Cooperative Task Assignment/Path Planning of Multiple Unmanned Aerial Vehicles Using Genetic Algorithm

2009 ◽  
Vol 46 (1) ◽  
pp. 338-343 ◽  
Author(s):  
Yeonju Eun ◽  
Hyochoong Bang
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Task Assignment ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles

Target-bundled genetic algorithm for multi-unmanned aerial vehicle cooperative task assignment considering precedence constraints

2019 ◽  
Vol 234 (3) ◽  
pp. 760-773 ◽  
Author(s):  
Guangtong Xu ◽  
Teng Long ◽  
Zhu Wang ◽  
Li Liu
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Task Assignment ◽  
Precedence Constraints ◽  
Task Execution ◽  
Aerial Vehicles ◽  
Multiple Tasks ◽  
Multiple Unmanned Aerial Vehicles ◽  
Multiple Task ◽  
Execution Order

This paper presents a modified genetic algorithm using target-bundle-based encoding and tailored genetic operators to effectively tackle cooperative multiple task assignment problems of heterogeneous unmanned aerial vehicles. In the cooperative multiple task assignment problem, multiple tasks including reconnaissance, attack, and verification have to be sequentially performed on each target (e.g. ground control stations, tanks, etc.) by one or multiple unmanned aerial vehicles. Due to the precedence constraints of different tasks, a singular task-execution order may cause deadlock situations, i.e. one or multiple unmanned aerial vehicles being trapped in infinite waiting loops. To address this problem, a target-bundled genetic algorithm is proposed. As a key element of target-bundled genetic algorithm, target-bundle-based encoding is derived to fix multiple tasks on each target as a target-bundle. And individuals are generated by fixing the task-execution order on each target-bundle subject to task precedence constraints. During the evolution process, bundle-exchange crossover and multi-type mutation operators are customized to generate deadlock-free offspring. Besides, the time coordination method is developed to ensure that task-execution time satisfies task precedence constraints. The comparison results on numerical simulations demonstrate that target-bundled genetic algorithm outperforms particle swarm optimization and random search methods in terms of optimality and efficiency.

scholarly journals A Hierarchical Cooperative Mission Planning Mechanism for Multiple Unmanned Aerial Vehicles

Electronics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 443 ◽  
Author(s):  
Zhe Zhao ◽  
Jian Yang ◽  
Yifeng Niu ◽  
Yu Zhang ◽  
Lincheng Shen
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Search Algorithm ◽  
Pseudospectral Method ◽  
Task Assignment ◽  
Mission Planning ◽  
Planning Problem ◽  
B Spline ◽  
Aerial Vehicles ◽  
Third Stage ◽  
Multiple Unmanned Aerial Vehicles

In this paper, the cooperative multi-task online mission planning for multiple Unmanned Aerial Vehicles (UAVs) is studied. Firstly, the dynamics of unmanned aerial vehicles and the mission planning problem are studied. Secondly, a hierarchical mechanism is proposed to deal with the complex multi-UAV multi-task mission planning problem. In the first stage, the flight paths of UAVs are generated by the Dubins curve and B-spline mixed method, which are defined as “CBC)” curves, where “C” stands for circular arc and “B” stands for B-spline segment. In the second stage, the task assignment problem is solved as multi-base multi-traveling salesman problem, in which the “CBC” flight paths are used to estimate the trajectory costs. In the third stage, the flight trajectories of UAVs are generated by using Gaussian pseudospectral method (GPM). Thirdly, to improve the computational efficiency, the continuous and differential initial trajectories are generated based on the “CBC” flight paths. Finally, numerical simulations are presented to demonstrate the proposed approach, the designed initial solution search algorithm is compared with existing methods. These results indicate that the proposed hierarchical mission planning method can produce satisfactory mission planning results efficiently.

Survey of Cooperative Path Planning for Multiple Unmanned Aerial Vehicles

2014 ◽  
Vol 668-669 ◽  
pp. 388-393 ◽  
Author(s):  
Xiao Ming Cheng ◽  
Dong Cao ◽  
Chun Tao Li
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Cooperative Control ◽  
Control Methods ◽  
Future Time ◽  
Research Directions ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Problem Description ◽  
Multiple Unmanned Aerial Vehicles

As an important part of cooperative control for multiple unmanned aerial vehicles (UAVs), cooperative path planning can get optimal flight path which can satisfy different constraints. Research on cooperative path planning for multiple UAVs is summarized in this paper. Firstly, problem description and constraints are given. Then, solution frameworks and path coordination approaches are summarized. After that, several control methods commonly used in formation of multiple UAVs are introduced respectively. Lastly, possible research directions in the future time are put forward.

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