The Technology Study Of Four-rotor UAV Route Planning

In recent years, with the development of the technology of information, the study of four-rotor route planning has been a hot research field. For example, the mobile robot path planning, path planning of unmanned aerial vehicle (UAV), and so on. Especially, the research of four-rotor aircraft has become a hot direction of the field of the rotor. The path planning of four-rotor is to find out an optimal path from the start to the end under some certain constraint conditions. It is the key to achieve the changes from "flight manual control" to "autonomous flight"of four-rotor aircraft. Aiming at the path planning of four-rotor aircraft, the paper studied in two aspects. Firstly, two common algorithms used in path planning of the four-rotor aircraft,ant colony algorithm (ACO),particle swarm optimization (PSO) algorithm.In the known three-dimensional model, searching out an optimal path and avoiding the obstacles encountered on the way. Secondly, the ant particle swarm fusion algorithm was proposed in this paper. It is simulated in MATLAB. The paper analyzed and compared each algorithm.The main contents are as follows:Firstly, the paper constructed an evaluation function which was based on the constrains of the four-rotor UAV. The evaluation made up of the length of the route and the height of the route node. The coefficient of length and the height shows the importance of the two parts respectively. The results of route planning will be different if the coefficient values is different.Secondly, the paper established a three-dimensional environment model after considering the latitude ,the longitude and the height of the obstacles, GPS positioning and the characteristics of the four-route. The establishment of environment model also used the knowledge of the three-dimensional grid map. The paper studied the path planning methods of the ant colony algorithm and the particle swarm algorithm in three-dimensional environment. According to the problem in the path planning of the ant colony algorithm, the paper proposed connectivity estimation and the method of self-adaption of?a and ?. In the view of the problem in the path planning of the particle swarm algorithm. The paper proposed a discrete binary particle swarm algorithm and a new way of updating the weight for optimizing the path. This paper analyzed several parameters of the two algorithms. The paper counted the results and analyzed of affects of the population and the height coefficient of the ant colony algorithm and the particle swarm algorithm.Finally, according to the respective characteristics of ant colony algorithm and particle swarm optimization (PSO) algorithm, the paper combined the dispersed particle swarm and the improved ant colony algorithm each other to form a fusion algorithm of mixed path. The paper compared the improved ant colony algorithm, the dispersed particle swarm optimization(PSO) algorithm and the fusion algorithm. And then the paper counted and analyzed the results of the route planning form three aspects: stability, error rate and planning time, and then analyzed their respective features and compared the superiority of the algorithm.