摘要
针对无人机在空中执行航线跟随任务时无法对未知环境作出合理应对措施等问题,提出了一种基于深度强化学习的四旋翼无人机航线跟随方法.通过无人机受力分析、欧拉角变换建立四旋翼无人机动力学模型.在深度强化学习的框架下,分析无人机坐标值、欧拉角、速度值等相关因素,对无人机的状态空间进行模糊化,作为深度强化学习的状态输入.相对于传统方法,构建的四旋翼无人机非线性飞行运动学和动力学模型更为真实.仿真结果表明,在不断的训练和学习后,四旋翼无人机能够对随机产生的任务航线进行高精度跟随.
In view of the problem that the unmanned aerial vehicle(UAV)is unable to take reasonable countermeasures in the stochastic external environment during executing the mission in the air,an approach of UAV route following based on deep reinforcement learning is proposed.The quadrotor UAV dynamics model is found by force analysis of UAV and transformation of Euler angle.Under the framework of deep reinforcement learning,such relative factors of UAV as the coordinates,the Euler angles,the flight velocity,etc.are analyzed,the state space is fuzzified as the state input of deep reinforcement learning.Compared with the traditional method,the buile non-linear flight dynamics and dynemic model of the quadrotor UAV is more realistic.The simulation results show that the quadrotor UAV can perform the task of randomly generated route following with high efficiency and low error after continuous training.
作者
杨志鹏
李波
甘志刚
梁诗阳
YANG Zhipeng;LI Bo;GAN Zhigang;LIANG Shiyang(School of Electronic Information,Northwestern Polytechnical University,Xi’an Shaanxi 710072,China;System Design Institute,Hubei Aerospace Technology Academy,Wuhan Hubei 430040,China;Key Laboratory of Data Link Technology,China Electronics Technology Group Corporation,Xi’an Shaanxi 710077,China;China Luoyang Institute of Electro-optical Equipment of AVIC,Luoyang Henan 471000,China)
出处
《指挥与控制学报》
CSCD
2022年第4期477-482,共6页
Journal of Command and Control
基金
数据链技术重点实验室开放基金(CLDL-20182101)资助。
作者简介
杨志鹏(1995-),男,硕士研究生,主要研究方向为深度强化学习、无人机智能控制;通信作者:李波(1978-),男,博士,副教授,主要研究方向为先进航空火力控制技术、无人系统智能决策、军事大数据分析.E-mail:libo803@nwpu.edu.cn;甘志刚(1997-),男,硕士研究生,主要研究方向为深度学习、深度强化学习、无人机自主决策;梁诗阳(1997-),女,硕士研究生,主要研究方向为无人机智能机动决策、先进航空火力控制技。
