服务器的硬件要求非常高。策略的选择取决于实际环境状况和任务要求。最后,一组移动机器人的实验结果表明了所有这三种策略都具有很好的定位性能。
本文提出了一种基于视觉的方案,用于解决室内多机器人编队的定位问题。这种定位方法不仅能用于多机器人编队中,而且在其他一些多智能体任务中也具有很好的应用前景。本文研究成果能有效的应用于室内环境下(如医院,机场,仓库和购物大厅等等)的机器人自动化,从而帮助人们从繁重的体力劳动中解脱出来。
Over the past decade,cooperation of swarm of mobile robots has been widely studied for high error tolerance,efficiency,and scalar extendibility.A multi-robot system has exhibited advantages for various services and surveillances in an unknown environment.Examples include cooperative manipulation,building measurement,and cooperative mapping.In many of these applications,the robots in a team are controlled as a whole to follow required formations to accomplish tasks with satisfied overall performance.
Localization is a key technology to address how the robots localize themselves in the operating environment and how they obtain their individual poses with respect to the team.Most of the present approaches put the focus on the local localization only, and few of them discuss how to globally localize the robots in the multi-robot formations.For simplicity,many works just assume that the robots employ absolute positioning capability.This thesis study aims to develop a new framework for global localization of a multi-robot formation system in an unknown indoor environment. Both global and local localizations are addressed in this research.Two main works reported in this thesis are localization system and localization strategies.
First,an efficient ceiling vision based localization framework is developed for multi-robot formations.A camera pointing upward to ceiling is installed on each robot such that there is no need to use expensive and hardly calibrated sensing equipment (i.e.,laser sensors and omni-directional cameras).The majority of existing works using the ceiling vision have concentrated on the single robot applications only.This thesis makes an extension of applying the ceiling vision system to multi-robot formations,using newly developed feature detection and data association technologies. Match-based local localization is used to calculate the relative poses amongst the robots,and simultaneous localization and mapping(SLAM) is utilized for global localization.Data association,a key issue of SLAM process,is well considered in this study.For balance of robustness and implementation simplicity,a modified JCBB (Joint Compatibility Branch and Bound) is developed to be used to obtain an optimistic feature match hypothesis quickly and accurately.
Second,three localization strategies are proposed for global localization of a multi-robot formation system,to meet different task and environment requirements. The first strategy is to globally localize one robot only(i.e.,leader) and then localize the others based on relative poses amongst the robots.This strategy is relatively easy to implement since only one robot needs to be localized globally.The robots must work physically close enough such that they have overlapped ceiling observations for local localization.The second strategy is that each robot localizes itself individually by implementing SLAM.Since all the robots are facilitated to process SLAM independently,they are not required to work close physically.With this strategy,each robot has its own localization frame,and it is necessary to unify localization results of all the robots in the common world frame.The third strategy is to utilize a common SLAM server,which may be installed on one robot,to globally localize all the robots simultaneously,based on a shared global map.This strategy does not require every robot to have SALM processing capability,but has relative high demand for the common SLAM server to accomplish the complex task.Selection of strategy will be based on actual environmental situation and task requirement.Experiments are finally performed on a group of mobile robots to show that all the three strategies exhibit good localization performance.
This research provides a vision-based solution for the localization of indoor multi-robot formations.The proposed visual localization methodologies have potential prospects not only in multi-robot formations,but also in other general multi-agent tasks.The research outputs will eventually benefit the automatic applications in indoor environment,such as hospital,airport,depository and shopping mall,and release people from laborious works.
引文
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