Three-Dimensional Swarming Using Cyclic Stochastic Optimization
This work addresses the challenge of multi-agent target tracking in 3D environments, but it appears incremental as it extends an existing CSO method to three dimensions without major innovations.
The paper tackled the problem of coordinating mobile sensing agents to survey and track multiple targets in 3D using cyclic stochastic optimization (CSO), resulting in simulation-based experiments that demonstrate the algorithm's convergence and applicability in three-dimensional swarming scenarios.
In this paper we simulate an ensemble of cooperating, mobile sensing agents that implement the cyclic stochastic optimization (CSO) algorithm in an attempt to survey and track multiple targets. In the CSO algorithm proposed, each agent uses its sensed measurements, its shared information, and its predictions of others' future motion to decide on its next action. This decision is selected to minimize a loss function that decreases as the uncertainty in the targets' 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 stochastic gradient. This paper examines, via simulation-based experiments, the implications and applicability of CSO convergence in three dimensions.