Energy Consumption Prediction For Path Planning Of UAV Based On Improved Sparrow Search Algorithm

Unmanned aerial vehicle(UAV)path planning is a key technology for ensuring UAV flight safety,efficiency,and stability.However,in complex high-dimensional environments,the path planning process becomes more challenging.Traditional algorithms may face limitations in dealing with these challenges.In addition,existing UAV path planning evaluation models only consider path constraints or simple threat constraints,which may compromise the accuracy of the evaluation model and ultimately affect the quality of path planning.In view of the above problems,the main work of this paper is as follows:(1)This paper presents an improved Sparrow search algorithm(CSSA)to address the problem of poor convergence performance in the original algorithm.We introduce Singer chaotic mapping and Levy flight strategy to optimize the Sparrow search algorithm to improve its convergence performance and avoid falling into local optima.Furthermore,we apply this algorithm to the improvement of the grey prediction model.Due to the fixed coefficient of the background value calculation formula in the grey prediction model,its flexibility is limited.By using the proposed algorithm to dynamically generate the background value for the traditional GM(1,1)model,the prediction error is reduced,which is beneficial for further application of the model in subsequent path planning problems.In summary,the proposed improved algorithm solves the problem of local optima in the original algorithm and improves the prediction accuracy of the GM(1,1).(2)This paper proposes an UAV energy consumption prediction model based on an improved grey prediction model.Existing UAV path planning evaluation models mostly consider simple constraints and overlook the important role of energy consumption in UAV flight,leading to inaccuracies and instability in path planning results.To address these issues,we establish an energy consumption prediction model based on actual flight data,which considers the relationship between the flying distance,angle,and energy consumption of the UAV.Additionally,a comprehensive evaluation function is established that considers multiple constraints and threats,providing optimized paths for the UAV.This energy consumption model serves as the basis for subsequent path planning for rotary-wing UAV.(3)This paper presents a two-dimensional terrain unmanned aerial vehicle(UAV)path planning method that combines the improved Sparrow search algorithm and the energy prediction model.Most existing algorithms cannot meet the high requirements for accuracy and stability of UAV path planning.To address this issue,this study applies our proposed improved algorithm and energy prediction model to two-dimensional path planning.Simulation results show that the algorithm can comprehensively consider energy consumption,path,accuracy,and other requirements to find an effective path in a complex environment.This method provides a more accurate and efficient solution for UAV path planning.(4)This paper proposes a three-dimensional UAV path planning method that combines an improved Sparrow search algorithm and an energy consumption prediction model.Compared to two-dimensional environments,three-dimensional environments have more complex constraints and higher requirements for planning efficiency.An improved Sparrow search algorithm(S-SSAE)is proposed in this article,which uses a Sinusoidal chaotic mapping to achieve uniform distribution of the population,an updated formula based on the t-distribution to improve the Sparrow search algorithm,and an elite reverse strategy to further update the population’s positions.These three methods together solve the problems of slow convergence speed and easy local optimization that the original algorithm faces.The proposed algorithm improves the efficiency and effectiveness of UAV three-dimensional path planning.