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Cooperation between multiple autonomous vehicles requires inter-vehicle communication, which in many scenarios must be established over an ad-hoc wireless network. This paper proposes an optimization-based approach to the deployment of such mobile robotic networks. A primal-dual gradient descent algorithm jointly optimizes the steady-state positions of the robots based on the specification of a high-level task in the form of a potential field, and routes packets through the network to support the communication rates desired for the application. The motion planning and communication objectives are tightly coupled since the link capacities depend heavily on the relative distances between vehicles. The algorithm decomposes naturally into two components, one for position optimization and one for communication optimization, coupled via a set of Lagrange multipliers. Crucially and in contrast to previous work, our method can rely on on-line evaluation of the channel capacities during deployment instead of a prespecified model. In this case, a randomized sampling scheme along the trajectories allows the robots to implement the algorithm with minimal coordination overhead.