Path Planning And Task Analysis, Simulation And Realization

With the development of computer automation and information technology, modern Unmanned Aerial Vehicle (UAV) technology has been changed a lot. The mission of UAV took on become more and more. According to the need of mission of flight, the requirement of the ability of auto flight has been become higher and higher. Not only request the UAV can finish the mission, but also reduce all of the cost in the ideal range. In order to solve this problem, UAV mission planning occurred. As one important factor of mission planning, path planning has key effect.UAV path planning is to search the optimization track from start to destination; witch should satisfy the limitation of performance of UAV and environment information. It should be the key technology of UAV mission planning system and ensure the automation flight of UAV. This paper, which based on the integration of software system of UAV Ground Control Station (GCS), researches the key problem in the UAV path planning technology.1. In the low-altitude flight, UAV will face extremely complex battlefield environment. In this paper, two different methods of dealing with threat are discussed. We choose the method which makes the threat information equal to an electronic map during the threat processing. Respectively, to detect threats, fire and weather threats to the threat of attack were studied. The role of radar range, probability of detection, as well as anti-air missile and artillery the probability are analyzed. The equivalent terrain models of these types of threats are modeled. By this method can reduce the complexity of route planning effectively.2. In order to monitor the work state of airborne sensors in real-time, that is to say, observe the coverage of the sensor beam in real-time, we study the correlative parameters of synthetic aperture radar and optical sensors. Respectively, the corresponding models are established and complete the simulation and analysis of coverage of the sensors.3. The missions of UAV are analyzed; several common algorithms are studied and establish the models of path planning. Sparse A* algorithm is simulated and analyzed. Than sparse A * algorithm has been improved, mainly on the question of search step length of sparse itself, effectively reducing the overall cost of track and the numbers of path nodes, thereby saving memory space the planning system. This improvement has very important significance in practice.