Research And Realization Of Low-altitude Uav Path Planning

UAV(Unmanned Aerial Vehicle) path planning is to plan the best flight path to meet the constraints based on the mission. It is a key component of UAV’s mission planning system and an important link to realize UAV’s intelligent fighting. UAV path planning is mainly studied in this thesis, for the purpose of improving UAV’s capability of survival and mission.In the modeling of path planning, three works are conducted in this thesis. Firstly, a grid space whose resolution is 300 meter is established by using the ASTER GDEM v2 data; Secondly, the UAV dynamic constraint models including the maximum range, the maximum pitching angle, the maximum yawing angle, the smallest step and the minimum flight altitude is established; Finally, radar threat, missile threat and antiaircraft gun threat are analyzed, and their threat indicator functions are established.In the expression of flight path cost, a cost function that synthesizes the path’s length price, altitude price and threat price is proposed. In addition, a determination method of path weighting factors is also addressed based on fuzzy inference.In the research of off-line path planning, a path planner based on the sparse A* algorithm is presented. By incorporating constraints into the track points’ extending, this approach can efficiently prune the search space. After considering the physical meaning of track points’ extending, a new approach to extend the track points is also proposed in this thesis.In the research of on-line path planning, a new planner based on the Wolf Pack Search Algorithm is proposed. The planner uses penalty function to guarantee the path can meet all the restrains, and the approach’s high execution efficiency can meet the real-time demand of on-line path planning.Many simulation experiments were carried out. Results demonstrate that the proposed method was correct and reliable.