Potential-Field-Based Evolutionary Route Planner for the Control of Multiple Unmanned Aerial Vehicles

Abstract

A novel flight route planning design technique is proposed for a group of unmanned aerial vehicles (UAVs) autonomous navigation based on the combination of evolutionary algorithms and modified artificial potential field methods. In this framework, the individual candidate route is capable of avoiding threats by introducing a virtual potential field while taking into account a variety of flight constraints. The candidate flight route is divided into several sections whose lengths are calculated individually such that the corresponding speed of each UAV can be evaluated to ensure that each member arrives at the desired termination simultaneously. The flight route planner has been tested under a variety of scenarios and proven to be effective in achieving the rendezvous of UAVs.

Keywords

unmanned aerial vehicle path planning evolutionary algorithm potential field obstacle avoidance

Get full access to this article

View all access options for this article.

References

Latombe

J.C.

Robot motion planning , 1991 (Kluwer Academic Publishers, Massachusetts).

Choset

Lynch

Hutchinson

Kanto

Burgard

Kavraki

Thrun

Principles of robot motion: theory, algorithms, and implementations , 2005 (MIT Press, Massachusetts).

LaValle

S.M.

Planning algorithms , 2006 (Cambridge University Press, New York).

Latombe

J.C.

Motion planning: A journey of robots, molecules, digital actors, and other artifacts. Int. J. Robot. Res , 1991, 18(11), 1119–1128.

LaValle

S.M.

Kuffner

J.J.

Rapidly-exploring random trees: progress and prospects. In Proceedings of the Workshop on Algorithmic foundations of robotics, Hanover, New Hampshire, 2000.

Frazzoli

Dahleh

M.A.

Feron

Real-time motion planning for agile autonomous vehicles. AIAA J. Guid. Control Dyn. , 2002, 25(1), 116–129.

Hocaoglu

Sanderson

A.C.

Planning multiple paths with evolutionary speciation. IEEE Trans. Evol. Comput. , 5(3), 169–191.

Rathbun

Capozzi

B.J.

Evolutionary approaches to path planning through uncertain environments. In Proceedings of the AIAA Unmanned Aerospace Vehicles, Systems, Technologies, and Operations Conference and Workshop, Portsmouth, 2002.

Xiao

Michalewicz

Zhang

Trojan-owski

Adaptive evolutionary planner/navigator for mobile robots. IEEE Trans. Evol. Comput. , 1997, 1(1), 18–28.

10.

Sugihara

Smith

Genetic algorithms for adaptive motion planning of an autonomous mobile robot. In Proceedings of the IEEE International Symposium on Computational intelligence in robotics and automation, Monterey, California, 1997, pp. 38–143.

11.

Zheng

Sun

Ding

Evolutionary route planner for unmanned air vehicles. IEEE Trans. Robot. , 2005, 21(4), 609–620.

12.

Lin

C.L.

Lin

J.R.

Jan

H.Y.

Singularity analysis and path planning for a MDOF manipulator. J. Chin. Inst. Eng. , 2007, 30(5), 917–922.

13.

Arkin

R.C.

Motor schema based navigation for mobile robot: an approach to programming by behavior. In Proceedings of the IEEE International Conference on Robotics and automation, Raleigh, 1987, pp. 264–271.

14.

Khatib

Real-time obstacle avoidance for manipulators and mobile robots. Int. J. Robot. Res. , 1986, 5(1), 90–98.

15.

Savage

Marquez

Pettersson

Trygg

Petersson

Wahde

Optimization of waypoint-guided potential field navigation using evolution algorithm. In Proceedings of the IEEE/RSJ International Conference on Intelligent robots and systems, Sendai, 2004, pp. 3463–3468.

16.

Barraquand

Langlois

Latombe

J.C.

Nume-rical potential field techniques for robot path planning. IEEE Trans. Syst. Man Cybern. , 1992, 22(2), 224–241.

17.

Park

M.P.

Jeon

J.H.

Lee

M.C.

Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing. In Proceedings of the IEEE International Symposium on Industrial electronics, Pusan, 2001, pp. 1530–1535.

18.

Vadakkepat

Tan

K.C.

Wang

M.L.

Evolutionary artificial potential fields and their application in real time robot path planning. In Proceedings of the IEEE Conference on Evolutionary computation, California, 2000, pp. 256–263.

19.

Han

Lee

Kim

Unmanned aerial vehicle swarm control using potential functions and sliding mode control. Proc. IMechE, Part G: J. Aerospace Engineering , 2008, 222(6), 721–730. DOI: 10.1243/ 09544100JAERO352.

20.

Lazarus

S.B.

Tsourdos

Silson

White

Zbikowski

Unmanned aerial vehicle navigation and mapping. Proc. IMechE, Part G: J. Aerospace Engineering , 2008, 222(4), 531–548. DOI: 10.1243/09544100JAERO246.

21.

Jiecai

Some new optimal control problems in UAV cooperative control with information flow constraints. In Proceedings of the American Control Conference, Denver, 2003, pp. 2181–2186.

22.

Belanger

Desbiens

Gagnon

UAV guidance with control of arrival time. In Proceedings of the American Control Conference, New York, 2007, pp. 4488–4493.

23.

Hou

Kobierski

R.G.

Performance modeling of agent-aided operator-interface interaction for the control of multiple UAVs. In Proceedings of the IEEE International Conference on Systems, man, cybernetics, Toronto, 2005, pp. 2463–2468.