The Performance Research On Local Algorithm

When unmanned aerial vehicle is flying along the pre-planned flight path, it always got some reason to make the current flight status and the scheduled inconsistent, and it is common to increasing or changing the task because of the uncertainty of the task. In order to make the UAV have the ability to adapt the changing of battlefield environment, establishing the local real-time route planning system is necessary. This system will plan the adjust route, so that UAV can fly with the pre-planned path continually. This paper is mainly to choose suitable algorithm for the local real-time route planning system, to make the process of partial adjustment meeting rapidity and stability requirements.At the first,we were introduced the “super error ”and analyzed the occurred reason of the “super error ”. Then according to the basic request of the local adjustment algorithm, choose several route planning algorithms: parse A* algorithm, colony algorithm and genetic algorithm. Programming these algorithms and doing the comparison experiment between the three kinds of the algorithms. The experimental results show that the sparse A* algorithm and genetic algorithm meet the requirements of fast planning, and the colony algorithm can not meet the requirement. But for the special conditions of the “super error ”,the three kinds of algorithms are difficult to plan out a satisfactory result, the success rate of proposed algorithms is not high. So in the end, based on the analysis of the reasons of the planning failure, we have proposed an improved scheme of the genetic algorithm. The experimental results show that the improved algorithm with orient circle can be a very good solution to that kinds of the situations, and the planning time also meet the requirements of the speed. So the improved algorithm with orient-circle can be used to the local real-time route planning system.