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This paper focuses on the problem of multi-robot source-seeking, where a group of mobile sensors localizes and moves close to a single source using only local measurements. Drawing inspiration from the optimal sensor placement research, we develop an algorithm that estimates the source location while approaches the source following gradient descent steps on a loss function defined on the Fisher information. We show that exploiting Fisher information gives a higher chance of obtaining an accurate source location estimate and naturally leads the sensors to the source. Our numerical experiments demonstrate the advantages of our algorithm, including faster convergence to the source than other algorithms, flexibility in the choice of the loss function, and robustness to measurement modeling errors. Moreover, the performance improves as the number of sensors increases, showing the advantage of using multi-robots in our source-seeking algorithm. We also implement physical experiments to test the algorithm on small ground vehicles with light sensors, demonstrating success in seeking a moving light source. 

We consider a class of multi-agent optimization problems, where each agent is associated with an action vector and a local cost that depends on the joint actions of all agents, and the goal is to minimize the average of the local costs. Such problems arise in many control applications such as wind farm operation and mobile sensor coverage. In many of these applications, while we have access to (zeroth-order) information about function values, it can be difficult to obtain (first-order) gradient information. In this paper, we propose a zeroth-order feedback optimization (ZFO) algorithm based on two-point gradient estimators for the considered class of problems, and provide the convergence rate to a first-order stationary point for nonconvex problems. We complement our theoretical analysis with numerical simulations on a wind farm power maximization problem.