Research On UAV Trajectory Control In The Cruising Stage

With the rapid development of aviation science and technology, the applications of UAVs in the field of modern military and civilian are more and more broader. UAVs' routing planning problems are planning the optimal flight trajectory based on mission objectives meeting the conditions of UAVs flight performance constraints and threats constraints, which are the key of UAVs mission planning system and the technical support of achieving automatic fight. The following aspects are studied focusing on UAVs trajectory control in the cruising stage:Firstly, combining the research status and development trends in domestic and abroad,the introduction analyzes the research background and significance of UAVs trajectory planning comprehensively, and defines the composition of the trajectory planning system and the key technologies involved.Secondly, the key premises of UAVs trajectory planning are analyzed and described.Through comparing the advantages and disadvantages of UAVs trajectory planning algorithms which are commonly used, the ant colony algorithm which has better performance of parallel computing and optimization is chosen as the routing optimization algorithm in this topic. To meet the flight performance constraint conditions of UAVs trajectory planning, the constraint models which includes the minimum step, the maximum range, the maximum turn angle, the maximum climb / glide angle, the minimum turn radius and the minimum flight altitude and terrain constraint model are discussed and established respectively. The MAKLINK graph method and grid division method are used to construct the space models of two-dimensional and three-dimensional trajectory planning.Thirdly, the basic mathematical model of ant colony algorithm is analyzed from the aspects of the representation of pheromone, the state transition rule and the update mechanism of pheromone. It also discusses the impact of algorithm parameters on the performance and the parameters selection principles. Though adding the improvement strategies to the traditional ant algorithm, an improved ant colony algorithm is proposed for UAV trajectory planning, the improvement strategies include introducing heuristic probability function,updating pheromone based on wolves allocation principles, introducing the guidance factor and the random subgroup, etc. The results acquired by MATLAB simulation of two-dimensional and three-dimensional trajectory planning show that the trajectory optimization ability and planning accuracy of improved algorithm are improved.Finally, in order to solve the problems that the obtained initial tracks are difficult to meet the flight performance constraints, the three non-uniforms B-spline curves interpolation is adopted to smooth original tracks, according to the basis functions and the curve properties of B-spline. The results show that the obtained smooth tracks of two-dimensional and three-dimensional can avoid threatened regions effectively, and the overall transition is natural.Furthermore, the track costs and its heading are also in the stable range.