Research On UAV Path Planning Based On Swarm Intelligence Optimization

Unmanned aerial vehicle(UAV)technology has made great progress in both military and civilian fields,and its application is becoming more and more extensive.At present,the aerodynamic structure design,flight power system and attitude stability control of UAV have achieved an important breakthrough,and the precise flying technology of UAV is effective.Nowadays,the key problem that limits the development of UAV is the autonomous control of UAV.In this case,the commander only needs to give his own intention,and the UAV can independently sense the surrounding environment and complete the assigned tasks.One of the key technologies of UAV autonomous control is path planning,which is to design and calculate an optimal flight path from the starting point to the end point.The path needs to meet the constraints of the external environment conditions and the self-maneuverability of UAV in order to ensure that the UAV can complete the ordered mission safely and efficiently.The path planning of UAV is an optimization problem with complex objective function and high difficulty.It has strong non-linearity and non-convexity,and the traditional optimization algorithm is very difficult to deal with.Swarm Intelligent optimization algorithms do not require the mathematical form of the optimization problems,even though there is only a functional mapping relation between independent variables and dependent variables.Therefore,it is very suitable for solving this kind of complex optimization problems.Based on the development of UAV path planning,more effective path planning methods are put forward in this paper.Firstly,the research status of UAV path planning is analyzed,and the key problems of path planning are explained.And the classical swarm intelligence algorithms are deeply analyzed and explained,which lays a theoretical foundation for the solution of the path planning problems.Secondly,two improved algorithms are proposed aiming at some shortcomings of swarm intelligence algorithms: the improved particle swarm optimization algorithm based on T-distribution random number and the improved cuttlefish algorithm based on a mixture of the particle swarm optimization and the cuttlefish algorithm.Some benchmark functions are applied to test the optimization performance of the proposed algorithms.Thirdly,the improved algorithms are applied to the solution of path planning problem on a probabilistic map,and the simulation results show that the improved algorithms have excellent performance.Finally,a multi-UAV cooperative path planning method based on the improved cuttlefish algorithm is proposed,which widens the application scope of UAV.