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1. 中国北方车辆研究所 无人中心, 北京 100072
2. 中兵智能创新研究院有限公司, 北京 100072
3. 群体协同与自主实验室, 北京 100072
Received:18 May 2023,
Published Online:25 September 2023,
Published:20 September 2023
移动端阅览
Wei XU, Bo SU, Lei JIANG, et al. Key Technologies and Application Prospects of Off-road Legged Robot Swarm System[J]. Acta Armamentarii, 2023, 44(9): 2568-2579.
Wei XU, Bo SU, Lei JIANG, et al. Key Technologies and Application Prospects of Off-road Legged Robot Swarm System[J]. Acta Armamentarii, 2023, 44(9): 2568-2579. DOI: 10.12382/bgxb.2023.0445.
足式机器人作为典型的地面移动机器人之一
其越野能力和智能水平不断提升
将足式越野技术与集群技术深入融合
是未来无人化装备发展的重要趋势之一。首先分析了群体仿生学机理、单体足式越野机器人、集群感知与协作和集成演示与应用等方面的研究现状
提出了足式越野机器人集群系统面临的技术挑战和科学问题
从足式越野机器人高机动越野、群体自组织机理与集群协作决策控制、群体协同态势感知和通讯自组网与应用验证四个方面深入剖析了亟待解决的核心关键技术
最后对足式越野机器人集群在军民领域的发展前景提出了设想和建议。
As one of the typical ground mobile robots
the legged robot has continuously improved off-road capability and intelligence level. The in-depth integration of leg-based off-road technology and swarm technology is one of the important trends in the development of unmanned equipment in the future. This paper first analyzes the research status of swarm bionic mechanism
off-road single-legged robot
swarm sensing and cooperation
and integrated demonstration and application
puts forward the technical challenges and scientific problems faced by the off-road legged swarm system
and presents in-depth analysis of the core key technologies to be solved from four aspects: high mobility off-road capability of off-road legged robots
swarm self-organization mechanism and collaborative decision-making and control
swarm collaborative situational awareness
and communication ad hoc networks and application validation. Finally
ideas and suggestions for the development prospects of the off-road legged robots swarm in the military and civilian fields are proposed.
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HOWARD A , PARKER LE , SUKHATME GS . Experiments with a large heterogeneous mobile robot team: exploration, mapping, deployment and detection [J ] . The International Journal of Robotics Research , 2006 , 25 ( 5/6 ): 431 - 447 . doi: 10.1177/0278364906065378 https://dx.doi.org/10.1177/0278364906065378 . http://journals.sagepub.com/doi/10.1177/0278364906065378 http://journals.sagepub.com/doi/10.1177/0278364906065378 We describe the design and experimental validation of a large heterogeneous mobile robot team built for the DARPA Software for Distributed Robotics (SDR) program. The core challenge for the SDR program was to develop a multi-robot system capable of carrying out a specific mission: to deploy a large number of robots into an unexplored building, map the building interior, detect and track intruders, and transmit all of the above information to a remote operator. To satisfy these requirements, we developed a heterogeneous robot team consisting of approximately 80 robots. We sketch the key technical elements of this team, focusing on the novel aspects, and present selected results from supervised experiments conducted in a 600 m 2 indoor environment.
李超 , 王瑞星 , 黄建忠 , 等 . 稀疏奖励下基于强化学习的无人集群自主决策与智能协同 [J ] . 兵工学报 , 2023 , 44 ( 6 ): 1537 - 1546 . DOI: 10.12382/bgxb.2022.0177 http://doi.org/10.12382/bgxb.2022.0177 无人集群将深刻地塑造战争样式,为提升无人集群自主决策算法能力,对异构无人集群攻防对抗自主决策方法进行研究。对无人集群对抗模型设计进行总体概述,并对无人集群攻防对抗场景进行模型设计;针对无人集群自主决策采用强化学习技术广泛存在的稀疏奖励问题,提出基于局部回报重塑的奖励机制设定方法;在此基础上叠加优先经验回放,有效地改善稀疏奖励问题;通过程序仿真和演示系统设计,验证该方法的优越性。该方法的研究将加速基于强化学习技术的无人集群自主决策算法网络收敛过程,对无人集群自主决策算法研究具有重要意义。
LI C , WANG R X , HUANG J Z , et al . Autonomous decision-making and intelligent collaboration of UAV swarms based on reinforcement learning with sparse rewards [J ] . Acta Armamentarii , 2023 , 44 ( 6 ): 1537 - 1546 . (in Chinese) DOI: 10.12382/bgxb.2022.0177 http://doi.org/10.12382/bgxb.2022.0177 UAV swarms will profoundly shape the pattern of warfare. In order to improve the autonomous decision-making algorithm capability of UAV swarms, the autonomous decision-making method for heterogeneous UAV swarm attack-defense confrontation scenarios is studied. An overview of the design of the UAV swarm confrontation model and the model design of the UAV swarm attack-defense confrontation scenario are carried out. To solve the sparse reward problem which widely exists in the reinforcement learning technology in the autonomous decision-making of the UAV swarm, a reward mechanism setting method based on local reward reshaping is proposed. And then, the prioritized experience replay is superimposed, which effectively improves the sparse reward problem. Finally, the superiority of this method is verified by simulation and demonstration system design. This study will accelerate the network convergence process of the autonomous decision-making algorithm for UAV swarms based on reinforcement learning technology, which is of great significance to the research on autonomous decision-making algorithms of UAV swarms.
孔国杰 , 冯时 , 于会龙 , 等 . 无人集群系统协同运动规划技术综述 [J ] . 兵工学报 , 2023 , 44 ( 1 ): 11 - 26 . DOI: 10.12382/bgxb.2022.0930 http://doi.org/10.12382/bgxb.2022.0930 地面无人集群由多个地面无人移动平台构成,能够通过各无人平台间相互协同完成统一的系统协同目标,在军事和交通系统等领域能够发挥重要的作用。协同运动规划作为无人集群系统协同的关键技术之一,近年来在理论和应用等方面的研究受到越来越多的关注。针对此研究问题,归纳总结了近年来相关领域的无人集群系统协同运动规划的研究成果,阐述了无人集群协同运动规划技术的研究背景和意义,结合国内外发展现状和研究进展,对多车协同系统的应用进行了表述,并根据主流研究方法使用的框架和算法,对现有的协同运动规划技术进行分类,并讨论了各类方法的主要特点,同时对相关代表性工作进行讨论,提出了无人集群协同规划技术面临的挑战和未来发展方向。
KONG G J , FENG S , YU H L , et al . A review on cooperative motion planning of unmanned vehicles [J ] . Acta Armamentarii , 2023 , 44 ( 1 ): 11 - 26 . (in Chinese) DOI: 10.12382/bgxb.2022.0930 http://doi.org/10.12382/bgxb.2022.0930 An unmanned ground swarm system consists of multiple unmanned ground mobile platforms, which can achieve common objectives through cooperation and has promising applications in military and transportation systems. Cooperative motion planning is one of the key technologies in the cooperation of unmanned swarm systems or vehicles. It has received increasing attention in both theoretical and application research. This review summarizes and analyzes recent advances in cooperative motion planning of unmanned swarm systems, and provides the background of relevant research. Then the techniques utilized in cooperative motion planning and its applications are briefly discussed considering its development in China and beyond. These techniques are categorized according to different frameworks and algorithms. With such a classification, representative works are discussed regarding their features. Moreover, the challenges and future development of cooperative motion planning are proposed.
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喻俊志 , 周子烨 , 刘金存 . 多自主水下机器人系统研究进展与分析 [J ] . 信息与控制 , 2021 , 50 ( 2 ): 129 - 140 . DOI: 10.13976/j.cnki.xk.2021.0572 http://doi.org/10.13976/j.cnki.xk.2021.0572 多自主水下机器人系统(MAUV)越来越多地被应用于海洋勘探与资源开发,受到研究者们的普遍关注.本文探讨了常见的多自主水下机器人系统所执行的任务及所用方法,并从新功能涌现的角度对多自主水下机器人系统的协作这一关键问题进行了全面回顾与分析.首先,将合作按照任务空间、运动空间和量测空间及其组合进行分类;其次,从三个视角介绍了不同类别的水下多机器人系统及其技术方法;最后,展望了多自主水下机器人系统未来的研究思路.该综述有望为复杂的海洋应用场景中水下多机器人系统高效协同完成水下任务提供策略和途径.
YU J Z , ZHOU Z Y , LIU J C . Research progress and analysis on multiple autonomous underwater vehicle systems [J ] . Information and Control , 2021 , 50 ( 2 ): 129 - 140 . (in Chinese) DOI: 10.13976/j.cnki.xk.2021.0572 http://doi.org/10.13976/j.cnki.xk.2021.0572 Multiple autonomous underwater vehicles (MAUV) are increasingly applied to marine exploration and resource development. As such, they have been widely explored. In this paper, the tasks and methods of common MAUV are discussed, and the key problem of cooperation among MAUV is comprehensively investigated from the perspective of emerging functions. Firstly, the cooperation among MAUV is classified according to task-, motion-, and measurement-space aspects and their combination. Secondly, different kinds of MAUV and their realization methods are introduced from three perspectives. Lastly, the future research direction of MAUV is discussed. This survey is conducted to provide strategies and approaches for MAUV and help accomplish underwater tasks efficiently and cooperatively in complex ocean-related application scenarios.
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DORIGO M , THERAULAZ G , TRIANNI V . Swarm robotics: past, present, and future [Point of View] [J ] . Proceedings of the IEEE , 2021 , 109 ( 7 ): 1152 - 1165 . DOI: 10.1109/JPROC.2021.3072740 http://doi.org/10.1109/JPROC.2021.3072740 https://ieeexplore.ieee.org/document/9460560/ https://ieeexplore.ieee.org/document/9460560/
张建东 , 王鼎涵 , 杨啟明 , 等 . 基于分层强化学习的无人机空战多维决策 [J ] . 兵工学报 , 2023 , 44 ( 6 ): 1547 - 1563 . DOI: 10.12382/bgxb.2022.0711 http://doi.org/10.12382/bgxb.2022.0711 针对无人机空战过程中面临的智能决策问题,基于分层强化学习架构建立无人机智能空战的多维决策模型。将空战自主决策由单一维度的机动决策扩展到雷达开关、主动干扰、队形转换、目标探测、目标追踪、干扰规避、武器选择等多个维度,实现空战主要环节的自主决策;为解决维度扩展后决策模型状态空间复杂度、学习效率低的问题,结合Soft Actor-Critic算法和专家经验训练和建立元策略组,并改进传统的Option-Critic算法,设计优化策略终止函数,提高策略的切换的灵活性,实现空战中多个维度决策的无缝切换。实验结果表明,该模型在无人机空战全流程的多维度决策问题中具有较好的对抗效果,能够控制智能体根据不同的战场态势灵活切换干扰、搜索、打击、规避等策略,达到提升传统算法性能和提高解决复杂决策效率的目的。
ZHANG J D , WANG D H , YANG Q M , et al . Multi-dimensional decision-Making for UAV air combat based on hierarchical reinforcement learning [J ] . Acta Armamentarii , 2023 , 44 ( 6 ): 1547 - 1563 . (in Chinese) DOI: 10.12382/bgxb.2022.0711 http://doi.org/10.12382/bgxb.2022.0711 To solve the intelligent decision-making problem in the process of UAV air combat, a multi-dimensional decision-making model for UAV intelligent air combat based on the hierarchical reinforcement learning architecture is established, allowing the autonomous decision-making of air combat to be extended from a single-dimensional maneuver decision to a multi-dimensional one including radar switch, active jamming, formation conversion, target detection, target tracking, interference avoidance, weapon selection, etc., so that autonomous decision-making in the main steps of air combat is realized. In order to solve the problems of state-space complexity and low learning efficiency of the decision-making model after the dimension expansion, a meta-strategy group is trained and established with the Soft Actor-Critic algorithm and expert experience, and the traditional Option-Critic algorithm is improved. The strategy termination function is designed and optimized to improve the flexibility of strategy switching and realize seamless multi-dimensional decision-making switching in air combat.. The experimental results show that the proposed method has good countermeasure effectiveness for the multi-dimensional decision-making during the whole process of UAV air combat, which can control the agent to flexibly switch among interference, search, strike, and avoidance strategies according to different battlefield situations with the purpose of improving the performance of traditional algorithms and the efficiency of solving complex decision-making processes.
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