摘要
随着无人集群在物流运输、农业管理、军事行动等场景的试验和应用,其面临的作业环境和任务内容日趋复杂,亟需设计效率更高、泛化能力更强、适应性更好的控制算法.将人工智能引入到无人集群系统控制的研究中,能够大幅提升现有无人集群的能力,完成复杂的作业任务.深度强化学习具有深度学习和强化学习的优点,无人集群系统深度强化学习控制研究受到了国内外科研人员的广泛关注,涌现出许多标志性成果.本文将从原理、特点等方面阐述深度强化学习概念,深入分析深度强化学习的多种典型算法,并讨论无人机集群的各类控制需求,进而介绍深度强化学习在无人机集群控制领域的典型研究成果,最后针对该领域研究成果的落地转化总结了应用前景和面临的挑战.
Recently,testing and using micro-unmanned vehicles,such as unmanned aerial vehicles(UAVs),in scenarios such assupply transportation,agricultural management,and military operations have become more common.It is no longer sufficient to controla single UAV to accomplish all missions.With the increasing complexities associated with operating and task requirements,anunmanned swarm requires a series of algorithms with higher efficiency,greater generalization ability,and better adaptability than theearlier algorithms.A combination of unmanned swarms with artificial intelligence is becoming a common solution to manage the aboverequirements.Deep reinforcement learning(DRL)is a machine learning method that combines deep learning(DL)and reinforcementlearning(RL);therefore,this method has the advantages of DL and RL.Using an RL method,an agent can learn from the environmentby trial and error and make decisions that autonomously obtain high scores.However,when the given environment is complex,thedecision function of the agent may be too difficult to implement and then the agent cannot make the correct decision.The DL method hasstrong fitting ability.A suitable deep neural network can simulate any linear or nonlinear function.If the DL method is used to simulate the decision function in RL,the hybrid method can solve the problem that an agent cannot solve and make a correct decision in acomplex environment.The combination of an unmanned swarm and a DRL method has been widely studied.This paper introduces theconcept of DRL from the perspective of principles and characteristics.This paper analyzes several typical DRL algorithms,discusses thevarious control requirements of a UAV swarm,and then focuses on the achievements of combining DRL and a UAV swarm control.Finally,this paper presents viewpoints on the application prospects and challenges related to landing and transformation in thecombination field.The concept of an unmanned swarm originated from the study of the behavior of biological groups.Several species ofbees,ants,bird
作者
梁鸿涛
王耀南
华和安
钟杭
郑成宏
曾俊豪
梁嘉诚
李政辰
LIANG Hongtao;WANG Yaonan;HUA Hean;ZHONG Hang;ZHENG Chenghong;ZENG Junhao;LIANG Jiacheng;LI Zhengchen(School of Electrical and Information Engineering,Hunan University,Changsha 410082,China;National Engineering Research Center of RVC,Hunan University,Changsha 410082,China;School of Robotics,Hunan University,Changsha 410082,China)
出处
《工程科学学报》
EI
CSCD
北大核心
2024年第9期1521-1534,共14页
Chinese Journal of Engineering
基金
湖南省自然科学基金重大项目(2021JC0004)
国家重点研发计划资助项目(2022YFB4701800,2021ZD0114503)
湖南省自然科学基金资助项目(2023JJ40165)
国家自然科学基金资助项目(62173132)。
关键词
无人集群
集群控制
深度强化学习
多智能体
人工智能
集群智能
unmanned swarm
swarm control
deep reinforcement learning
multiagent
artificial intelligence
swarm intelligence
