Three-Dimensional Swarming Using Cyclic Stochastic Optimization

TL;DR

This paper explores the application of cyclic stochastic optimization for coordinating multiple mobile sensing agents in three-dimensional space to improve target tracking and survey efficiency.

Contribution

It introduces a novel 3D implementation of the CSO algorithm for multi-agent coordination in target tracking tasks.

Findings

CSO converges effectively in 3D simulations

Agents successfully minimize the loss function using stochastic gradients

The approach enhances multi-target tracking accuracy

Abstract

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.