Information sharing based on local PSO for UAVs cooperative search of unmoved targets

Abstract

In this work, a new optimization strategy is proposed for sharing and merging information about unmoved targets' locations in cooperative research of unmanned aerial vehicles (UAVs). The objective is to minimize the search time taking into account the detection and the communication limitations. Taking into account the potential false alarm and the miss detection of the target, we declare, based on sensors' observations during the exploration, either the existence or the absence of a target. The search area is partitioned into cells of equal size, each cell being associated with a target-occurrence probability and the number of hits received by UAVs, which are a probability map (search map) and a map visit (certainty map). Based on the cooperative and competitive particle swarm optimization algorithm, we propose a decentralized control model for goal-seeking relying on the construction of subgroups of cooperative UAVs in real time. Each UAV takes into consideration the possible actions of other UAVs to increase global environmental information. The simulation results illustrate the effectiveness of the proposed strategy compared to previously known ones.