Research On Route Planning For Unmanned Aerial Vehicles Under Uncertain Environment

Path planning is one of the critical components of Unmanned Air Vehicle(UAV).Because it is difficult to obtain accurate information of the global environment,UAV missions are always carried out in dynamic and complex environment which includes many uncertain factors such as moving threat and external disturbance.Route planning under uncertainty environment for is to plan out a feasible route for UAV rapidly considering arrival time,performance constraints and threat factors.Focusing on UAV robust route planning under uncertainty environment,the main contents are summarized as follow.(1)The linear probabilistic constraint model was built according to uncertainty caused by the location error of the fixed obstacles and the uncertainty in the UAV system model.The probabilistic constraint model is converted to the deterministic constraint model by chance constrain formulation.This chance constrain formulation is introduced into the Rapidly-Exploring Random Trees algorithm(RRT),thus yielded a novel algorithm noted as CC-RRT to solve the path planning problem with environmental uncertainty.The validity of CC-RRT algorithm was proved by several simulation test.(2)On the basis of summarizing the shortcomings of tradition targets tracking model,the concept of motion behavior is introduced to describe the motion intend of the moving threats.The mathematic model of motion pattern is established by the Gaussian process which defines a mapping from UAV states to a distribution over trajectory derivatives.As to the situation that the moving threats have more than one motion pattern,the mixed Gaussian process motion pattern model is built based on Bayesian statistics framework.(3)The model of Gaussian process is used to build the motion patterns of the moving threats.The Gaussian process learns the mapping of positions to velocities,then the trajectory of moving targets or threats could be predicted.To overcome the shortage of Gaussian process trajectory prediction algorithm,it is combined with a sampling-based reachability refinement,which conditions the GP predictions to enforce dynamic and environmental constraints.This improved trajectory prediction algorithm is call RR-GP.The accuracy of the RR-GP algorithm was validated by(4)The CC-RRT algorithm is integrated with RR-GP algorithm to solve the robust path planning problem in dynamic uncertainty environment.These algorithms were put into simulation test in several scenarios under the MATLAB environment.Simulation results showed that the proposed planning algorithm could effective find feasible for UAV under such conditions.