Research On UAV Path Planning Based On Improved Ant Colony Algorithm

With the development and application of civil UAV,the flight environment becomes more and more complex,and the low altitude airspace becomes more and more narrow.As one of the key technologies of UAV autonomous flight,path planning can significantly improve flight efficiency,ensure flight safety,and effectively improve the capacity of low altitude airspace,which has important research significance and value.In this paper,the static path planning of multi rotor UAV in urban low altitude environment is studied.A path planning strategy based on improved ant colony algorithm is proposed and the corresponding mathematical planning model is established.The effectiveness and superiority of the improved algorithm are verified by MATLAB simulation experiment.In the mathematical model of UAV path planning,the environment space is planned in the form of grid.The flight path can be expressed as multiple continuous line segments connected from point to point.Then,the performance constraints of UAV are analyzed and the comprehensive cost model is constructed.The former includes the minimum step length,the maximum range and the maximum / minimum flight altitude;the latter includes the energy consumption cost model and the threat cost model.According to the weight setting,the comprehensive cost function is established as the evaluation criteria of flight path quality.In the aspect of ant colony algorithm improvement,by introducing the state transition intervention coefficient,the pheromone on the path accumulates rapidly,which speeds up the early search speed of the algorithm;By limiting the amount of pheromones on each path in the search space,and setting up the upper and lower limits,the global search ability is enhanced,while the excessive growth of pheromones on the optimal path is restrained;by introducing the pheromone adjustment factor,the pheromones on the non-optimal path are adjusted according to a certain proportion,on the one hand,the induction effect of the path with more pheromones on ants is weakened,so as to avoid the algorithm In the later stage,the local optimal solution is obtained.On the other hand,it inherits the search experience accumulated by ants before,so that the inducement of the updated paths is relatively balanced.In this paper,based on the basic ant colony algorithm and the improved ant colony algorithm,the two algorithms are simulated in 2D and 3D environment respectively.The simulation results show that the flight path planning based on the improved ant colony algorithm has lower cost,shorter average time-consuming and more average convergence times.In view of the shortcomings of sharp angle and large turning in the path,the non-uniform cubic B-spline interpolation method is used to smooth the UAV flight path.After smoothing,the overall continuity of the flight path is good,the curvature changes continuously,there is no sharp angle locally,and the flight path cost does not change significantly.Through the verification of simulation results and comparative analysis of data,the improved ant colony algorithm has better performance in optimization,which is embodied in: better flight path,faster search speed and excellent global search ability.The flight path based on this algorithm can meet the requirements of UAV’s safety and efficiency after being smoothed.