TY - GEN
T1 - Task allocation of multiple UAVs and targets using improved genetic algorithm
AU - Zuo, Yong
AU - Peng, Zhihong
AU - Liu, Xin
PY - 2011
Y1 - 2011
N2 - In this paper, task allocation of multi-Unmanned Aerial Vehicles (UAVs) is studied, that is, multi-UAVs from different bases should be allocated to attack multiple targets. Based on the existing task allocation model, which just take the values of targets, UAVs and weapons into account, the fuel consumption is added into consideration to make the model much more practical. An improved genetic algorithm is proposed for such a multi-UAVs multi-targets task allocation. Simulation results show that the algorithm is significantly effective and the allocation result is reasonable.
AB - In this paper, task allocation of multi-Unmanned Aerial Vehicles (UAVs) is studied, that is, multi-UAVs from different bases should be allocated to attack multiple targets. Based on the existing task allocation model, which just take the values of targets, UAVs and weapons into account, the fuel consumption is added into consideration to make the model much more practical. An improved genetic algorithm is proposed for such a multi-UAVs multi-targets task allocation. Simulation results show that the algorithm is significantly effective and the allocation result is reasonable.
KW - Genetic Algorithm
KW - Task Allocation
KW - UAV
UR - https://www.scopus.com/pages/publications/80053182841
U2 - 10.1109/ICICIP.2011.6008408
DO - 10.1109/ICICIP.2011.6008408
M3 - Conference contribution
AN - SCOPUS:80053182841
SN - 9781457708145
T3 - Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011
SP - 1030
EP - 1034
BT - Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011
T2 - 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011
Y2 - 25 July 2011 through 28 July 2011
ER -