Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind

He Luo; Zhengzheng Liang; Moning Zhu; Xiaoxuan Hu; Guoqiang Wang

doi:10.1371/journal.pone.0194690

Integrated optimization of unmanned aerial vehicle task allocation and path planning under steady wind

PLoS One. 2018 Mar 21;13(3):e0194690. doi: 10.1371/journal.pone.0194690. eCollection 2018.

Authors

He Luo^{1

2}, Zhengzheng Liang^{1

2}, Moning Zhu^{1

2}, Xiaoxuan Hu^{1

2}, Guoqiang Wang^{1

2}

Affiliations

¹ School of Management, Hefei, Anhui, China.
² Key Laboratory of Process Optimization & Intelligent Decision-making, Ministry of Education, Hefei, Anhui, China.

Abstract

Wind has a significant effect on the control of fixed-wing unmanned aerial vehicles (UAVs), resulting in changes in their ground speed and direction, which has an important influence on the results of integrated optimization of UAV task allocation and path planning. The objective of this integrated optimization problem changes from minimizing flight distance to minimizing flight time. In this study, the Euclidean distance between any two targets is expanded to the Dubins path length, considering the minimum turning radius of fixed-wing UAVs. According to the vector relationship between wind speed, UAV airspeed, and UAV ground speed, a method is proposed to calculate the flight time of UAV between targets. On this basis, a variable-speed Dubins path vehicle routing problem (VS-DP-VRP) model is established with the purpose of minimizing the time required for UAVs to visit all the targets and return to the starting point. By designing a crossover operator and mutation operator, the genetic algorithm is used to solve the model, the results of which show that an effective UAV task allocation and path planning solution under steady wind can be provided.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aircraft* / standards
Algorithms*
Calibration
Remote Sensing Technology* / instrumentation
Remote Sensing Technology* / methods
Remote Sensing Technology* / standards
Robotics* / methods
Robotics* / standards
Systems Integration*
Wind*

Grants and funding

This work was supported by National Natural Science Foundation of China (71671059, 71401048, 71521001, 71690230, 71690235, 71472058) and the URL is http://www.nsfc.gov.cn/, and the Anhui Provincial Natural Science Foundation (1508085MG140) and the URL is http://218.22.27.67/Ahzrjj/index.action. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.