This paper presents an investigation of odor localization by groups of autonomous mobile robots using principles of Swarm Intelligence. First, we describe a distributed algorithm by which groups of agents can solve the full odor localization task more efficiently than a single agent. Next, we demonstrate that a group of real robots under fully distributed control can successfully traverse a real odor plume, and that an embodied simulator can faithfully reproduce these real robots experiments. Finally, we use the embodied simulator combined with a reinforcement learning algorithm to optimize performance across group size, showing that it can be useful not only for improving real world odor localization, but also for quantitatively characterizing the influence of group size on task performance.

This thesis presents a methodology for designing self-organized autonomous robotic systems and demonstrates how this process can be applied to the problem of finding the source of an airborne odor plume. The design methodology is applicable to other task domains and the resulting odor localization system extends the state of the art. The design procedure centers on the ability to define a specific task performance metric, systematically evaluate performance in a realistic environment, and define abstract relationships between system parameters and system performance. Once such relationships have been experimentally validated in a test environment, they can be used to guide the design of a deployable system. Because this process relies heavily on evaluative feedback, this work emphasizes the development of tools that allow the collection of accurate performance data. It presents a reliable multiple robot test-bed and some task-enabling sensory hardware, as well as validation of the sensory and kinematic models used in simulation. Also, a reinforcement learning methodology is described that provides consistent optimization performance while minimizing the amount of required evaluation.

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