Research On Some Key Techniques Of UAV Path Planning Based On Intelligent Optimization Algorithm

UAV path planning is to plan the flight path and meet the constraints. It is based on the missionand is a key component of UAV mission planning system. This paper is supported by the AviationScience Foundation (2009ZC52041). This paper studies mainly some key issues,include the UAVPath Planning, including UAV static path planning,path planning with pop-up threats,cooperativepath planning for UAVs and path smoothing.To meet the constraints of path planning problem, the paper establishes the UAV dynamicconstraint models and the threat constraint models. The former includes the maximum range, themaximum angle of climb, the minimum turning radius and the smallest step. The latter includesterrain threat, radar threat, missile threats, artillery threat, and atmospheric threat. In addition, takingthe terrain into account to avoid the risk of UAV, so this paper regards height as one of the cost.When the standard ACO algorithm is applied to UAV path planning problem, thetransition-strategy of state makes a choice by the probability based only on inspiration factor andpheromone. It is easy to do a blind selection and it is difficult to quickly reach the target node. In thealgorithm of this paper, the guidance factor is introduced in the state transition strategy. By setting themaximum number of path, it solves the problem that the number of nodes is not fixed and it isdifficult to find the target nodes. Furthermore, random ant subgroup is introduced into the algorithmand it can expand the search space and increase the diversity of solutions to obtain more accuratesolution. The simulation results show that the performance of the improved algorithm is better thanthe original algorithm.In addition, the pop-up threat may appear in the flight process of UAV, it's necessary to generatethe new path quickly to avoid the threat. In order to meet the time limit, the algorithm must bereal-time, high efficiency. According to the characteristics of neighborhood search of Artificial BeeColony algorithm, path segment with sudden threats is considered as the lead path. Neighborhoodsearch is just done at the reference path section with sudden threats by following bee. It will not bedone at other reference path section. Therefore, we can obtain the optimal trajectory segment quickly,and replace the original path segment with Pop-up threat with a new reference trajectory. In thethroughout the flight, UAV determine path segments with the Pop-up threats based on threatinformation and modify the reference trajectory repeatedly until reaches the target node. Simulationresults show that: This Algorithm has more advantages in terms of local path modification than ACO algorithm.To solve the problem of cooperative path planning for UAVs, two-stage planning algorithm isproposed. In this method, cooperative path planning is divided into route planning layer andcooperative planning layer. Among them, the path planning layer, candidate attack node is determinedby setting the attack angle of each UAV. The corresponding candidate optimal path set of is attainedby intelligent optimization algorithm. In cooperative planning layer, coordination functions andcoordination variables of each candidate path are designed. The cooperative solution with minimumcost is determined. Finally, two attack strategies,cooperative converge attacks and cooperative tookturns attacking, are researched separately. Simulation results show that: the global optimumalternation can be attained by the method and it can generate accurate path which meet requirementsof space-time collaboration.UAV mobility constraints are not all taken into accounted during the Initial path planning, so theinitial path can only meet the tactical operations. But for the actual flight, it is often difficult to meetthe maneuverability performance constraints. Therefore, it is necessary to smooth the path and threenon-uniform B-spline curve interpolations are proposed to smooth the path. It will also ensure thatthere are no significant change between the old path and the smooth one. Two-dimensional andthree-dimensional initial paths are simulated. The results show that turning radius of the smoothedpath is greater than the minimum turning radius of the UAV. And the overall transition is natural;heading of the path has no mutations. The smooth path not only passes all the path nodes,but alsoapproaches the original path curve. Therefore, its comprehensive cost has no significant change bycontrasted with the original path.Finally, since weight of indicator is often determined based on average and experience,it issubjective. This paper fully considers the relationships between each index, weights of them aredetermined according to objective method. Deviation maximization method and information entropyare introduced into solve weights. In addition, in optimizing decision-making of multiple solutions,interrelated system characteristics between various factors are further considered. Optimizationdecision-making system with multi-objective and multiple constraints is established. Gray relationalanalysis (GRA) is introduced to construct the optimization model of path planning programs. It isused to path planning problem to avoid the subjectivity and randomness of traditional method byexperience.