Distributed adaptive coverage control with obstacle avoidance for a drone network based on collective initial excitation

Abstract

This paper introduces a distributed adaptive controller for mobile sensor network (MSN) coverage that incorporates collective initial excitation (C-IE) while simultaneously ensuring obstacle avoidance. Agents exchange their parameter estimates with their neighbors to facilitate consensus on the unknown sensory function. They collectively achieve the C-IE condition, which enables parameter convergence and obviates the need for every agent to individually satisfy the excitation condition. This C-IE condition extends the previously established initial excitation (IE) condition to multi-agent systems. The proposed artificial potential-like control law and parameter adaptation guarantee both obstacle avoidance and coverage, along with parameter estimation. Experimental tests with multiple drones validate the effectiveness of the proposed method.