This thesis proposes a unified approach for controlling a group of robots to reach a goal configuration in a decentralized fashion. As a motivating example, robots are controlled to spread out over an environment to provide sensor coverage. This example gives rise to a cost function that is shown to be of a surprisingly general nature. By changing a single free parameter, the cost function captures a variety of different multi-robot objectives which were previously seen as unrelated. Stable, distributed controllers are generated by taking the gradient of this cost function. Two fundamental classes of multi-robot behaviors are delineated based on the convexity of the underlying cost function. Convex cost functions lead to consensus (all robots move to the same position), while any other behavior requires a nonconvex cost function. The multi-robot controllers are then augmented with a stable on-line learning mechanism to adapt to unknown features in the environment. In a sensor coverage application, this allows robots to learn where in the environment they are most needed, and to aggregate in those areas. The learning mechanism uses communication between neighboring robots to enable distributed learning over the multi-robot system in a provably convergent way. Three multi-robot controllers are then implemented on three different robot platforms. Firstly, a controller for deploying robots in an environment to provide sensor coverage is implemented on a group of 16 mobile robots.
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