Dynamic Deployment of Heterogeneous Wireless Sensor Drone Networks With Limited Communication Range

In this paper, we address the deployment of a swarm of heterogeneous drones in a three-dimensional space to maximize the overall sensing performance, defined as the swarm's collective accuracy in sensing the events. We use a realistic 3D sigmoid sensing model to define the sensing accuracy function of drones. The drones must avoid collisions, operate within safe regions by maintaining safe distances, adhere to maneuverability constraints by limiting velocity and acceleration, and ensure connectivity by staying within their communicable ranges. We also guarantee a sparse connected communication network among drones using the lesser neighborhood topology control policy. Our primary objective is to develop a distributed constrained trajectory optimization algorithm for this complex optimization problem. We introduce a novel receding horizon predictive control approach using Bézier curves to model drone trajectories while satisfying constraints. Additionally, we utilize a quadratic programming (QP)-based method in conjunction with a gradient ascent search to solve the constrained optimization problem. We provide analytic proofs to demonstrate the strategy's optimality and convergence. We also validate the proposed approach through extensive simulations on various test cases.