This paper presents a decentralized control strategy for positioning and orienting multiple robotic cameras to collectively monitor an environment. The cameras may have various degrees of mobility from six degrees of freedom, to one degree of freedom. The control strategy is proven to locally minimize a novel metric representing information loss over the environment. It can accommodate groups of cameras with heterogeneous degrees of mobility (e.g. some that only translate and some that only rotate), and is adaptive to robotic cameras being added or deleted from the group, and to changing environmental conditions. The robotic cameras share information for their controllers over a wireless network using a specially designed networking algorithm. The control strategy is demonstrated in repeated experiments with three flying quadrotor robots indoors, and with five flying quadrotor robots outdoors. Simulation results for more complex scenarios are also presented.

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In this paper we present an information-theoretic approach to distributively control multiple robots equipped with sensors to infer the state of an environment. The robots iteratively estimate the environment state using a sequential Bayesian filter, while continuously moving along the gradient of mutual information to maximize the informativeness of the observations provided by their sensors. The gradient-based controller is proven to be convergent between observations and, in its most general form, locally optimal. However, the computational complexity of the general form is shown to be intractable, and thus non-parametric methods are incorporated to allow the controller to scale with respect to the number of robots. For decentralized operation, both the sequential Bayesian filter and the gradient-based controller use a novel consensus-based algorithm to approximate the robots' joint measurement probabilities, even when the network diameter, the maximum in/out degree, and the number of robots are unknown. The approach is validated in two separate hardware experiments each using five quadrotor flying robots, and scalability is emphasized in simulations using 100 robots.