Hostname: page-component-7dd5485656-bvgqh Total loading time: 0 Render date: 2025-10-30T13:39:56.656Z Has data issue: false hasContentIssue false
  • English
  • Français

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
Get access Rights & Permissions [Opens in a new window]

Summary

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

1.Jun, M. and D'Andrea, R., “Path planning for unmanned aerial vehicles in uncertain and adversarial environments,” Cooperative Control: Models, Applications and Algorithms (2002) pp. 95–111.Google Scholar
2.Kim, Y., et al., “Real-time path planning with limited information for autonomous unmanned air vehicles,” Automatica 44, 696–712 (2008).Google Scholar
3.Brian, R. G., et al., “Optimal Path Planning of UAVs Using Direct Collocation with Nonlinear Programming,” American Institute of Aeronautics and Astronautics (2006).Google Scholar
4.Shanmugavel, M., et al., “Co-operative path planning of multiple UAVs using Dubins paths with clothoid arcs,” Control Engineering Practice 18, 1084–1092 (2010).CrossRefGoogle Scholar
5.Zhonghua, H., et al., “Cooperative attack path planning for unmanned air vehicles swarm based on grid model and bi-level programming,” Information & Computational Science 8 (4), 671–679 (2011).Google Scholar
6.Tong, H., “Path planning of UAV based on voronoi diagram and DPSO,” Procedia Engineering 29, 4198–4203 (2012).Google Scholar
7.Qu, H. and Xing, K., “Takacs Alexander, An improved genetic algorithm with co-evolutionary strategy for global path planning of multiple mobile robots,” Neurocomputing 120, 509–517 (Nov. 23, 2013).Google Scholar
8.Melia, E., Malvezzia, M., Papinia, S., Pugia, L., Rinchia, M. and Rindia, A., “A railway vehicle multibody model for real-time applications,” Int. J. Veh. Mech. Mobility 46 (12), 1083–1105 (2008).Google Scholar
9.Falomi, S., Malvezzi, M., Meli, E. and Rindi, A., “Determination of wheel–rail contact points: Comparison between classical and neural network based procedures,” Meccanica 44 (6), 661–686 (Dec. 2009).Google Scholar
10.Babel, L., “Flight path planning for unmanned aerial vehicles with landmark-based visual navigation,” Robot. Auton. Syst. 62 (2), 142–150 (Feb. 2014).CrossRefGoogle Scholar
11.Andreev, M. A., Miller, A. B., Miller, B. M. and Stepanyan, K. V., “Path planning for unmanned aerial vehicle under complicated conditions and hazards,” J. Comput. Syst. Sci. Int. 51 (2), 328–338 (2012).Google Scholar
12.Shiiba, T., Obana, K. and Machida, N., “Comparison of linearized vs. non-linearized multibody vehicle model for real-time simulation,” Int. J. Non-Linear Mech. 53, 32–40 (Jul. 2013).Google Scholar
13.Elfes, A., “Sonar-based real-world mapping and navigation,” IEEE Trans. Robot. Autom. 3, 249–265 (1987).Google Scholar
14.Hespanha, J. P., Kizilocak, H. H. and Ateskan, Y. S., “Probabilistic Map Building for Aircraft-Tracking Radars,” American Control Conference, Arlington, VA (2001) pp. 4381–4386.Google Scholar