Research On Route Planning Of Integrated Economic Navigation

With the continuous development and application of various technologies such as automation and computers,the technology of drones has become more intelligent,the structure is increasingly complex,and the functions are more powerful.As drones can take on more and more tasks,the application environment they face is increasingly characterized by non-cooperation,multiple uncertainties,high threats,and dynamics.This puts higher requirements on the command and control of the drone.The traditional ground-based accusation station remote control or program-based automatic control strategy has been difficult to adapt to the mission requirements of future drones.UAV's autonomous mission planning and decision making is one of the main goals of future UAV command and control development according to mission requirements and real-time battlefield environment.Economic navigation refers to the fact that the aircraft meets its physical constraints and specific environmental conditions,combined with the fuel consumption of the aircraft in actual flight,and optimizes the flight path of the aircraft to improve the flight trajectory.Fuel utilization.In this paper,the fuel consumption model of the climb,cruise and descent phases of the aircraft during flight is studied and constructed.A multi-objective optimization algorithm is used to optimize the trajectory of the aircraft,so that the energy utilization rate in the actual flight process is improved,and the flight is extended.Combat time.Finally,under the condition of minimizing energy consumption,the route is reasonably planned and the economic route planning is completed.The main work and innovations of the thesis are as follows:(1)Using the improved Rapidly-exploring Random Tree algorithm for aircraft path planning.Firstly,the planning space is established.Then,the random expansion problem of the classical fast search random tree algorithm in the search path is improved.The introduction of the selection probability improves the non-targeting of random tree node growth,and the bidirectional extended random tree is used to improve the search efficiency.The node pruning strategy removes redundant path points and finally smoothes the path based on the B-spline curve.The simulation shows the effectiveness of the bidirectional RRT algorithm based on the selection probability,and is superior to the traditional RRTalgorithm in time and path length.(2)The economic navigation solution is solved by the improved non-dominated sorting genetic algorithm-II.Firstly,the aircraft energy consumption model is constructed.On this basis,the method of adaptively adjusting the crossover and mutation operator according to the current aircraft weight is proposed to accelerate the convergence speed of the algorithm,improve the time performance of the algorithm,and simulate the improved algorithm.(3)By analyzing the principle characteristics of the typical heuristic search algorithm A*,the evaluation function is applied to the neighboring nodes for the shortcomings of the RRT algorithm in the path planning because the purpose of the random tree diffusion is not strong and the final path quality is not high.In the selection strategy,the influencing factors of the performance of the heuristic RRT algorithm are simulated and analyzed.Finally,the classical RRT algorithm is used to verify that the heuristic RRT algorithm has a certain degree of optimization after using the distance estimation function.Theoretically,it is feasible to introduce trajectory optimization for decision making while path planning.