Reconnaissance Mission Conducted by UAV Swarms Based on Distributed PSO Path Planning Algorithms

Yubing Wang,Jiaqiang Zhang,Qixi Fu,Weijia Wang,Peng Bai,Xiaolong Liang

doi:10.1109/access.2019.2932008

Abstract

Reconnaissance mission has a wide application in both civil and military fields, which provides intelligence and basis for the following decision-making to accomplish certain goals. Due to numerous advantages of UAV swarms such as strong flexibility, high efficiency, and low cost, conducting reconnaissance missions by UAV swarms has become a trend of future. However, the path planning problem of UAV swarms is a key challenge in the aspect of model construction, algorithm, selection and high computational complexity, especially when the mission is complicated. In this paper, various distributed particle swarm optimization (DPSO)-based path planning algorithms are proposed for UAV swarms conducting a reconnaissance mission, in which targets are gathered in the form of clusters and different tactic needs are taken into consideration. Three algorithms named the maximum density convergence DPSO algorithm (MDC-DPSO), the fast cross-over DPSO algorithm (FCO-DPSO), and the accurate coverage exploration DPSO algorithm (ACE-DPSO) are proposed correspond to the needs of fast convergence, random cross-over, and accurate search, respectively. Different fitness functions and search strategies are specifically designed considering the mobility and communication constraints of the UAV swarms. Besides, the jump-out mechanism and revisit mechanism are designed to save invalid search efforts and avoid falling into local optimum. The simulation results demonstrate that the proposed algorithms are effective in generating paths for UAV swarms conducting a reconnaissance mission, which can be easily applied to large scale swarms.

Highlights

The development of UAV swarms technology has drawn great attention in both civil and military applications, such as aerial mapping for terrain mapping [1], disaster search and rescue [2], surveillance and reconnaissance mission [3], [4]
Inspired by [17]–[21], we focus on a distributed particle swarm optimization (DPSO)-based algorithm for a reconnaissance mission in a given hostile region which contains several unknown target clusters, accomplished by the UAV swarms with optical sensors and limited communication ranges
Three DPSO based algorithms are proposed for path planning of UAV swarms conducting reconnaissance mission, where targets appear in the form of clusters

Summary

INTRODUCTION

The development of UAV swarms technology has drawn great attention in both civil and military applications, such as aerial mapping for terrain mapping [1], disaster search and rescue [2], surveillance and reconnaissance mission [3], [4]. Y. Wang et al.: Reconnaissance Mission Conducted by UAV Swarms Based on Distributed PSO Path Planning Algorithms each moment. High time complexity within each time step is the main drawback of these bio-inspired approaches and cannot settle down at a predictable solution To avoid this problem, Hereford et al [17], [18] firstly gave the distributed PSO (DPSO) algorithm to perform a search task of robots. Spanogianopoulos [20] reviewed the various applications that use the PSO based algorithm It shows that DPSO algorithm can be applied efficiently in multi-agents path planning algorithm such as robotics. Three DPSO based algorithms are proposed for path planning of UAV swarms conducting reconnaissance mission, where targets appear in the form of clusters.

DPSO ALGORITHM AND OUR WORK

2: Select initial directions of velocity for N UAVs

FOR TWO OR MULTIPLE UAVS’ EXPLORATION

Findings

VIII. CONCLUSION