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Optimal cooperative path planning of unmanned aerial vehicles by a parallel genetic algorithm

Published online by Cambridge University Press:  24 July 2014

Hamed Shorakaei,
Mojtaba Vahdani,
Babak Imani and
Ali. Gholami
Hamed Shorakaei*
Affiliation:
Department of mechanical engineering, Asadabad Branch, Islamic Azad University, Asadabad, Iran
Mojtaba Vahdani
Affiliation:
Imam Hossein University, Tehran, Iran
Babak Imani
Affiliation:
Department of mechanical engineering, Harsin Branch, Islamic Azad University, Harsin, Iran
Ali. Gholami
Affiliation:
Imam Hossein University, Tehran, Iran
*
*Corresponding author. E-mail: h.shorakaei@basu.ac.ir
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Summary

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The current paper presents a path planning method based on probability maps and uses a new genetic algorithm for a group of UAVs. The probability map consists of cells that display the probability which the UAV will not encounter a hostile threat. The probability map is defined by three events. The obstacles are modeled in the probability map, as well. The cost function is defined such that all cells are surveyed in the path track. The simple formula based on the unique vector is presented to find this cell position. Generally, the cost function is formed by two parts; one part for optimizing the path of each UAV and the other for preventing UAVs from collision. The first part is a combination of safety and length of path and the second part is formed by an exponential function. Then, the optimal paths of each UAV are obtained by the genetic algorithm in a parallel form. According to the dimensions of path planning, genetic encoding has two or three indices. A new genetic operator is introduced to select an appropriate pair of chromosome for crossover operation. The effectiveness of the method is shown by several simulations.

Keywords

Optimal cooperative path planningParallel genetic algorithmProbability mapUnmanned aerial vehicle / UAVCost function

Information

Type
Articles
Information
Robotica , Volume 34 , Issue 4 , April 2016, pp. 823 - 836
Copyright
Copyright © Cambridge University Press 2014 

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