Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding  

作　　者：Zhu WANG Li LIU Teng LONG Yonglu WENa 

机构地区：[1]School of Aerospace Engineering, Beijing Institute of Technology [2]Key Laboratory of Dynamics and Control of Flight Vehicle, Ministry of Education

出　　处：《Chinese Journal of Aeronautics》2018年第2期339-350,共12页中国航空学报（英文版）

基　　金：co-supported by the National Natural Science Foundation of China (Nos. 51675047, 11372036, and 51105040);the Aeronautical Science Foundation of China (No. 2015ZA72004)

摘　　要：This paper presents a novel multiple Unmanned Aerial Vehicles（UAVs） reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs＇ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor＇s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs＇ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem（MDTSP）, where visit paths to the heterogeneous targets must meet specific constraints due to the targets＇ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators（OGA-DEMMO） is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGADEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.This paper presents a novel multiple Unmanned Aerial Vehicles（UAVs） reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs＇ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor＇s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs＇ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem（MDTSP）, where visit paths to the heterogeneous targets must meet specific constraints due to the targets＇ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators（OGA-DEMMO） is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGADEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.

关 键 词：Unmanned aerial vehicles Task allocation Genetic algorithm Travelling salesman problems Dubins vehicles 

分 类 号：E91[军事] E926.3[军事—军事装备学]