Citation: Liu, H.; Ge, J.; Wang, Y.; Li,
J.; Ding, K.; Zhang, Z.; Guo, Z.; Li, W.;
Lan, J. Multi-UAV Optimal Mission
Assignment and Path Planning for
Disaster Rescue Using Adaptive
Genetic Algorithm and Improved
Artificial Bee Colony Method.
Actuators 2022, 11, 4. https://
doi.org/10.3390/act11010004
Academic Editor: Dario Richiedei
Received: 10 November 2021
Accepted: 15 December 2021
Published: 28 December 2021
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Article
Multi-UAV Optimal Mission Assignment and Path Planning for
Disaster Rescue Using Adaptive Genetic Algorithm and
Improved Artificial Bee Colony Method
Haoting Liu
1,2,
* , Jianyue Ge
1
, Yuan Wang
1
, Jiacheng Li
1
, Kai Ding
3,
*, Zhiqiang Zhang
2
, Zhenhui Guo
4
,
Wei Li
4
and Jinhui Lan
1
1
Beijing Engineering Research Center of Industrial Spectrum Imaging, University of Science and Technology,
Beijing 100083, China; yueyue202011@outlook.com (J.G.); Willing20201@outlook.com (Y.W.);
yyca_2015@sina.com (J.L.); jinhuilan@protonmail.com (J.L.)
2
School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9JT, UK; eenzzh@leeds.ac.uk
3
Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China
4
Jiuquan Satellite Launch Center, Jiuquan 732750, China; gzh9829@sohu.com (Z.G.);
liwei_20@outlook.com (W.L.)
* Correspondence: liuhaoting@ustb.edu.cn (H.L.); winfast113@sina.com (K.D.)
Abstract:
An optimal mission assignment and path planning method of multiple unmanned aerial
vehicles (UAVs) for disaster rescue is proposed. In this application, the UAVs include the drug delivery
UAV, image collection UAV, and communication relay UAV. When implementing the modeling and
simulation, first, three threat sources are built: the weather threat source, transmission tower threat
source, and upland threat source. Second, a cost-revenue function is constructed. The flight distance,
oil consumption, function descriptions of UAV, and threat source factors above are considered. The
analytic hierarchy process (AHP) method is utilized to estimate the weights of cost-revenue function.
Third, an adaptive genetic algorithm (AGA) is designed to solve the mission allocation task. A fitness
function which considers the current and maximum iteration numbers is proposed to improve the
AGA convergence performance. Finally, an optimal path plan between the neighboring mission
points is computed by an improved artificial bee colony (IABC) method. A balanced searching
strategy is developed to modify the IABC computational effect. Extensive simulation experiments
have shown the effectiveness of our method.
Keywords:
multiple UAVs; mission assignment; path planning; genetic algorithm; artificial bee colony;
disaster rescue
1. Introduction
Different from traditional disaster rescue applications [
1
] which utilize 3D terrain
reconstruction or ground target recognition, the multiple unmanned aerial vehicles (UAVs)-
based disaster rescue plan [
2
] utilizes the UAVs with distinct functions to realize people
searching, complete injury identification, and provide medical assistance in the disaster
zones [
3
]. In our past research works, a multi-UAV system is developed; this system
includes [
4
] the drug delivery UAV, image collection UAV, and communication relay UAV.
Compared with the single UAV system, the multiple UAVs can increase the resource
delivery ability, intensify the flight security, and reduce the mission cost. Many factors will
affect the rescue task including the environment factor [
5
] (such as the weather, terrain, and
artificial facilities), UAV performance factor [
6
] (such as the maximum flight distance, oil
consumption, or typical rescue function), and mission complexity itself. Currently, when
implementing the disaster rescue using the multi-UAV-based system, how to assign the
amount and combination mode of UAVs reasonably [
7
,
8
] and how to plan their flight paths
are the issues that should be researched carefully.
Actuators 2022, 11, 4. https://doi.org/10.3390/act11010004 https://www.mdpi.com/journal/actuators
