New Framework Enables Drone Swarms for Persistent Surveillance with Formal Guarantees

A new research paper by Hossein Rastgoftar introduces a distributed framework for continuous aerial surveillance using multi-unmanned aerial systems (UAS) that operates under formal mission specifications. The approach tackles the challenges of decentralized coordination, limited energy, and communication constraints by partitioning the drone swarm into stationary anchors and mobile workers that cyclically replace each other. A deep neural network (DNN)-inspired communication topology enables fully decentralized coordination through local interactions, while a hierarchical bounded Linear Temporal Logic (LTL) specification formally captures mode-to-mode reference consistency, cyclic team rotation, finite-time reachability, trajectory tracking, and surveillance coverage. The framework proves finite-time convergence of worker-agent coordination dynamics, guaranteeing finite-time satisfaction of the mission specification.

To maximize sensing effectiveness, the framework uses an information-theoretic optimization that synthesizes the reference configuration of newly deployed workers by minimizing the Kullback–Leibler divergence between the surveillance-node distribution and the induced coverage density. This determines a deterministic, mode-dependent communication topology, eliminating the need for online optimization while preserving formal guarantees. A decentralized quadrotor controller realizes the distributed references using only local communication. Numerical simulations demonstrate cyclic team reconfiguration, decentralized communication-topology synthesis, finite-time formation convergence, and certified persistent surveillance coverage. The work has significant implications for defense, disaster response, and large-area monitoring where continuous aerial coverage is critical.