DocumentCode :
3485542
Title :
UAVs task allocation using multiple colonies of ants
Author :
Zhenhua, Wang ; Weiguo, Zhang ; Guangwen, Li
Author_Institution :
Coll. of Autom., Northwestern Polytech. Univ., Xi´´an, China
fYear :
2009
fDate :
5-7 Aug. 2009
Firstpage :
371
Lastpage :
374
Abstract :
The probabilistic roadmap method (PRM) has been successfully implemented in the motion planning field. But, when the sampled points are being connected to each other, collision check is an inevitable step, which is the most time-consuming operation of this method. A new method is proposed, in which the collision check operation is not necessary, and speed up the path planning operation. Based on the paths planed using this method, with the objective of minimizing the time that the UAVs used to complete all the tasks, an optimization method that uses multiple colonies of ants working together to solve the task allocation problem is designed. The simulation results show that it outperforms the genetic algorithm.
Keywords :
aerospace robotics; aircraft control; military aircraft; mobile robots; motion control; optimisation; path planning; probability; remotely operated vehicles; ant colony; motion planning field; optimization method; path planning operation; probabilistic roadmap method; task allocation; unmanned aerial vehicles; Ant colony optimization; Automation; Educational institutions; Genetic algorithms; Logistics; Path planning; Road accidents; Sampling methods; Testing; Unmanned aerial vehicles; Multiple Ant Colonies; Probabilistic Roadmap Method; path planning; task assignment;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automation and Logistics, 2009. ICAL '09. IEEE International Conference on
Conference_Location :
Shenyang
Print_ISBN :
978-1-4244-4794-7
Electronic_ISBN :
978-1-4244-4795-4
Type :
conf
DOI :
10.1109/ICAL.2009.5262895
Filename :
5262895
Link To Document :
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