Multi-robot cooperative surveillance in unknown environments
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.
Multi-robot, Surveillance, Cooperative, Learning, Exploration, Tracking
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