Abstract
With the continuing increase in use of UAVs (Unmanned Aerial Vehicles) in various applications, much effort is directed towards creating fully autonomous UAV systems to handle tasks independently of human operators. One such task is the monitoring of an area, e.g. by deploying sensors in this area utilizing a system of multiple UAVs to autonomously create an efficient dynamic WSN (Wireless Sensor Network). The locations, order and which UAV to deal with deployment of individual sensors is a complex problem which in any real life problem is deemed to be hard to solve using brute force methods. A method is proposed for multi-UAV cooperative path planning by allocation of sensor placement tasks between UAVs, using a cooperative coevolving genetic algorithm as a basis for the solution to the described challenge. Algorithms have been implemented and preliminary tested in order to show proof of concept.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Antonio, L.M., Coello, C.A.C.: Use of cooperative coevolution for solving large scale multiobjective optimization problems. In: IEEE Congress on Evolutionary Computation, CEC 2013, vol. 2, pp. 2758–2765 (2013)
Besada-Portas, E., De La Torre, L., De La Cruz, J.M., De Andrés-Toro, B.: Evolutionary trajectory planner for multiple UAVs in realistic scenarios. IEEE Trans. Robot. 26(4), 619–634 (2010)
Machado, P., Tavares, J., Pereira, F., Costa, E.: Vehicle routing problem: doing it the evolutionary way. In: Proceedings of the Genetic and Evolutionary Computation Conference (January), p. 690 (2002)
Meng, B.B., Gao, X., Wang, Y.: Multi-mission path re-planning for multiple unmanned aerial vehicles based on unexpected events. In: International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2009, vol. 1, pp. 423–426 (2009)
David, O.E., van den Herik, H.J., Koppel, M., Netanyahu, N.S.: Genetic algorithms for evolving computer chess programs. ACM Comput. Surv. 18(5), 779–789 (2014)
Peng, Z.H., Wu, J.P., Chen, J.: Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm. J. Central South Univ. Technol. 18(5), 1502 (2011). doi:10.1007/s11771-011-0866-4
Potter, M.A., Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994). doi:10.1007/3-540-58484-6_269
Roberge, V., Tarbouchi, M., Labonte, G.: Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Trans. Industr. Inform. 9(1), 132–141 (2013). http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6198334
Shanmugavel, M., Tsourdos, A., White, B.A.: Collision avoidance and path planning of multiple UAVs using flyable paths in 3D. In: 15th International Conference on Methods and Models in Automation and Robotics, MMAR 2010, pp. 218–222 (2010)
Sim, L., Dias, D., Pacheco, M.: Refinery scheduling optimization using genetic algorithms and cooperative coevolution. In: IEEE Symposium on Computational Intelligence in Scheduling, pp. 151–158 (2007)
Takahama, T., Sakai, S.: Structural learning of neural networks by coevolutionary genetic algorithm with degeneration. In: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, vol. 4(3), pp. 3507–3512 (2004)
Wang, X., Yang, S.X., Shi, W., Meng, M.H.: A co-evolution approach to sensor placement and control design for robot obstacle avoidance. In: International Conference on Information Acquisition, pp. 107–112 (2004)
Yu, X., Gen, M.: Introduction to Evolutionary Algorithms. Springer, London (2010). http://www.springer.com/gp/book/9781849961288
Zheng, C., Ding, M., Zhou, C., Li, L.: Coevolving and cooperating path planner for multiple unmanned air vehicles. Eng. Appl. Artif. Intell. 17(8), 887–896 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Sørli, JV., Graven, O.H., Bjerknes, J.D. (2017). Multi-UAV Cooperative Path Planning for Sensor Placement Using Cooperative Coevolving Genetic Strategy. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-61833-3_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61832-6
Online ISBN: 978-3-319-61833-3
eBook Packages: Computer ScienceComputer Science (R0)