摘要
针对模糊控制避障算法在障碍物信息未知的环境中适应性不强,难以有效规避“凹多边形”障碍物的问题,提出了一种基于模糊神经网络的无人机实时避障算法。采用等效夹角对模糊控制器的输入变量进行优化设计,克服了单一角度或距离在障碍物表征方面的局部片面性。基于BP神经网络理论设计了模糊控制器的初始隶属度函数和模糊神经网络架构;将模糊控制器在多个未知环境下生成的有效避障数据作为训练数据集,对模糊神经网络进行训练。仿真结果表明:所提出的模糊神经网络方法与模糊控制器相比具有更强的适应性,在面对未知复杂障碍物时避障更加灵活、路径更短,避障成功率更高。
Aimed at the problem that adaptability of fuzzy control obstacle avoidance algorithm is poor in the environment with unknown obstacle information,this paper proposes an obstacle avoidance strategy for UAV based on fuzzy neural network.Firstly,the fuzzy controller is designed,and the input variables of the fuzzy controller are optimized.Secondly,the fuzzy neural network is trained by using the relevant data in the ideal obstacle avoidance path.The simulation results show that the method has stronger adaptability,better obstacle avoidance effect in the face of complex obstacles,can shorten the local planning path,can safely reach the target area in the unknown environment,and the motion trajectory is smooth.
作者
吕智虎
梁晓龙
任宝祥
李哲
张佳强
齐铎
侯岳奇
LYU Zhihu;LIANG Xiaolong;REN Baoxiang;LI Zhe;ZHANG Jiaqiang;QI Duo;HOU Yueqi(Air Traffic Control and Navigation College,Air Force Engineering University,Xi’an 710051,China;Shaanxi Province Lab of Meta-synthesis for Electronic&Information System,Xi’an 710051,China)
出处
《空军工程大学学报（自然科学版）》
CSCD
北大核心
2021年第5期82-89,共8页
Journal of Air Force Engineering University(Natural Science Edition)
基金
国家自然科学基金(61703427)。
关键词
未知环境
实时避障
神经网络
模糊控制
unknown environment
drone avoidance
neural network
fuzzy control
