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|Title: ||Towards multi-robot formations : study on vision-based localization system|
|Other Titles: ||Mian xiang duo ji qi ren bian dui de ji yu shi jue de ding wei xi tong yan jiu|
|Authors: ||Chen, Haoyao (陳浩耀)|
|Department: ||Department of Manufacturing Engineering and Engineering Management|
|Degree: ||Doctor of Philosophy|
|Issue Date: ||2009|
|Publisher: ||City University of Hong Kong|
|Subjects: ||Mobile robots.|
|Notes: ||CityU Call Number: TJ211.415 .C45 2009|
ix, 112 leaves : ill. 30 cm.
Thesis (Ph.D.)--City University of Hong Kong, 2009.
Includes bibliographical references (leaves 87-100)
|Abstract: ||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 multirobot
formations. A camera pointing upward to the 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 obtain an optimistic feature match hypothesis quickly and accurately.
Second, three localization strategies are proposed for global localization of a multirobot
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 the 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 multirobot
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.|
|Online Catalog Link: ||http://lib.cityu.edu.hk/record=b3008295|
|Appears in Collections:||MEEM - Doctor of Philosophy |
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