Integrated task assignment and path optimization for cooperating uninhabited aerial vehicles using genetic algorithms

2011 ◽  
Vol 38 (1) ◽  
pp. 340-356 ◽  
Author(s):  
Eugene Edison ◽  
Tal Shima
Keyword(s):  
Genetic Algorithms ◽  
Task Assignment ◽  
Path Optimization ◽  
Aerial Vehicles ◽  
Uninhabited Aerial Vehicles

Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms

2006 ◽  
Vol 33 (11) ◽  
pp. 3252-3269 ◽  
Author(s):  
Tal Shima ◽  
Steven J. Rasmussen ◽  
Andrew G. Sparks ◽  
Kevin M. Passino
Keyword(s):  
Genetic Algorithms ◽  
Aerial Vehicles ◽  
Uninhabited Aerial Vehicles ◽  
Multiple Task ◽  
Task Assignments

Human operator issues for uninhabited aerial vehicles

2001 ◽  
Author(s):  
Jesse Lucas ◽  
Jennie Gallimore ◽  
S. Narayanan
Keyword(s):  
Human Operator ◽  
Aerial Vehicles ◽  
Uninhabited Aerial Vehicles

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.

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