Intelligent Optimization-based Trajectory Planning And Simulation For Rotorcraft Under Complex Environment

The rotorcraft is a kind of flight vehicle using rotors as direct power source to achieve a flight.Compared to traditional fixed-wing aircraft,rotorcraft has less requirements on the taking-off and landing site,and it has strong maneuverability,relatively simple structure,high reliability and so on.Therefore,it has been widely applied in both military and civil fields.In this paper,the trajectory planning problems for rotorcraft in complex environments are studied.The dynamic model of flight vehicle is established as the foundation of trajectory planning problem.Then the mathematical models are formulated to describe the trajectory planning problem,and trajectory planning algorithms are designed to solve the established models and obtain an optimal trajectory.The study on dynamic characteristics of the rotorcraft is the basis of trajectory planning.In this paper,the dynamic model of rotorcraft is divided into several modules.First,nonlinear dynamic model is established,including the flapping equations,force and moment equations,and rigid body dynamics and kinematics equations.Then,the motion equations are trimmed and linearized.Furthermore,the accuracies of reduced-order model and standard model are compared.At present,the rotorcraft has been successfully applied to execute transportation tasks in order to relieve the pressure of ground traffic in cities and reduce the environment pollution.In this kind of task,it is a precondition to plan a safe and efficient flight route for the rotorcraft to ensure successful completion of the transportation task.First,the urban environment and constraints are defined,including the establishment of urban wind field model and obstacle model.Based on the characteristic of the transportation task and the requirement of transported goods,the constraints on rotercraft’s attitude are set.The goal of the transportation task is to minimize the deviation between the destination and the final position of rotorcraft.Based on the principles of Cuckoo Search(CS)algorithm,some improvements are made to integrate it into the trajectory planning problem.On this basis,the trajectory planning algorithm for rotorcraft in a urban transportation tasks is designed.The effectiveness of the proposed method is proved by simulation results in different environments.In addition,the influence of important parameters on algorithm performance in CS algorithm is analyzed by comparing the CS algorithm with other common intelligent optimization algorithms.The rotorcraft also has been widely applied in the military field.Taking the aircraft-carrier system as an example,the carrier-based rotorcraft can execute tasks without occupying the deck runway and other resources when its take-off and landing,which cannot be executed by the fixed-wing aircrafts.It has great significance to study the trajectory planning problem for landing of carrier-based rotorcraft,which can improve the recycling capability of the aircraft carrier.At first,the landing task is divided into two phases,i.e.,approaching the aircraft carrier and landing on the deck.When establishing the mathematical model of trajectory planning problem,the constraints are classified into two categories according to the characteristics of rotorcraft and the task requirements.The maneuvering performance of the rotorcraft,the direction of entering target point,and the influence between the aircraft carrier motion and the landing motion are all taken into account.The goal of the landing task is to reduce the terminal position error and the relative speed between the rotorcraft and the flight deck.In order to achieve this goal,a multiphase path planning algorithm based on the pigeon inspired optimization(PIO)is proposed to adapt the changing environment.The results of landing trajectory planning with different carrier-aircraft movements are compared and analyzed in the simulation examples,which verifies the rationality of the established model and the proposed algorithm.At last,the reason of selecting PIO algorithm to solve this problem is explained by comparing it with other common intelligent optimization algorithms.