Research On Path Planning Of Multiple UAVS Based On Improved Bat Algorithm

With the development of the intelligent era,UAV has been widely used in modern military and daily life because of its high flexibility and light fuselage.Flight path planning is the core technology in the field of unmanned aerial vehicle(UAV),and also an important prerequisite for UAV to successfully complete flight tasks.The flight path planning of UAV is to plan the optimal flight path of UAV from the starting point to the end point and avoid the obstacles and threats in the flight environment in the known or unknown working space,considering the dynamic performance of UAV itself and constraints such as terrain obstacles.In most flight cases,the environment of UAV is relatively complex,there may be unexpected unknown conditions,and it is subject to more constraints.Therefore,the planned flight path is not only related to the constraint performance,but also depends on the merits and feasibility of the flight path planning algorithm to a certain extent.Therefore,selecting a suitable and efficient algorithm becomes the key to solve the problem of UAV track planning.Aiming at the shortcomings of the traditional bat algorithm,such as premature convergence and easy to fall into the local optimum,this thesis proposes an improvement,and applies it to the multi-UAV three-dimensional mountain environment for track planning simulation analysis and research.The main research content and work of this thesis are as follows:Firstly,an improvement was proposed according to the shortcomings of the traditional bat algorithm.The convergence factor and dynamic decreasing inertia weight were added into the speed updating formula.The monotone decreasing inertia weight could make the bat speed gradually decrease with the gradual increase of the number of iterations,which could reduce the risk of individual bats falling into the local optimal,and improve the ability of the population in the global search.In addition,Levy flight mechanism and speed adjustment factor were incorporated to solve the problem of slow speed in the late iteration,which improved the diversity of the population.Secondly,in order to verify the advantages and disadvantages of the improved bat algorithm,this thesis applies it to the classical test function,and compares it with the other two algorithms.Through software simulation,the optimal value,the worst value,the average value,the variance of each test function and the iterative curve of each test function were generated.At the same time,the data from the test function were processed to produce a box-box graph,and the iterative curve of the three algorithms and the box-box graph were compared and analyzed.Finally,the particle swarm optimization algorithm,the traditional bat algorithm and the improved Bat algorithm were applied to the track planning of multiple UAVs in the complex three-dimensional mountain environment.Mathematical modeling was carried out for the relevant problems in the field of track planning,and the 3D mountain environment was generated by reference terrain model and obstacle modeling.After debugging the important parameters of bat algorithm,find the most appropriate parameters to carry out the flight path planning of UAV,and compare and analyze the flight path,real-time speed and position of the three algorithms,and prove the effectiveness and feasibility of the improved bat algorithm by comparing the simulation results.