Route Planning for Unmanned Aircraft Based on Ant Colony Optimization and Voronoi Diagram

Topics: Unmanned aerial vehicle, Ant colony optimization, Ant Pages: 7 (1150 words) Published: April 14, 2013
2011 Second International Conference on Intelligent System Design and Engineering Application 2012 International Conference on Intelligent Systems Design and Engineering Application

Route Planning for Unmanned Aircraft Based on Ant Colony Optimization and Voronoi Diagram Zhou Shudao, Wang Jun, Jin Yongqi
Institute of Meteorology, PLA Univ. of Sci. & Tech., Nanjing Jiangsu 211101, China Abstract: The technology based on ant colony optimization and Voronoi diagram was used to achieve the intelligent route planning for unmanned aircraft. First, the Voronoi weighted direction diagram was created by the threat sources distribution and the threat costs. Then the ant colony optimization was used to find out the best route from all the possible routes. Simulation was carried out to find the best route by the Matlab soft, the results showed the UAV route planning based on Voronoi diagram and ant colony optimization was right and effective. Key words- Ant colony optimization; Voronoi diagram; Unmanned aircraft; Route planning of intelligent



Before the searching of routes, the planning space must be modeled. The Voronoi diagram was used to build the initial planning space, the main idea was to take radar threats as the growing points, making the connections in the growing point of the vertical, the source of the threat encircling polygon was the feasible space characterization of unmanned aerial vehicles [2]

, The edges of Voronoi

diagram were the feasible tracks for unmanned aerial vehicles, track points of intersection were the feasible sets. The two-dimensional coordinates of the known threat source distribution were used to generate the Voronoi diagram which denoted the collection of all optional paths, shown in Figure 1.



Unmanned aircraft route planning is the technology which used to search the best route from the starting point to the target point to ensure the successful completion of the mission, the route planning is based on topographic information and enemy information, considering the performance of unmanned aircraft, arrival time, fuel consumption and other threats and constraints[1]. With the increasing of sophisticated air defense system in modern warfare, a higher technology of route planning is required to avoid threats and complete the task more efficiently. At present, domestic and foreign experts and scholars presented a lot of path planning algorithms, including algorithms based on rough map of planning, planning methods based on evolutionary computation, artificial neural network method[2]. The paper presented a fusion method which combined ant colony algorithm with Voronoi diagram, the Voronoi diagram was used to generate the set of all feasible tracks, then the ant colony optimization algorithm was used to set out the best route that satisfying all the constraints and the optimal minimum cost of flying.

{W1, W2 {S1, S2

W18} represented the threat sources,

S24} represented feasible track nodes. From

the nature function of the Voronoi diagram we know that when the unmanned aerial vehicle flied along the edge of Voronoi diagram, it would suffer the smallest threats, so the problem can be converted to find the edges from the starting point to the destination point in the Voronoi diagram.

978-0-7695-4608-7/12 978-0-7695-4608-7/11 $26.00 © 2011 IEEE 2012 DOI 10.1109/ISdea.2011.191 10.1109/ISdea.2012.568


When the Voronoi diagram was built, it needed to be weighted. To facilitate the discussion, the source threats were reduced to radar threats, assuming that the unmanned aircraft maintained the constant altitude and speed, then the path planning problem will be reduced to a level route optimization problem, so the weight of the Vi edge can be expressed as the sum of radar threats and the fuel consumption, expressed in formula (1-1):

­ [τ ij ( t )]∂ × [η ik ( t )]β ° , ° [τ is ( t )]∂ × [η is...
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