{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/16635"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/16635","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"Multi-robot cooperative surveillance in unknown environments","abstract":"This thesis presents a series of distributed multi-robot approaches for practical surveillance in unknown environments. The approaches cover exploration, target searching, target tracking, and localization problems. With respect to exploration and target-searching problems, distributed algorithms such as potential field-based exploration, swarm intelligence exploration, landmark-based exploration, and hop-count gradient-oriented searching, are proposed. With respect to target tracking, an artificial potential field-based intelligent tracking algorithm is proposed to enable the cooperative behavior in tracking mobile targets. In addition, due to the complexity and uncertainty associated with tracking, two reinforcement learning-based algorithms are proposed. With respect to the localization problem, an auction-based task allocation scheme is developed for a robot team to improve the hop-count-based localization. This is a simple and scalable localization technique that can be widely applied to real-world applications. The proposed surveillance algorithms are tested using both simulations and real experiments.","abstract_html":"This thesis presents a series of distributed multi-robot approaches for practical surveillance in unknown environments. The approaches cover exploration, target searching, target tracking, and localization problems. 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With respect to target tracking, an artificial potential field-based intelligent tracking algorithm is proposed to enable the cooperative behavior in tracking mobile targets. In addition, due to the complexity and uncertainty associated with tracking, two reinforcement learning-based algorithms are proposed. With respect to the localization problem, an auction-based task allocation scheme is developed for a robot team to improve the hop-count-based localization. This is a simple and scalable localization technique that can be widely applied to real-world applications. 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