This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization(TLPIO)for unmanned combat aerial vehicles(UCAVs)in dogfight engagements.Firstly,a nonlinear F-16 aircraft...This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization(TLPIO)for unmanned combat aerial vehicles(UCAVs)in dogfight engagements.Firstly,a nonlinear F-16 aircraft model and automatic control system are constructed by a MATLAB/Simulink platform.Secondly,a 3-degrees-of-freedom(3-DOF)aircraft model is used as a maneuvering command generator,and the expanded elemental maneuver library is designed,so that the aircraft state reachable set can be obtained.Then,the game matrix is composed with the air combat situation evaluation function calculated according to the angle and range threats.Finally,a key point is that the objective function to be optimized is designed using the game mixed strategy,and the optimal mixed strategy is obtained by TLPIO.Significantly,the proposed TLPIO does not initialize the population randomly,but adopts the transfer learning method based on Kullback-Leibler(KL)divergence to initialize the population,which improves the search accuracy of the optimization algorithm.Besides,the convergence and time complexity of TLPIO are discussed.Comparison analysis with other classical optimization algorithms highlights the advantage of TLPIO.In the simulation of air combat,three initial scenarios are set,namely,opposite,offensive and defensive conditions.The effectiveness performance of the proposed autonomous maneuver decision method is verified by simulation results.展开更多
Driving space for autonomous vehicles(AVs)is a simplified representation of real driving environments that helps facilitate driving decision processes.Existing literatures present numerous methods for constructing dri...Driving space for autonomous vehicles(AVs)is a simplified representation of real driving environments that helps facilitate driving decision processes.Existing literatures present numerous methods for constructing driving spaces,which is a fundamental step in AV development.This study reviews the existing researches to gain a more systematic understanding of driving space and focuses on two questions:how to reconstruct the driving environment,and how to make driving decisions within the constructed driving space.Furthermore,the advantages and disadvantages of different types of driving space are analyzed.The study provides further understanding of the relationship between perception and decision-making and gives insight into direction of future research on driving space of AVs.展开更多
基金the Science and Technology Innovation 2030-Key Project of“New Generation Artificial Intelligence”(2018AAA0100803)the National Natural Science Foundation of China(U20B2071,91948204,T2121003,U1913602)。
文摘This paper proposes an autonomous maneuver decision method using transfer learning pigeon-inspired optimization(TLPIO)for unmanned combat aerial vehicles(UCAVs)in dogfight engagements.Firstly,a nonlinear F-16 aircraft model and automatic control system are constructed by a MATLAB/Simulink platform.Secondly,a 3-degrees-of-freedom(3-DOF)aircraft model is used as a maneuvering command generator,and the expanded elemental maneuver library is designed,so that the aircraft state reachable set can be obtained.Then,the game matrix is composed with the air combat situation evaluation function calculated according to the angle and range threats.Finally,a key point is that the objective function to be optimized is designed using the game mixed strategy,and the optimal mixed strategy is obtained by TLPIO.Significantly,the proposed TLPIO does not initialize the population randomly,but adopts the transfer learning method based on Kullback-Leibler(KL)divergence to initialize the population,which improves the search accuracy of the optimization algorithm.Besides,the convergence and time complexity of TLPIO are discussed.Comparison analysis with other classical optimization algorithms highlights the advantage of TLPIO.In the simulation of air combat,three initial scenarios are set,namely,opposite,offensive and defensive conditions.The effectiveness performance of the proposed autonomous maneuver decision method is verified by simulation results.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.U1864203)in part by the International Science,and Technology Cooperation Program of China(No.2016YFE0102200).
文摘Driving space for autonomous vehicles(AVs)is a simplified representation of real driving environments that helps facilitate driving decision processes.Existing literatures present numerous methods for constructing driving spaces,which is a fundamental step in AV development.This study reviews the existing researches to gain a more systematic understanding of driving space and focuses on two questions:how to reconstruct the driving environment,and how to make driving decisions within the constructed driving space.Furthermore,the advantages and disadvantages of different types of driving space are analyzed.The study provides further understanding of the relationship between perception and decision-making and gives insight into direction of future research on driving space of AVs.
