A UAV route planning method based on improved bat algorithm

Abstract

A UAV route planning method based on the improved bat algorithm. This method is based on the traditional bat algorithm. The optimization success rate is introduced to change the bat individual speed update method. At the same time, the chaos method is used to initialize the distribution of the bat individual in the search space. , and use the concept of artificial potential field to simulate the gravitational field of the end point and the repulsion field of the starting point and obstacles to accelerate the movement speed of individual bats to the optimal solution. Finally, an improved bat algorithm based on chaotic artificial potential field is proposed. In the UAV route planning task, compared with the traditional bat algorithm, this method has a 36.56% shorter track length, 56.05% shorter planning time, and 49.53% lower fitness value of obstacle avoidance effect; compared with the differential evolution bat algorithm, the track length The length is 27.16% shorter, the planning time is 27.30% shorter, and the fitness value of the obstacle avoidance effect is 42.46% lower. It is a route planning algorithm with practical application significance.

Description

technical field [0001] The invention belongs to the technical field of intelligent control, and relates to a route planning method for an unmanned aerial vehicle, in particular to a route planning method for an unmanned aerial vehicle based on an improved bat algorithm. Background technique [0002] UAV route planning generally refers to the process of finding a flyable route from the starting point to the target point that meets the UAV performance indicators under specific constraints. The algorithm adopted for the UAV route planning problem directly affects the success rate and efficiency of route planning. Swarm intelligence algorithms generally have the advantages of fast convergence speed, strong robustness, and potential parallelism, and are widely used in UAV route planning. However, when these swarm intelligence algorithms are used to solve route planning problems, there are generally defects that the solution accuracy is not high enough, the planned trajectory is ...

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Problems solved by technology

For the first time, Shibo Li et al. used the particle swarm optimization algorithm combined with fuzzy logic to solve the problem of two-dimensional UAV route planning, and compared and analyzed the different planning effects after the parameters in the particle swarm optimization algorithm were changed. However, there are two-dimensional experimental environment settings. Too simple, and there is no 3D simulation experiment; Hao Meng et al. used genetic algorithm combined with digital elevation map and RBF neural network to solve 3D UAV route planning, and gave a rigorous mathematical demonstration process, and also obtained Intuitive 3D route planning effect, but limited to the limitations of the genetic algorithm itself, there is still much room for improvement in the solution convergence speed and route planning effect of 3D route planning; Ioannis K.Nikolos et al. applied differential evolution algorithm to multi-UAV collaborative route In the planning problem, a better solution is given for the UAV formation to complete the task together. However, it can be seen from the experimental results that the route planned by the differential evolution algorithm has the characteristics of Levy flight trajectory, and cannot be directly used as an unmanned aerial vehicle. Man-machine flight route, in addition, the algorithm itself is not fast enough to solve, and the algorithm execution stability is not high enough; GaigeWang et al. applied the bat algorithm to the optimization of the flight cost function in the optimization of the UAV route planning problem, and the optimization effect of other traditional swarm intelligence algorithms A comparative analysis has been carried out, which proves that the bat algorithm has the advantages of fast solution speed, high convergence accuracy, and good algorithm stability in optimizing the flight cost function, but it lacks intuitive simulation experiments of two-dimensional and three-dimensional route planning effects; Gai-Ge Wang In the subsequent research, the bat algorithm based on the differential evolution algorithm was applied to the route planning of the UAV, and the results of two-dimensional and three-dimensional simulation experiments were given. The route planning effect of the algorithm is compared and demonstrated, which confirms the superiority of the proposed algorithm. However, the experimental environment setting has the defect of being too simple, which is not conducive to the detection of the real optimization and obstacle avoidance performance of the algorithm. In addition, the planned route also has defects. The characteristics of Levy's flight path, without smoothing the route

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