This dissertation presents a novel multi-robot coordination control algorithm for search
and rescue (SAR) operations. Continuous and rapid coverage of the unstructured and
complex disaster areas in search of possible buried survivors is a time critical operation
where prior information about the environment is either not available or very
limited. Human navigation of such areas is definitely dangerous due to the nature
of the debris. Hence, exploration of unknown disaster environments with a team of
robots is gaining importance day by day to increase the efficiency of SAR operations.
Localization of possible survivors necessitates uninterrupted navigation of robotic
aiding devices within the rubbles without getting trapped into dead ends. In this
work, a novel goal oriented prioritized exploration and map merging methodologies
are proposed to generate efficient multi-robot coordination control strategy. These
two methodologies are merged to make the proposed methodology more realistic for
real world applications.
Prioritized exploration of an environment is the first important task of the efficient coordination control algorithm for multi-robots. A goal oriented and prioritized exploration
approach based on a percolation model for victim search operation in unknown
environments is presented in this work. The percolation model is used to describe
the behavior of liquid in random media. In our approach robots start prioritized exploration
beginning from regions of the highest likelihood of finding victims using
percolation model inspired controller.
A novel map merging algorithm is presented to increase the performance of the SAR
operation in the sense of time and energy. The problem of merging partial occupancy
grid environment maps which are extracted independently by individual robot units
during search and rescue (SAR) operations is solved for complex disaster environments.
Moreover, these maps are combined using intensity and area based features
without knowing the initial position and orientation of the robots. The proposed approach
handles the limitation of existing works in the literature such as / limited overlapped
area between partial maps of robots is sufficient for good merging performance
and unstructured partial environment maps can be merged efficiently. These abilities
allow multi-robot teams to efficiently generate the occupancy grid map of catastrophe
areas and localize buried victim in the debris efficiently.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12613841/index.pdf |
| Date | 01 September 2011 |
| Creators | Topal, Sebahattin |
| Contributors | Erkmen, Ismet |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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