High-Altitude Long-Endurance(HALE)solar-powered Unmanned Aircraft Vehicles(UAVs)can utilize solar energy as power source and maintain extremely long cruise endurance,which has attracted extensive attentions from resea...High-Altitude Long-Endurance(HALE)solar-powered Unmanned Aircraft Vehicles(UAVs)can utilize solar energy as power source and maintain extremely long cruise endurance,which has attracted extensive attentions from researchers.Trajectory optimization is a promising way to achieve superior flight time because of the finite solar energy absorbed in a day.In this work,a method of trajectory optimization and guidance for HALE solar-powered aircraft based on a Reinforcement Learning(RL)framework is introduced.According to flight and environment information,a neural network controller outputs commands of thrust,attack angle,and bank angle to realize an autonomous flight based on energy maximization.The validity of the proposed method was evaluated in a 5-km radius area in simulation,and results have shown that after one day-night cycle,the battery energy of the RL-controller was improved by 31%and 17%compared with those of a Steady-State(SS)strategy with a constant speed and a constant altitude and a kind of statemachine strategy,respectively.In addition,results of an uninterrupted flight test have shown that the endurance of the RL controller was longer than those of the control cases.展开更多
现代飞行程序设计受地形、障碍物、空域和飞行性能等多种因素的影响,设计过程中需进行大量针对设计细节有效性的评估工作;设计完毕的飞行程序还需专业的飞行试飞人员进行模拟机和真机试飞,耗费大量的人力、经济成本。如果试飞前缺少针...现代飞行程序设计受地形、障碍物、空域和飞行性能等多种因素的影响,设计过程中需进行大量针对设计细节有效性的评估工作;设计完毕的飞行程序还需专业的飞行试飞人员进行模拟机和真机试飞,耗费大量的人力、经济成本。如果试飞前缺少针对性的分析评估,一方面会增加试飞成本的支出,另一方面也会导致真机试飞环节存在安全隐患。针对上述问题,利用深度强化学习技术,提出一种在满足飞行程序设计规范条件下,面向飞行程序有效性和可行性验证的离场航迹自动生成方法。首先,利用空气动力学原理,建立考虑飞行性能和障碍物超障因素的基本飞行动力学模型,并借助Unity3D引擎构建三维可视化的训练平台;其次,在PyTorch深度学习框架中,利用Mlagents强化学习平台构建航空器在飞行时各个阶段的试飞训练模型,设计包括起飞、转弯、巡航和降落这4个目标的场景和奖励函数。以离场飞行程序试飞为例,采用厦门高崎机场某PBN(Performance Based Navigation)离场程序进行实例训练验证,并利用动态时间规整(DTW)距离量化实际生成航迹与标称航迹之间的偏离度。实验结果显示,偏差度满足飞行程序超障保护区的限制要求。上述训练模型在其他离场程序的实验结果也验证了模型具有较好的泛化能力。展开更多
基金Foundation of the Special Research Assistant of Chinese Academy of Sciences(No.E0290A0301)。
文摘High-Altitude Long-Endurance(HALE)solar-powered Unmanned Aircraft Vehicles(UAVs)can utilize solar energy as power source and maintain extremely long cruise endurance,which has attracted extensive attentions from researchers.Trajectory optimization is a promising way to achieve superior flight time because of the finite solar energy absorbed in a day.In this work,a method of trajectory optimization and guidance for HALE solar-powered aircraft based on a Reinforcement Learning(RL)framework is introduced.According to flight and environment information,a neural network controller outputs commands of thrust,attack angle,and bank angle to realize an autonomous flight based on energy maximization.The validity of the proposed method was evaluated in a 5-km radius area in simulation,and results have shown that after one day-night cycle,the battery energy of the RL-controller was improved by 31%and 17%compared with those of a Steady-State(SS)strategy with a constant speed and a constant altitude and a kind of statemachine strategy,respectively.In addition,results of an uninterrupted flight test have shown that the endurance of the RL controller was longer than those of the control cases.
文摘现代飞行程序设计受地形、障碍物、空域和飞行性能等多种因素的影响,设计过程中需进行大量针对设计细节有效性的评估工作;设计完毕的飞行程序还需专业的飞行试飞人员进行模拟机和真机试飞,耗费大量的人力、经济成本。如果试飞前缺少针对性的分析评估,一方面会增加试飞成本的支出,另一方面也会导致真机试飞环节存在安全隐患。针对上述问题,利用深度强化学习技术,提出一种在满足飞行程序设计规范条件下,面向飞行程序有效性和可行性验证的离场航迹自动生成方法。首先,利用空气动力学原理,建立考虑飞行性能和障碍物超障因素的基本飞行动力学模型,并借助Unity3D引擎构建三维可视化的训练平台;其次,在PyTorch深度学习框架中,利用Mlagents强化学习平台构建航空器在飞行时各个阶段的试飞训练模型,设计包括起飞、转弯、巡航和降落这4个目标的场景和奖励函数。以离场飞行程序试飞为例,采用厦门高崎机场某PBN(Performance Based Navigation)离场程序进行实例训练验证,并利用动态时间规整(DTW)距离量化实际生成航迹与标称航迹之间的偏离度。实验结果显示,偏差度满足飞行程序超障保护区的限制要求。上述训练模型在其他离场程序的实验结果也验证了模型具有较好的泛化能力。
