Hierarchical coverage scheduling and control over geographically dispersed farmlands with multi‐drone systems

Abstract This paper is concerned about coverage control with multi‐drone systems toward smarter agriculture. In order to address the issue of geographically‐dispersed farmlands that many Asian countries face, we present a hierarchical control architecture with a wide range of roles, from controlling drones and allocating farmlands to scheduling battery charging. The high‐level control, which is responsible for optimally assigning farmlands and charging stations to the drones, is designed based on the concept of model predictive control (MPC). However, due to the fact that the state equation is highly nonlinear and high‐dimensional, the finite‐time optimal control problem to be solved suffers from computational difficulties, becoming untractable sometimes. To address the issue, the present control architecture employs a constraint‐based control as a low‐level controller so that it not only reduces the local cost function but also simplifies the state equations of the high‐level controller, making the computation of the optimal control action by the scheduler easier. Finally, the efficiency of the proposed architecture is demonstrated on a simulation example duplicating real farms.