Three-Dimensional Swarming Using Cyclic Stochastic Optimization

In this article, we simulate an ensemble of cooperating, mobile sensing agents that implement the cyclic stochastic optimization (CSO) algorithm in an attempt to survey, track, and follow multiple targets. In the CSO algorithm proposed, each agent uses its sensed measurements, its shared information, and its predictions of other agents’ future motion to decide on its next action. This decision is selected to minimize a loss function that decreases as the uncertainty in the target state estimates decreases. Only noisy measurements of this loss function are available to each agent, and, in this study, each agent attempts to minimize this function by calculating its gradient. This article examines, via simulation-based experiments, the implications and applicability of CSO convergence in three dimensions.