摘要
近年来,无人机产业规模与应用需求不断扩大,实现无人机的自主化和智能化成为了行业内亟待解决的核心问题。无人机导航作为无人机自主控制领域的基础技术,已然成为无人机应用研究的重中之重。目前大多数无人机导航方法依赖于环境信息的重建,消耗过多的计算和内存,无法满足日益复杂的场景与实时性要求。因此,基于深度学习卓越的表征学习能力与强化学习的自主学习决策能力,提出无人机自主导航方法,通过不断自主学习优化决策策略,更好地完成导航任务。首先构造连续性动作空间以及非稀疏性奖励函数,用来引导无人机的学习过程;并设计特征提取模块与决策模块来提高无人机感知能力和决策能力。实验结果表明,在仿真三维场景下,该算法表现出最优的导航避障性能,在所设计的三维场景下导航成功率可达到87%,平均累计奖励收敛值较同期方法提高33%,同时缩短训练时长,提高训练稳定性。
In recent years,with the UAV industry and application demands expanding,the realization of UAV autonomy and intelligence has been identified as a critical challenge As a foundational technology in the field of autonomous control of UAVs,UAV navigation and exploration have become a top priority in UAV application research.Currently,most UAV navigation and exploration methods rely on the reconstruction of environmental information,consuming excessive computation and memory,thus failing to meet the increasingly complex scenarios and real-time requirements.Therefore,based on the excellent representation learning ability of deep learning and the self-learning decision-making ability of reinforcement learning,an autonomous navigation method for unmanned aerial vehicles was proposed.By continuously optimizing decision-making strategies through self-learning,the navigation task could be better completed.The method first constructed a continuous action space and a non-sparse reward function to guide the learning process of the drone;then designed feature-extraction and decision-making modules to enhance the perception and decision-making capabilities of the UAV.The experimental results demonstrated that the algorithm exhibited the best navigation and obstacle avoidance performance in the simulated 3D scene.The navigation success rate in the designed 3D scene reached 87%,a 33%increase in average cumulative reward convergence value over that of the same period method,reduced the training time,and improved training stability.
作者
刘伯凯
殷雪峰
孙传昱
葛慧林
魏子麒
姜雨彤
朴海音
周东生
杨鑫
LIU Bokai;YIN Xuefeng;SUN Chuanyu;GE Huilin;WEI Ziqi;JIANG Yutong;PIAO Haiyin;ZHOU Dongsheng;YANG Xin(Key Laboratory of Social Computing and Cognitive Intelligence,School of Computer Science,Dalian University of Technology,Dalian Liaoning 116024,China;School of Automation,Jiangsu University of Science and Technology,Zhenjiang Jiangsu 212100,China;Nstitute of Automation,Chinese Academy of Sciences,Beijing 100190,China;National Key Laboratory of Advanced Off-road System Technology,China North Vehicle Research Institute,Beijing 100072,China;Shenyang Aircraft Design and Research Institute,Aviation Industry Corporation of China,Shenyang Liaoning 110035,China;School of Software Engineering,Dalian University,Shenyang Liaoning 116024,China)
出处
《图学学报》
北大核心
2025年第5期1010-1017,共8页
Journal of Graphics
基金
国家自然科学基金(62441216)
科技部“脑科学与类脑研究”重大项目(2022ZD0210500)。
关键词
深度强化学习
注意力机制
无人机
导航避障
三维场景
deep reinforcement learning
attention mechanism
unmanned aerial vehicle
navigation and obstacle avoidance
3D scene
