Multi-robot coordination control methodology for search and rescue operations

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Tezin Türü: Doktora

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2011

Öğrenci: SEBAHATTİN TOPAL

Danışman: İSMET ERKMEN

Özet:

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.