-
摘要: 多机协同围捕作为多机器人协同领域的一项重要分支, 着重研究多个机器人通过相互协作对动态可疑目标实现有效的追踪与围捕, 在军事侦查、紧急救援、协同探测等领域具有重要的研究意义与实际应用价值. 首先通过国内外科学引文数据库对多机协同围捕领域相关的文献进行全面检索, 深入剖析目前该领域前沿技术的发展现状与研究热点. 接下来从理论与技术层面分别针对多机协同围捕领域中的目标协同搜索、多机任务分配、协同围捕控制等方面进行全面总结, 重点阐述各研究内容常用方法与技术的工作原理、优缺点及适用范围等. 最后对该领域的发展现状进行总结, 并分析探讨目前尚未解决的难点, 对未来的发展方向提出展望.Abstract: As a significant branch of multi-robot coordination, multi-robot cooperative hunting mainly focuses on tracking and capturing dynamic suspicious targets effectively through cooperation. It has important significance and has been applied in various fields, such as military reconnaissance, emergency rescue, and collaborative detection. This paper conducts a comprehensive search of relevant literature in the field of multi-robot cooperative hunting through domestic and foreign scientific citation databases. It then thoroughly analyzes the current development status and research hotspots of frontier technologies in this field. Following this, the paper offers a thorough summary of research in the field of multi-robot cooperative hunting, covering theoretical and technical aspects, which concentrates on target cooperative search, multi-robot task allocation, and cooperative hunting control, etc.. The working principles, advantages, disadvantages, and application ranges of commonly used methods and technologies in each research aspect are introduced in detail. Finally, this paper summarizes the current state of development and unresolved challenges in this field, and suggests potential directions for future development.
-
显著性目标检测[1-5]能够对图像中在视觉上最与众不同的对象或区域进行检测与识别. 目前, 显著性目标检测已经成功地作为许多计算机视觉领域任务的预处理过程, 包括目标跟踪[6]、物体识别[7]、语义分割[8]等.
传统方法[9-10]大多依靠颜色、纹理等手工特征或者启发式先验来捕获图像局部细节和全局上/下文. Goferman等[9]提出一种基于上/下文感知的方法, 对目标周围的不同区域均进行检测, 并最终基于四个心理学原理简单生成了显著性图. Yan等[10]设计了一个分层模型, 能够对显著信息进行层次分析, 并将不同层次的输出进行组合得到最终结果. 尽管上述算法取得了一定的成功, 但是由于缺乏高级语义信息的参与, 在复杂场景中检测显著物体的能力受到了很大限制.
近年来, 卷积神经网络得到快速发展. 例如文献[11-13]的卷积神经网络所具备的金字塔结构, 能够在较浅层拥有丰富的低层边缘细节特征, 而较深层则包含了更多语义信息, 更擅长定位显著物体的确切位置. 基于上述先验, 大量基于卷积神经网络的深度模型被相继提出. Hou等[11]对编码过程中每个阶段都引入了跳跃连接, 对特征图进行多层次多角度的聚合连接, 输出精确的结果. Li等[14]将粗纹理的显著图作为前景信息, 将图像边界的超像素值作为背景信息, 并将两者结合, 得到最终的结果. Qin等[15]设计了一种嵌套的U型结构, 融合了不同感受野大小的特征, 能够捕捉更多的上/下文信息. 在这些方法中, U型结构由于能够通过在基础的分类网络上建立自上而下的路径来构建丰富的特征图, 而受到了最多的关注.
尽管上述方法相对于传统方法已经取得了很大进步, 但是还有很大改进空间. 首先, 在U型结构的解码过程中, 高层语义信息逐渐传递到较浅层, 虽然较浅层获得了显著物体的语义信息, 但是位置信息同时也被稀释, 造成最终输出的预测图中并不是当前图像中最显著部分, 丢失了显著物体准确的空间定位; 其次, 低层特征拥有丰富的边界信息, 但是由于在网络的较浅层, 无法获得较大感受野, 此时如果只是简单地融合高层特征与低层特征, 是无法精确地捕捉图片中显著物体边界的, 尤其是小目标. 因此, 本文考虑在增大低层特征感受野, 提高其表征力后, 将其送入到高效的特征聚合模块中, 以此来细化显著物体的边缘.
针对上述问题, 本文研究了如何在U型结构中通过高效的特征融合解决这些问题. 本文主要贡献包括以下3个方面: 1)混合注意力模块(Mixing attention module, MAM)对来自第5个残差层的特征利用注意力机制进行显著性增强, 得到更加关注显著物体的语义特征, 同时为了解决解码过程中显著物体位置信息被不断稀释的问题, 将其作为整个解码过程中的语义指导, 不断指导解码过程中的特征聚合, 生成更加具体的显著性图. 2)增大感受野模块(Enlarged receptive field module, ERFM)可以对来自低层的特征进行处理. 低层特征的边缘细节相当丰富, 但受限于感受野, 无法获得更加全局的信息. 因此, 考虑加入ERFM, 可以在保留原有边缘细节的同时, 获得更大的感受野, 增强语义信息. 3)多层次聚合模块(Multi-level aggregation module, MLAM)是对来自经过上述2个模块生成特征进行高效聚合, 以级联方式不断提取特征中的显著部分, 细化显著物体的边缘细节, 生成最终的显著图. 具体结构如图1所示.
1. 相关工作
近年来, 大量基于全卷积神经网络深度模型[16-17]被相继提出, 受益于全卷积神经网络强大的特征提取能力, 基于深度学习的方法已经在性能方面超越了大多数基于手工特征的传统方法. 文献[18]详细总结了传统方法. 本文主要讨论基于深度学习的显著性目标检测算法.
1.1 基于注意力机制的显著性目标检测
注意力机制具有很强的特征选择能力, 能够将特征信息进行深度整合, 使得网络更加去关注所需的语义信息. 根据加工域的不同, 注意力机制可以分为空间域注意力和通道域注意力两类, 其中空间注意力模块旨在捕获特征图中任意两个空间位置之间的空间依赖性, 通道注意力模块旨在捕获任意两个通道之间的通道依赖性. 因此, 许多学者利用注意力机制进行显著性目标检测. Zhang等[19]提出一种渐近注意力引导网络的显著性方法, 在解码阶段, 级联多个注意力模块渐近地生成最终结果. Zhao等[20]考虑到不同层次的特征所具备的信息并不相同, 因此, 对来自不同层次的特征, 分别设计了不同角度的注意力模块, 并对多个结果进行融合, 得到最终的结果. Chen等[21]提出一种反向注意网络, 将粗糙的预测图反馈到中间特征层, 希望网络可以补全缺失的显著部分. Wang等[22]设计了一个金字塔注意力模块, 通过考虑多尺度注意力来增强显著特征的表征力. 上述方法都是对注意力机制的有效使用, 本文方法需要生成更加关注显著物体语义信息的高层特征, 利用注意力机制可以取得很好效果.
1.2 基于特征聚合的显著性目标检测
大多数对特征进行聚合的方法都是采用编码−解码的框架, 其中编码器用于提取多尺度特征, 解码器用于聚合特征以生成不同级别的上/下文信息. Wu等[23]对深层特征进行优化, 提高其表征力, 并利用双分支结构对特征进行聚合, 生成细化后的结果. Deng等[24]设计一种循环策略, 不断聚合来自不同层次的特征, 对网络进行细化, 增强显著信息. Wang等[25]提出一个特征打磨模块, 通过重复使用该模块, 对特征不断细化, 聚合来自不同层次的特征, 得到最终结果. 上述方法都探索了高效的特征聚合方法, 虽然有一定效果, 但是对于空间细节的捕捉仍然不够, 并且在解码过程中, 由于缺少高级语义的指导, 导致预测出的显著物体位置出现了偏移. 本文针对上述问题, 设计了多层次聚合模块, 使其能够在高级语义的指导下, 精确地定位显著物体, 并且通过级联多个、多层次聚合模块, 可以实现对边缘细节的细化.
2. 本文方法
如图1所示, 本文建立一个编码−解码结构. 首先, 选用ResNeXt101作为特征提取器, 提取图片的各层特征; 其次, 利用MAM生成一个全局语义特征, 来引导解码过程, 通过上采样、卷积和元素累加等操作, 将全局语义融合到解码器的各层特征中; 接着, 编码过程中生成的各级特征通过ERFM后, 生成具备更多边界信息的低层特征; 最后, 将各级特征一起送入MLAM进行特征的有效聚合, 通过级联方式生成最终的显著性图.
2.1 混合注意力模块
图片送入网络中, 经过编码后, 会生成一系列具备不同信息的特征. 最高层的特征具备最强的语义表征能力, 并且在解码过程中, 逐渐与低层特征进行融合, 最终得到显著图. 但是, 直接将这种语义信息进行解码融合, 会造成许多显著性细节的丢失, 原因在于高层特征的不同通道和不同空间位置对显著性计算的贡献是不同的. 具体地, 不同通道对同一对象会有不同响应, 而同一通道的不同空间位置也会包含不同的对象. 受文献[26]启发, 本文设计了混合注意力模块, 该模块分为通道注意力机制和空间注意力机制两部分, 用来捕捉不同通道和不同空间位置中最显著的部分, 利用这些最显著的语义信息, 对高层特征进行有效增强, 得到更具鲁棒性的全局语义特征. MAM模块结构见图2.
2.1.1 空间注意力机制
对于从残差块5中提取的高层特征, 首先, 将其宽、高维度展开成一维向量并进行转置, 得到二维矩阵${{\boldsymbol{X}}}\in {\bf{R}}^{H W \times C}$, $C $是该特征的通道数, $H$和$W $分别是高和宽, $HW $为高与宽相乘的数量. 然后, 经过3个并行的全连接层${{\boldsymbol{W}}_{{q}}}$、${{\boldsymbol{W}}_{{k}}}$和${{\boldsymbol{W}}_{{v}}}$对通道进行降维, 分别得到${\boldsymbol{Q}} = {\boldsymbol{X}}{{\boldsymbol{W}}_{{q}}}$、 ${\boldsymbol{K}} = {\boldsymbol{X}}{{\boldsymbol{W}}_{{k}}}$、$\boldsymbol{V} = {\boldsymbol{X}}{{\boldsymbol{W}}_{{v}}}$三个矩阵. 接着, 利用$\boldsymbol{A} = {\boldsymbol{Q}}{{\boldsymbol{K}}^{\rm{T}}}$得到相关性矩阵, 其中, $\boldsymbol A_{ij} $代表$\boldsymbol Q $中第$i $行与$\boldsymbol K $中第j行的内积, 即两个不同空间位置处向量的相关性. 并且对相关性矩阵$\boldsymbol A $的每一行利用Softmax函数进行归一化, 约束到(0, 1)内. 最后, 将相关性矩阵$\boldsymbol A $与$\boldsymbol V $相乘, 并且经过一个全连接层${{\boldsymbol{W}}_{{s}}}$对通道维度进行恢复, 得到空间显著性增强后的特征图${{\boldsymbol{X}}^{{S}}} = {\boldsymbol{AV}}{{\boldsymbol{W}}_{{s}}}$, 最终的特征表达式为:
$$ {{\boldsymbol{X}}^{{S}}} = \sigma \left( {{\boldsymbol{X}}{{\boldsymbol{W}}_{{q}}}{{\left( {{\boldsymbol{X}}{{\boldsymbol{W}}_{{k}}}} \right)}^{\rm{T}}}} \right){\boldsymbol{X}}{{\boldsymbol{W}}_{{v}}}{{\boldsymbol{W}}_{{s}}}$$ (1) 式中, ${{\boldsymbol{W}}_{{q}}},{{\boldsymbol{W}}_{{k}}},{{\boldsymbol{W}}_{{v}}} \in {\bf{R}}^{{C \times C/4}}$, ${{\boldsymbol{W}}_{{s}}} \in {\bf{R}}^{(C/4) \times C}$, $\sigma ( \cdot )$为Softmax函数.
2.1.2 通道注意力机制
通道维度的操作与上述类似, 也是对残差块5提取的特征先沿着宽、高维度展开成一维向量并转置, 得到${{\boldsymbol{X}}} \in {\bf{R}}^{H W \times C}$经过三个全连接层, 输出 ${\boldsymbol{Q}} = {\boldsymbol{X}}{{\boldsymbol{W}}_{{q}}}$, ${\boldsymbol{K}} = {\boldsymbol{X}}{{\boldsymbol{W}}_{{k}}}$, $\boldsymbol{V} = {\boldsymbol{X}}{{\boldsymbol{W}}_{{v}}}$. 考虑到降维会带来过多的信息损失, 因此本文算法没有对通道进行降维. 然后, 通过${\boldsymbol{B}} = {{\boldsymbol{K}}^{\rm{T}}}{\boldsymbol{Q}}$得到相关性矩阵, 其中$\boldsymbol B_{ij} $代表了$\boldsymbol K $中第$i $列与$\boldsymbol Q $中第$j $列的内积, 即两个不同通道向量的相关性. 同样, 需要对相关性矩阵$\boldsymbol B $的每一列利用Softmax函数进行归一化, 约束到(0, 1)内. 最后, 将$\boldsymbol V $与$\boldsymbol B $相乘且经过一个全连接层${{\boldsymbol{W}}_{{s}}}$, 得到通道显著性增强后的特征图${{\boldsymbol{X}}^{\boldsymbol{C}}} = {\boldsymbol{VB}}{{\boldsymbol{W}}_{{s}}}$, 最终的特征表达式为:
$$ {{\boldsymbol{X}}^{\boldsymbol{C}}} = {\boldsymbol{X}}{{\boldsymbol{W}}_{{v}}}\sigma \left( {{{\left( {{\boldsymbol{X}}{{\boldsymbol{W}}_{{k}}}} \right)}^{{{\rm{T}}}}}{\boldsymbol{X}}{{\boldsymbol{W}}_{{q}}}} \right){{\boldsymbol{W}}_{{s}}}$$ (2) 式中, ${{\boldsymbol{W}}_{{q}}},{{\boldsymbol{W}}_{{k}}},{{\boldsymbol{W}}_{{v}}},{{\boldsymbol{W}}_{{s}}} \in {\bf{R}}^{C \times C}$. 最后合并这两个分支的输出. 考虑到残差结构的影响, 本文将合并后的特征与输入X进行相加, 生成最终特征图${{\boldsymbol{Y}}} \in {\bf{R}}^{H W \times C}$:
$$ {\boldsymbol{Y}} = {{\boldsymbol{X}}^{{C}}} \oplus {{\boldsymbol{X}}^{{S}}} \oplus {\boldsymbol{X}}$$ (3) 式中, “$\oplus $”表示元素级的特征图相加. Y在经过转置并且将维度展开恢复后, 送入到后续的模块中.
2.2 增大感受野模块
低层特征的边缘细节非常丰富, 但由于下采样的次数有限, 感受野相对受限, 无法捕捉全局的信息. 在解码过程中, 如果仅仅是简单利用低层特征, 虽然边缘的细节信息得到利用, 但并没有充分挖掘特征的空间细节. 受文献[27]启发, 本文设计如图3所示的增大感受野模块. 低层特征经过该模块后, 在保证边缘细节不丢失的前提下, 扩大了感受野, 具备了更多空间细节.
首先, 对于特征$M \in {\bf{R}}^{C \times H \times W}$, 设计四个并行分支$({l_i},i = 1,2,3,4)$, 其中$ {l_1} $采用一个$1 \times 1$卷积, 剩下的三个分支均采用$3 \times 3$卷积, 并且对这三个分支设置不同的扩张率. 根据低层特征分辨率的不同设置不同的扩张率: 对于分辨率较低的特征设置较小的扩张率, 对于分辨率较高的特征设置较大的扩张率. 本文最大的扩张率设置为$d = 5,8, 11$, 并随着特征图的缩小而不断缩小(具体设置见第3.6节). 然后, 对四个分支输出进行通道维度拼接, 并利用一个$1 \times 1$卷积得到融合后的特征.
2.3 多层次聚合模块
在解码过程中, 高效利用每一层的特征尤为关键. 以前的研究只对高层特征与低层特征进行简单的拼接融合, 得到的结果非常粗糙. 因此, 本文设计了多层次聚合模块, 对来自不同层、不同空间尺度的特征进行有效聚合. 该模块的输入分为MAM生成的语义特征$H_1 $, 经过ERFM增强后的低层特征$L $和当前进行解码的特征$H_2 $三个部分. 图4是多层次聚合模块示意图.
整个聚合过程分为2个阶段: 第1阶段是语义特征对当前解码特征的指导融合. 首先让$H_1 $经过两个并行的$1 \times 1$卷积, 第1个分支与$H_1 $在通道维度上进行拼接融合后, 与第2个分支的结果相加完成第1次融合, 得到高层特征$H $:
$$ H = {f_{conv}}({f_{cat}}({f_{{\text{c}}onv}}({H_1}),{H_2})) \oplus {f_{conv}}({H_1}) $$ (4) 式中, ${f_{conv}}( \cdot )$指卷积操作, ${f_{cat}}( \cdot )$指通道的拼接操作. 第2阶段是第1阶段融合得到的高层特征$H $与经过ERFM增强后的低层特征L的聚合. 此阶段分为自下而上和自上而下两个并行分支. 自下而上是$H $向$L $的聚合, 此阶段$L $不变, $H $经过一次上采样和一个$1 \times 1$卷积后与L进行通道维度的拼接, 得到聚合图${X^{h \to l}}$:
$$ {X^{h \to l}} = {f_{conv}}({f_{cat}}(L,{f_{up}}(H))) $$ (5) 式中, ${f_{up}}( \cdot )$指上采样操作. 自上而下是$L $向H的聚合, 此阶段$H $不变, $L $首先经过一个并行的池化操作, 其中最大池化可以提取特征中响应值较大的信息即特征中所包含的显著信息, 平均池化可以得到特征的全局信息. 经过并行池化后, 特征$L $具备更强的表征力, 并且与H有相同的空间尺寸, 此时将其与特征$H $在通道维度上进行拼接, 并利用$1 \times 1$卷积完成融合. 然后, 对其进行上采样, 得到最终的${X^{l \to h}}$:
$$ {X^{l \to h}} = {f_{up}}({f_{conv}}({f_{cat}}(H,{f_{avg}}(L) + {f_{\max }}(L)))) $$ (6) 式中, ${f_{\max }}( \cdot )$和${f_{avg}}( \cdot )$分别代表最大池化和平均池化操作. 最后, 对两个分支得到的聚合特征也进行一次聚合:
$$ Z = {f_{conv}}({f_{cat}}({X^{l \to h}},{X^{h \to l}})) $$ (7) 3. 实验设置与结果分析
3.1 训练细节
本文代码是在Pytorch1.5.0框架下完成, 并且使用1张GeForce GTX2080Ti GPU进行训练. 训练数据使用DUTS[28]数据集中10553张图片. 使用Adam[29]优化器进行优化, 初始学习率设置为$1 \times 10^4$, 并且在每训练完成两个周期后衰减一半, 批量大小为8. 使用ResNeXt101作为特征提取器提取各层特征, 并加载在ImageNet上预训练的分类权重, 作为初始权重. 为了减少过拟合的影响, 在训练阶段, 对图片进行了随机翻转和遮挡, 并将图片缩放到$320 \times 320 $像素后, 将其随机裁剪为$288 \times 288$像素, 输入到网络中进行训练; 测试阶段, 仅将图片缩放到$288 \times 288 $像素后, 输入到网络中进行测试.
3.2 数据集
本文在6个基准数据集上进行实验, 包括DUTS-TE[28]、DUT-OMRON[30]、ECSSD[31]、HKU-IS[32]、PASCAL-S[33]和SOD[34]. 其中, DUTS-TE与训练集的10553张图片同属一个数据集, 包含5019张测试图片. DUT-OMRON是最具有挑战性的数据集, 包含5188张图片, 该数据集的难点在于背景非常复杂, 对网络预测显著目标有很大干扰作用. ECSSD相对简单, 由1000张图片组成, 其中显著目标形状与外观有很大差异. HKU-IS包含4447张图片, 其中包含多个具有不同类别或外观的显著物体. PASCAL-S包含850张图片, 图片中物体之间会出现很大程度的重叠. SOD只有300张图片, 但场景的复杂多变, 带来很大挑战.
3.3 评价指标
本文使用平均绝对误差(Mean absolute error, MAE)、${F_\beta }$(F-measure) 和${S_m}$(Structure measure)作为评价指标.
1) MAE计算预测的显著图与真实标签之间的差异:
$${\rm{MAE}} = \frac{1}{{W \times H}}\sum\limits_{x = 1}^W {\sum\limits_{y = 1}^H {\left| {{\boldsymbol{P}}(x,y) - {\boldsymbol{G}}(x,y)} \right|} } $$ (8) 式中, $\boldsymbol P$指预测的显著图, $\boldsymbol G$指真实标签值.
2)${F_\beta }$是一种经典且有效的测量指标, 通过对查准率(Precision)与查全率(Recall)设置不同的权重来计算:
$$ {F_\beta }{\text{ = }}\frac{{(1{\text{ + }}{\beta ^2}) \times {\rm{{Re} call}} \times {{\rm{Pre}}} {\rm{cision}}}}{{{\beta ^2} \times {\rm{{Pre} cision}} + {\rm{{Re} call}}}} $$ (9) 式中, ${\beta ^2}$设置为0.3.
3)${S_m}$用来考虑预测的显著图与真实标签之间的全局和局部的结构相似性, 该指标的详细介绍见文献[35].
3.4 损失函数
本文使用标准的二元交叉熵损失作为训练的损失函数:
$$ \begin{split} {L_{bce}} =\;& - \sum\limits_{(x,y)} [{\boldsymbol{G}}(x,y){\rm{lg}}({\boldsymbol{P}}(x,y)) \;+\\ &(1 - {\boldsymbol{G}}(x,y)){\rm{lg}}(1 - {\boldsymbol{P}}(x,y))]\end{split} $$ (10) 3.5 与其他算法的性能比较
本文与最新10种基于深度学习的方法进行比较, 包括U2Net[15]、PAGR[19]、RAS[21]、CPD[23]、DGRL[36]、MLMS[37]、PoolNet[38]、AFNet[39]、BASNet[40]和ITSD[41]. 为了指标的公平性, 所有指标均在同一评测代码下进行评测, 并且所有用于评测的显著图均从作者发布的模型中得出.
3.5.1 定量分析
表1、表2和表3分别列出了各算法的${F_\beta }$、MAE和${S_m}$评价指标结果. 本文方法在3项指标中均表现优异. 由表1和表3可以看出, 本文方法在指标${F_\beta }$和${S_m}$上大幅领先于其他方法, 即便是次优的ITSD算法, 在较难的数据集DUT-OMRON中, 本文也在${F_\beta }$指标上领先其0.003, ${S_m}$指标领先其0.007. 这主要得益于本文多层次聚合模块能够最大限度地保留显著物体的空间信息和边界细节. 对于表2中MAE指标, 本文方法也仅在相对较难的3个数据集上表现稍有不足, 但与第1名的差距是非常小的, 基本保持在0.001 ~ 0.002之间. 图5是各方法的查准率−查全率曲线图, 加粗实线是本文方法, 由图5可以看出, 本文算法性能的优越性.
表 1 不同方法的${F_\beta }$指标结果比较Table 1 Comparison of ${F_\beta }$ values of different models数据集 本文方法 PAGR RAS DGRL CPD MLMS PoolNet AFNet BASNet U2Net ITSD ECSSD 0.951 0.924 0.921 0.921 0.936 0.930 0.944 0.935 0.942 0.951 0.947 DUT-OMRON 0.827 0.771 0.786 0.774 0.794 0.793 0.808 0.797 0.805 0.823 0.824 PASCAL-S 0.873 0.847 0.837 0.844 0.866 0.858 0.869 0.868 0.854 0.859 0.871 HKU-IS 0.937 0.919 0.913 0.910 0.924 0.922 0.933 0.923 0.928 0.935 0.934 DUTS-TE 0.888 0.855 0.831 0.828 0.864 0.854 0.880 0.862 0.860 0.873 0.883 SOD 0.873 0.838 0.810 0.843 0.850 0.862 0.867 — 0.851 0.861 0.880 注: ${F_\beta }$值越大越好, 加粗数字为最优结果, 加下划线数字为次优结果. 表 2 不同方法的MAE指标结果比较Table 2 Comparison of MAE values of different models数据集 本文方法 PAGR RAS DGRL CPD MLMS PoolNet AFNet BASNet U2Net ITSD ECSSD 0.034 0.064 0.056 0.043 0.040 0.038 0.039 0.042 0.037 0.034 0.035 DUT-OMRON 0.058 0.071 0.062 0.062 0.056 0.060 0.056 0.057 0.056 0.054 0.061 PASCAL-S 0.065 0.089 0.104 0.072 0.074 0.069 0.075 0.069 0.076 0.074 0.072 HKU-IS 0.032 0.047 0.045 0.036 0.033 0.034 0.033 0.036 0.032 0.031 0.031 DUTS-TE 0.042 0.053 0.060 0.049 0.043 0.045 0.040 0.046 0.047 0.044 0.041 SOD 0.093 0.145 0.124 0.103 0.112 0.106 0.100 — 0.114 0.108 0.095 注: MAE值越小越好. 表 3 不同方法的${S_m}$指标结果比较Table 3 Comparison of ${S_m}$ values of different models数据集 本文方法 PAGR RAS DGRL CPD MLMS PoolNet AFNet BASNet U2Net ITSD ECSSD 0.932 0.889 0.893 0.906 0.915 0.911 0.921 0.914 0.916 0.928 0.925 DUT-OMRON 0.847 0.775 0.814 0.810 0.818 0.817 0.836 0.826 0.836 0.847 0.840 PASCAL-S 0.865 0.749 0.795 0.869 0.844 0.849 0.845 0.850 0.838 0.844 0.859 HKU-IS 0.930 0.887 0.887 0.897 0.904 0.901 0.917 0.905 0.909 0.916 0.917 DUTS-TE 0.873 0.838 0.839 0.842 0.867 0.856 0.883 0.866 0.853 0.861 0.872 SOD 0.808 0.720 0.764 0.771 0.771 0.780 0.795 — 0.772 0.786 0.809 注: ${S_{{m} } }$值越大越好. 
图 5 不同算法的查准率−查全率曲线示意图Fig. 5 Comparison of precision−recall curves of different methods3.5.2 定性分析
图6是本文方法与其他10种方法的显著性图. 由图6可以看出, 本文方法对显著信息的捕捉明显更强. 在第1行中, 即便是指标与本文最接近的ITSD也将座椅当作显著物体, 但是在人类的视觉效果上, 明亮的灯与背景的区分度更大, 本文方法因为有全局语义指导特征聚合, 可以捕捉到壁灯的显著信息. 在第2行中, 围绳与人之间有很多交叉, 即便是当前性能较好的方法也并没有将目标完整地识别出来, 而本文方法由于对低层特征进行了感受野增强, 可以捕捉目标周围更多的上/下文信息, 能够将全部目标识别出来, 但同时也存在围绳部分被识别为人的问题. 综上所述, 本文算法对于复杂背景下的物体边界并不能很好地细化. 但对于背景较简单的物体(如第5行和最后1行), 本文均能很好地预测出边界轮廓.
3.6 消融实验
表4是在数据集ECSSD上针对各模块的消融实验结果: 1)混合注意力模块. 由表4第3行可知, 当缺少混合注意力模块时, MAE指标上升了0.008, 由此可见, 利用该模块生成的全局语义特征引导特征聚合, 能够大幅提升聚合性能; 2)增大感受野模块. 由表4第4行可知, MAE指标上升了0.005, 主要是因为缺少了感受野增强, 没有充分提取低层特征的空间上/下文信息, 不利于细化边界; 3)多层次融合模块. 由表4第2行可知, 当用简单的上采样和相加操作代替该模块时, MAE上升了0.011, 说明多层次融合模块聚合方式非常高效.
表 4 消融实验结果Table 4 Results of ablation experimentMAM ERFM MLAM MAE/${F_\beta }$ — — ✓ 0.049/0.935 ✓ ✓ — 0.045/0.937 — ✓ ✓ 0.042/0.942 ✓ — ✓ 0.039/0.944 ✓ ✓ ✓ 0.034/0.951 注: MAE值越小越好, 加粗字体为最优结果, “✓”为使用指定模块. 表5是对ERFM模块中, 不同扩张率设置的对比实验结果. 表5中不同设置组合从左向右依次对应不同分辨率的特征图(见图3), 即左边第1组扩张率对应分辨率最大的特征图, 最后1组扩张率对应分辨率最小的特征图. 表5的第4行是本文方法的设置. 由表5第1行可以看出, 当扩张率全部设置为(1, 3, 5)时, 与本文方法相比, MAE上升了0.005, 而随着本文方法对分辨率较高的特征图分配更大的扩张率时, MAE的指标不断降低. 实验结果表明, 在本文方法中, 扩张率的选择是有效的.
表 5 ERFM模块中, 不同扩张率设置的对比实验Table 5 Comparative experiment of different dilation rate configurations in ERFM扩张率的不同设置组合 MAE/${F_\beta }$ (1, 3, 5), (1, 3, 5), (1, 3, 5), (1, 3, 5) 0.039/0.946 (1, 3, 5), (1, 3, 5), (3, 5, 7), (1, 3, 5) 0.037/0.948 (1, 3, 5), (4, 6, 8), (3, 5, 7), (1, 3, 5) 0.036/0.950 (5, 8, 11), (4, 6, 8), (3, 5, 7), (1, 3, 5) 0.034/0.951 表6是对MLAM模块第2阶段中, 自上而下和自下而上两个分支在数据集ECSSD上的消融实验结果. 由表6第1行可知, 当只使用自下而上分支时, 相比两个并行分支均使用时, MAE上升了0.007; 而只使用自上而下分支时, 上升了0.006. 由此可见, 本文方法将两个分支并行使用的方式是有效的, 能够对精度有所提升.
表 6 MLAM模块中, 两个分支的消融实验Table 6 Ablation experiment of two branches in MLAM自下而上分支 自上而下分支 MAE/${F_\beta }$ ✓ — 0.041/0.940 — ✓ 0.040/0.946 ✓ ✓ 0.034/0.951 表7是对MAM中, 注意力模块位置关系的消融实验结果. 前2行是将两个模块串联并考虑其先后位置, 第3行是两个模块并行即本文方法. 当通道注意力位置在前时, 与本文方法相比, MAE上升了0.002; 当空间注意力位置在前时, MAE上升了0.004. 该实验结果验证了本文将两个模块设置成并行的有效性.
表 7 MAM模块中, 注意力模块位置关系的消融实验Table 7 Ablation experiment on the position relationship of attention module in MAM注意力模块之间的位置关系 MAE/${F_\beta }$ 通道注意力在前 0.036/0.947 空间注意力在前 0.038/0.944 并行放置 (本文方法) 0.034/0.951 4. 结束语
本文提出一种基于语义引导特征聚合的显著性目标检测算法, 主要包括混合注意力模块、增大感受野模块和多层次融合模块3个模块. MAM能够生成更佳的语义特征, 用来指导解码过程中的特征融合, 使得聚合的特征能够更好地定位显著物体; ERFM能够丰富低层特征所具备的上/下文信息, 并将增强后的特征输入到MLAM中; MLAM利用MAM生成的语义信息, 对当前解码的特征和ERFM输出的低层特征进行指导融合, 并最终以级联方式逐步恢复边界细节, 生成最终的显著图. 本文与目前流行的10种算法在6个基准数据集上进行了实验比较, 由可视化图6可以看出, 本文算法能够有效地保留显著物体的空间位置信息, 并且边缘也得到了很好细化. 实验结果也验证了本文算法具有领先性能.
-
表 2 围捕机器人常见运动学与动力学约束
Table 2 Common kinematic and dynamic constraints of hunting robots
运动学与动力学约束 约束描述 最小、最大运动速度约束 围捕机器人速度须介于最小速度和最大速度之间 最小、最大运动加速度约束 围捕机器人加速度须介于最小加速度和最大加速度之间 最小步长约束 围捕机器人轨迹从当前状态到改变行进方向的下一状态之间的直线运动距离须大于最小步长 最小转弯半径约束 围捕机器人轨迹的转弯半径须大于最小转弯半径 最大航偏角约束 围捕机器人运动过程中的航偏角须小于最大航偏角 
下载: 导出CSV
表 3 多机器人协同围捕任务分配方法总结
Table 3 Summary of multi-robot cooperative hunting task allocation methods
任务分配算法 架构特点 优点 缺点 贪婪算法[91] 集中式 实时性高, 简单易实现 局部最优解, “死锁”分配 匈牙利算法[74−75] 集中式 效率高, 局部最优解, 简单易实现 不适合大规模任务分配场景, 只适合一对一分配 遗传算法[81] 集中式 全局搜索能力, 适应性和鲁棒性, 并行性 计算成本高, 难以收敛, 参数设置敏感, 编码方式选择困难 粒子群算法[88, 90] 集中式 快速收敛, 简单易实现, 适应性强 过早收敛, 参数敏感 契约合同网络方法[95−96] 分布式 分布式协作, 动态灵活性, 适应性 高复杂性和通讯开销大, 信任建立和信息共享困难 拍卖算法[106−107] 分布式 分布式协作, 灵活性, 任务动态调整 竞争激烈可能导致效率下降, 存在信息不对称, 对于大规模问题不再适用
下载: 导出CSV
表 4 生物启发式神经网络方法分析
Table 4 Biologically inspired neural network method analysis
领域 内容 优点 缺点 路径规划[125−126, 128] 解决机器人路径规划、防碰撞问题 实时性好 仅适用于二维平面 多机围捕[137] 首次将围捕问题与生物神经网络模型对应 兼顾围捕任务目标搜索、任务分配过程 模型设计复杂 路径规划[129] 引入假想非障碍物相邻点, 考虑转角因素 解决路径错判问题 计算效率低 路径规划[130] 模型考虑相邻神经元权值影响 使模型更具网络特性 增加碰撞检测计算节点 路径规划[131−132] 决策项考虑洋流影响 使方法更贴合实际环境 缺乏高效任务分配方法 多机围捕[133−134] 决策项考虑洋流影响 使方法更贴合实际环境 规划路径可能并非最优解 多机围捕[135] 设计基于协商思想的任务分配方法 增加围捕任务效率 仅适用于低维环境 多机围捕[136] 将方法拓展至三维环境 增强方法的可拓展性 奖励函数设计复杂、计算效率低
下载: 导出CSV
-
[1] Motes J, Chen T, Bretl T, Aguirre M M, Amato N M. Hypergraph-based multi-robot task and motion planning. IEEE Transactions on Robotics, 2023, 39(5): 4166−4186 doi: 10.1109/TRO.2023.3297011 [2] Liu W H, Hu J W, Zhang H, Wang M Y, Xiong Z H. A novel graph-based motion planner of multi-mobile robot systems with formation and obstacle constraints. IEEE Transactions on Robotics, 2024, 40: 714−728 doi: 10.1109/TRO.2023.3339989 [3] Robin C, Lacroix S. Multi-robot target detection and tracking: Taxonomy and survey. Autonomous Robots, 2016, 40(4): 729−760 doi: 10.1007/s10514-015-9491-7 [4] 寇立伟, 项基. 基于输出反馈线性化的多移动机器人目标包围控制. 自动化学报, 2022, 48(5): 1285−1291Kou Li-Wei, Xiang Ji. Target fencing control of multiple mobile robots using output feedback linearization. Acta Automatica Sinica, 2022, 48(5): 1285−1291 [5] Duberg D, Jensfelt P. UFOExplorer: Fast and scalable sampling-based exploration with a graph-based planning structure. IEEE Robotics and Automation Letters, 2022, 7(2): 2487−2494 doi: 10.1109/LRA.2022.3142923 [6] Li Q B, Lin W Z, Liu Z, Prorok A. Message-aware graph attention networks for large-scale multi-robot path planning. IEEE Robotics and Automation Letters, 2021, 6(3): 5533−5540 doi: 10.1109/LRA.2021.3077863 [7] Hu J W, Xie L H, Lum K Y, Xu J. Multiagent information fusion and cooperative control in target search. IEEE Transactions on Control Systems Technology, 2013, 21(4): 1223−1235 doi: 10.1109/TCST.2012.2198650 [8] Lin Y C, Saripalli S. Sampling-based path planning for UAV collision avoidance. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(11): 3179−3192 doi: 10.1109/TITS.2017.2673778 [9] Phung M D, Ha Q P. Safety-enhanced UAV path planning with spherical vector-based particle swarm optimization. Applied Soft Computing, 2021, 107: Article No. 107376 doi: 10.1016/j.asoc.2021.107376 [10] 彭辉, 沈林成, 霍霄华. 多UAV协同区域覆盖搜索研究. 系统仿真学报, 2007, 19(11): 2472−2476 doi: 10.3969/j.issn.1004-731X.2007.11.022Peng Hui, Shen Lin-Cheng, Huo Xiao-Hua. Research on multiple UAV cooperative area coverage searching. Journal of System Simulation, 2007, 19(11): 2472−2476 doi: 10.3969/j.issn.1004-731X.2007.11.022 [11] 王洪民, 田家强, 韦凌云, 庄育锋. 多运动目标的多无人机协同搜索追踪策略. 控制理论与应用, 2021, 38(7): 971−978 doi: 10.7641/CTA.2021.00565Wang Hong-Min, Tian Jia-Qiang, Wei Ling-Yun, Zhuang Yu-Feng. Multi-unmanned aerial vehicles cooperative searching and tracking strategy for multiple moving targets. Control Theory & Applications, 2021, 38(7): 971−978 doi: 10.7641/CTA.2021.00565 [12] Sun L, Baek S, Pack D. Distributed probabilistic search and tracking of agile mobile ground targets using a network of unmanned aerial vehicles. Human Behavior Understanding in Networked Sensing: Theory and Applications of Networks of Sensors. Cham: Springer, 2014. 301–319 [13] 轩永波, 黄长强, 吴文超, 王勇, 翁兴伟, 李望西. 多无人机协同搜索随机目标决策. 控制与决策, 2013, 28(5): 711−715Xuan Yong-Bo, Huang Chang-Qiang, Wu Wen-Chao, Wang Yong, Weng Xing-Wei, Li Wang-Xi. Cooperative search stategies of multi-UAVs for random targets. Control and Decision, 2013, 28(5): 711−715 [14] El-Hady Kassem M A, El-Hadidy M A A. Optimal multiplicative Bayesian search for a lost target. Applied Mathematics and Computation, 2014, 247: 795−802 doi: 10.1016/j.amc.2014.09.039 [15] Xu X L, Yang L X, Meng W, Cai Q Q, Fu M Y. Multi-agent coverage search in unknown environments with obstacles: A survey. In: Proceedings of the Chinese Control Conference (CCC). Guangzhou, China: IEEE, 2019. 2317–2322 [16] 张世勇, 张雪波, 苑晶, 方勇纯. 旋翼无人机环境覆盖与探索规划方法综述. 控制与决策, 2022, 37(3): 513−529Zhang Shi-Yong, Zhang Xue-Bo, Yuan Jing, Fang Yong-Chun. A survey on coverage and exploration path planning with multi-rotor micro aerial vehicles. Control and Decision, 2022, 37(3): 513−529 [17] Barrientos A, Colorado J, del Cerro J, Martinez A, Rossi C, Sanz D, et al. Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots. Journal of Field Robotics, 2011, 28(5): 667−689 doi: 10.1002/rob.20403 [18] Bast H, Hert S. The area partitioning problem. In: Proceedings of the 12th Annual Canadian Conference on Computational Geometry (CCCG-00). Fredericton, Canada: University of New Brunswick, 2000. 163–171 [19] Chen J C, Du C L, Zhang Y, Han P C, Wei W. A clustering-based coverage path planning method for autonomous heterogeneous UAVs. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(12): 25546−25556 doi: 10.1109/TITS.2021.3066240 [20] Mei Y G, Lu Y H, Hu Y C, Lee C S G. Deployment of mobile robots with energy and timing constraints. IEEE Transactions on Robotics, 2006, 22(3): 507−522 doi: 10.1109/TRO.2006.875494 [21] Bouzid Y, Bestaoui Y, Siguerdidjane H. Quadrotor-UAV optimal coverage path planning in cluttered environment with a limited onboard energy. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vancouver, Canada: IEEE, 2017. 979–984 [22] 轩永波, 黄长强, 吴文超, 于文波, 王勇, 翁兴伟. 运动目标的多无人机编队覆盖搜索决策. 系统工程与电子技术, 2013, 35(3): 539−544 doi: 10.3969/j.issn.1001-506X.2013.03.15Xuan Yong-Bo, Huang Chang-Qiang, Wu Wen-Chao, Yu Wen-Bo, Wang Yong, Weng Xing-Wei. Coverage search strategies for moving targets using multiple unmanned aerial vehicle teams. Systems Engineering and Electronics, 2013, 35(3): 539−544 doi: 10.3969/j.issn.1001-506X.2013.03.15 [23] 王勋, 姚佩阳, 梅权. 多无人机协同运动目标搜索问题研究. 电光与控制, 2016, 23(8): 18−22 doi: 10.3969/j.issn.1671-637X.2016.08.005Wang Xun, Yao Pei-Yang, Mei Quan. On multi-UAV cooperation for moving target searching. Electronics Optics & Control, 2016, 23(8): 18−22 doi: 10.3969/j.issn.1671-637X.2016.08.005 [24] 曾国奇, 白宇, 林伟, 丁文锐. 地面运动目标的多UAV协同搜索方法. 系统工程与电子技术, 2018, 40(7): 1498−1505 doi: 10.3969/j.issn.1001-506X.2018.07.13Zeng Guo-Qi, Bai Yu, Lin Wei, Ding Wen-Rui. Multi-UAV cooperative search method for ground moving targets. Systems Engineering and Electronics, 2018, 40(7): 1498−1505 doi: 10.3969/j.issn.1001-506X.2018.07.13 [25] Nam L H, Huang L L, Li X J, Xu J F. An approach for coverage path planning for UAVs. In: Proceedings of the IEEE 14th International Workshop on Advanced Motion Control (AMC). Auckland, New Zealand: IEEE, 2016. 411–416 [26] Xing C, Wang J L, Xu Y M. Overlap analysis of the images from unmanned aerial vehicles. In: Proceedings of the International Conference on Electrical and Control Engineering. Wuhan, China: IEEE, 2010. 1459–1462 [27] Valente J, Sanz D, del Cerro J, Barrientos A, de Frutos M Á. Near-optimal coverage trajectories for image mosaicing using a mini quad-rotor over irregular-shaped fields. Precision Agriculture, 2013, 14(1): 115−132 doi: 10.1007/s11119-012-9287-0 [28] di Franco C, Buttazzo G. Coverage path planning for UAVs photogrammetry with energy and resolution constraints. Journal of Intelligent & Robotic Systems, 2016, 83(3): 445−462 [29] Cabreira T M, di Franco C, Ferreira P R, Buttazzo G C. Energy-aware spiral coverage path planning for UAV photogrammetric applications. IEEE Robotics and Automation Letters, 2018, 3(4): 3662−3668 doi: 10.1109/LRA.2018.2854967 [30] 赵发, 綦秀利, 余晓晗, 张所娟, 李本凌. 基于多无人机自主协作任务规划的区域搜索与目标围捕问题研究. 电子技术与软件工程, 2022(11): 141−146Zhao Fa, Qi Xiu-Li, Yu Xiao-Han, Zhang Suo-Juan, Li Ben-Ling. Research on area search and target rounding problem based on multi-UAV autonomous collaborative mission planning. Electronic Technology & Software Engineering, 2022(11): 141−146 [31] 刘云辉, 石永康. 未知环境下多无人机协同搜索与围捕策略研究. 现代电子技术, 2023, 46(6): 98−104Liu Yun-Hui, Shi Yong-Kang. Research on cooperative search and round up strategy of multiple-UAV in unknown environment. Modern Electronics Technique, 2023, 46(6): 98−104 [32] Sauter J, Matthews R, Parunak H, Brueckner S. Demonstration of digital pheromone swarming control of multiple unmanned air vehicles. In: Proceedings of the Infotech@Aerospace. Arlington, Virginia: AIAA, 2005. Article No. 7046 [33] Aznar F, Pujol M, Rizo R, Rizo C. Modelling multi-rotor UAVs swarm deployment using virtual pheromones. PLoS One, 2018, 13(1): Article No. e0190692 doi: 10.1371/journal.pone.0190692 [34] 甄子洋. 无人机集群作战协同控制与决策. 北京: 国防工业出版社, 2022.Zhen Zi-Yang. Cooperative Control and Decision of UAV Swarm Operations. Beijing: National Defense Industry Press, 2022. [35] 高炳霞, 张波涛, 王坚, 吴秋轩. 一种基于概率地图的移动机器人最优期望时间目标搜索. 控制与决策, 2022, 37(4): 944−952Gao Bing-Xia, Zhang Bo-Tao, Wang Jian, Wu Qiu-Xuan. An expected-time optimal target search method based on probabilistic maps. Control and Decision, 2022, 37(4): 944−952 [36] Hu J W, Xie L H, Xu J, Xu Z. Multi-agent cooperative target search. Sensors, 2014, 14(6): 9408−9428 doi: 10.3390/s140609408 [37] Sharma R, Yoder J, Kwon H, Pack D. Vision based mobile target geo-localization and target discrimination using Bayes detection theory. Distributed Autonomous Robotic Systems: The 11th International Symposium. Berlin, Heidelberg: Springer, 2014. 59–71 [38] 黄书召, 田军委, 乔路, 王沁, 苏宇. 基于改进遗传算法的无人机路径规. 计算机应用, 2021, 41(2): 390−397Huang Shu-Zhao, Tian Jun-Wei, Qiao Lu, Wang Qin, Su Yu. Unmanned aerial vehicle path planning based on improved genetic algorithm. Journal of Computer Applications, 2021, 41(2): 390−397 [39] 朱梦圆, 吕娜, 陈柯帆, 钟赟, 刘创, 高维廷. 航空集群协同搜索马尔科夫运动目标方法. 系统工程与电子技术, 2019, 41(9): 2041−2047 doi: 10.3969/j.issn.1001-506X.2019.09.17Zhu Meng-Yuan, Lv Na, Chen Ke-Fan, Zhong Yun, Liu Chuang, Gao Wei-Ting. Collaborative aeronautic swarm search of Markov moving targets. Systems Engineering and Electronics, 2019, 41(9): 2041−2047 doi: 10.3969/j.issn.1001-506X.2019.09.17 [40] Xin Y, Liang H W, Mei T, Huang R L, Du M B, Sun C, et al. A new occupancy grid of the dynamic environment for autonomous vehicles. In: Proceedings of the IEEE Intelligent Vehicles Symposium Proceedings. Dearborn, USA: IEEE, 2014. 787–792 [41] Sharifi F, Mirzaei M, Zhang Y M, Gordon B W. Cooperative multi-vehicle search and coverage problem in an uncertain environment. Unmanned Systems, 2015, 3(1): 35−47 doi: 10.1142/S230138501550003X [42] 沈东, 魏瑞轩, 祁晓明, 关旭宁. 基于MTPM和DPM的多无人机协同广域目标搜索滚动时域决策. 自动化学报, 2014, 40(7): 1391−1403Shen Dong, Wei Rui-Xuan, Qi Xiao-Ming, Guan Xu-Ning. Receding horizon decision method based on MTPM and DPM for multi-UAVs cooperative large area target search. Acta Automatica Sinica, 2014, 40(7): 1391−1403 [43] Steyer S, Tanzmeister G, Wollherr D. Grid-based environment estimation using evidential mapping and particle tracking. IEEE Transactions on Intelligent Vehicles, 2018, 3(3): 384−396 doi: 10.1109/TIV.2018.2843130 [44] Lum C, Rysdyk R, Pongpunwattana A. Occupancy based map searching using heterogeneous teams of autonomous vehicles. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit. Keystone, Colorado: AIAA, 2006. Article No. 6196 [45] Erignac C. An exhaustive swarming search strategy based on distributed pheromone maps. In: Proceedings of the AIAA Infotech@Aerospace 2007 Conference and Exhibit. Rohnert Park, California: AIAA, 2007. Article No. 2822 [46] 范衠, 孙福赞, 马培立, 李文姬, 石泽, 王诏君, 等. 基于共识主动性的群体机器人目标搜索与围捕. 北京理工大学学报, 2022, 42(2): 158−167Fan Zhun, Sun Fu-Zan, Ma Pei-Li, Li Wen-Ji, Shi Ze, Wang Zhao-Jun, et al. Stigmergy-based swarm robots for target search and trapping. Transactions of Beijing Institute of Technology, 2022, 42(2): 158−167 [47] 彭辉, 沈林成, 朱华勇. 基于分布式模型预测控制的多UAV协同区域搜索. 航空学报, 2010, 31(3): 593−601Peng Hui, Shen Lin-Cheng, Zhu Hua-Yong. Multiple UAV cooperative area search based on distributed model predictive control. Acta Aeronautica et Astronautica Sinica, 2010, 31(3): 593−601 [48] 彭辉. 分布式多无人机协同区域搜索中的关键问题研究 [博士学位论文], 国防科学技术大学, 中国, 2009.Peng Hui. A Study of Key Issues in Distributed Multi-UAV Collaborative Area Searches [Ph.D. dissertation], National University of Defense Technology, China, 2009. [49] Delight M, Ramakrishnan S, Zambrano T, MacCready T. Developing robotic swarms for ocean surface mapping. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Stockholm, Sweden: IEEE, 2016. 5309–5315 [50] Khan A, Yanmaz E, Rinner B. Information exchange and decision making in micro aerial vehicle networks for cooperative search. IEEE Transactions on Control of Network Systems, 2015, 2(4): 335−347 doi: 10.1109/TCNS.2015.2426771 [51] Shen Y K, Wei C, Sun Y B, Duan H B. Bird flocking inspired methods for multi-UAV cooperative target search. IEEE Transactions on Circuits and Systems II: Express Briefs, 2024, 71(2): 702−706 [52] 郑伟铭, 周贞文, 徐扬, 罗德林. 针对运动目标的多无人机协同鸽群优化搜索方法. 控制理论与应用, 2023, 40(4): 624−632 doi: 10.7641/CTA.2022.10466Zheng Wei-Ming, Zhou Zhen-Wen, Xu Yang, Luo De-Lin. Multi-UAV cooperative pigeon-inspired optimization search method for moving targets. Control Theory & Applications, 2023, 40(4): 624−632 doi: 10.7641/CTA.2022.10466 [53] 周鹤翔, 徐扬, 罗德林. 针对动态目标的多无人机协同组合差分进化搜索方法. 控制与决策, 2023, 38(11): 3128−3136Zhou He-Xiang, Xu Yang, Luo De-Lin. A composite differential evolution algorithm for multi-UAV cooperative dynamic target search. Control and Decision, 2023, 38(11): 3128−3136 [54] 岳伟, 辛弘, 林彬, 刘中常, 李莉莉. MAUV协同搜索多智能目标的路径规划. 控制理论与应用, 2022, 39(11): 2065−2073 doi: 10.7641/CTA.2022.11140Yue Wei, Xin Hong, Lin Bin, Liu Zhong-Chang, Li Li-Li. Path planning of MAUV cooperative search for multi-intelligent targets. Control Theory & Applications, 2022, 39(11): 2065−2073 doi: 10.7641/CTA.2022.11140 [55] Zhen Z Y, Chen Y, Wen L D, Han B. An intelligent cooperative mission planning scheme of UAV swarm in uncertain dynamic environment. Aerospace Science and Technology, 2020, 100: Article No. 105826 doi: 10.1016/j.ast.2020.105826 [56] 过劲劲, 齐俊桐, 王明明, 吴冲, 徐士博. 未知区域中四旋翼无人机集群协同搜索与围捕算法. 北京航空航天大学学报, 2023, 49(8): 2001−2010Guo Jin-Jin, Qi Jun-Tong, Wang Ming-Ming, Wu Chong, Xu Shi-Bo. A cooperative search and encirclement algorithm for quadrotors in unknown areas. Journal of Beijing University of Aeronautics and Astronautics, 2023, 49(8): 2001−2010 [57] 黄依新, 相晓嘉, 周晗, 闫超, 常远, 孙懿豪. 基于概率图模型的多机器人自组织协同围捕方法. 控制理论与应用, 2023, 40(12): 2225−2235 doi: 10.7641/CTA.2023.30245Huang Yi-Xin, Xiang Xiao-Jia, Zhou Han, Yan Chao, Chang Yuan, Sun Yi-Hao. Multi-robot self-organizing cooperative pursuit method based on probabilistic graphical model. Control Theory & Applications, 2023, 40(12): 2225−2235 doi: 10.7641/CTA.2023.30245 [58] Jiang P Y, Ergu D, Liu F Y, Cai Y, Ma B. A review of Yolo algorithm developments. Procedia Computer Science, 2022, 199: 1066−1073 doi: 10.1016/j.procs.2022.01.135 [59] Wu Z, Tang W L, Chen S J, Jiang L, Fu C W. CIA-SSD: Confident IoU-aware single-stage object detector from point cloud. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. AAAI, 2021. 3555–3562 [60] Zhang H K, Chang H, Ma B P, Wang N Y, Chen X L. Dynamic R-CNN: Towards high quality object detection via dynamic training. In: Proceedings of the 16th European Conference. Glasgow, UK: Springer, 2020. 260–275 [61] Qiao L M, Zhao Y X, Li Z Y, Qiu X, Wu J N, Zhang C. DeFRCN: Decoupled faster R-CNN for few-shot object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). Montreal, Canada: IEEE, 2021. 8681–8690 [62] Shi J F, Zhang T Q, He G H, Hao F. A review of abnormal personnel behavior detection based on deep learning. In: Proceedings of the 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). Queenstown, New Zealand: IEEE, 2023. 1–5 [63] Sabokrou M, Fayyaz M, Fathy M, Moayed Z, Klette R. Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes. Computer Vision and Image Understanding, 2018, 172: 88−97 doi: 10.1016/j.cviu.2018.02.006 [64] Pang G S, Yan C, Shen C H, van den Hengel A, Bai X. Self-trained deep ordinal regression for end-to-end video anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle, USA: IEEE, 2020. 12173–12182 [65] Ramachandra B, Jones M J, Vatsavai R R. Learning a distance function with a Siamese network to localize anomalies in videos. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV). Snowmass, USA: IEEE, 2020. 2598–2607 [66] Wu P, Liu J, Shen F. A deep one-class neural network for anomalous event detection in complex scenes. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(7): 2609−2622 [67] Samuel R D J, Fenil E, Manogaran G, Vivekananda G N, Thanjaivadivel T, Jeeva S, et al. Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM. Computer Networks, 2019, 151: 191−200 doi: 10.1016/j.comnet.2019.01.028 [68] Ravanbakhsh M, Nabi M, Sangineto E, Marcenaro L, Regazzoni C, Sebe N. Abnormal event detection in videos using generative adversarial nets. In: Proceedings of the IEEE International Conference on Image Processing (ICIP). Beijing, China: IEEE, 2017. 1577–1581 [69] Ravanbakhsh M, Sangineto E, Nabi M, Sebe N. Training adversarial discriminators for cross-channel abnormal event detection in crowds. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV). Waikoloa, USA: IEEE, 2019. 1896–1904 [70] Yang Q, Fu S, Wang H G, Fang H. Machine-learning-enabled cooperative perception for connected autonomous vehicles: Challenges and opportunities. IEEE Network, 2021, 35(3): 96−101 doi: 10.1109/MNET.011.2000560 [71] An Q E, Wang Y L, Shen Y. Sensor deployment for visual 3D perception: A perspective of information gains. IEEE Sensors Journal, 2021, 21(6): 8464−8478 doi: 10.1109/JSEN.2021.3050325 [72] 李新德, 杨伟东, Dezert Jean. 一种飞机图像目标多特征信息融合识别方法. 自动化学报, 2012, 38(8): 1298−1307 doi: 10.3724/SP.J.1004.2012.01298Li Xin-De, Yang Wei-Dong, Dezert Jean. An airplane image target's multi-feature fusion recognition method. Acta Automatica Sinica, 2012, 38(8): 1298−1307 doi: 10.3724/SP.J.1004.2012.01298 [73] 王峰, 黄子路, 韩孟臣, 邢立宁, 王凌. 基于KnCMPSO算法的异构无人机协同多任务分配. 自动化学报, 2023, 49(2): 399−414Wang Feng, Huang Zi-Lu, Han Meng-Chen, Xing Li-Ning, Wang Ling. A knee point based coevolution multi-objective particle swarm optimization algorithm for heterogeneous UAV cooperative multi-task allocation. Acta Automatica Sinica, 2023, 49(2): 399−414 [74] Liao J, Liu C, Liu H H T. Model predictive control for cooperative hunting in obstacle rich and dynamic environments. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Xi'an, China: IEEE, 2021. 5089–5095 [75] Zhu J L, Fang B F. Emotional robot pursuit task allocation algorithm based on emotional constraint. In: Proceedings of the 3rd International Conference on Robotics and Artificial Intelligence. Shanghai, China: ACM, 2017. 110–115 [76] Xia G Q, Sun X X, Xia X M. Multiple task assignment and path planning of a multiple unmanned surface vehicles system based on improved self-organizing mapping and improved genetic algorithm. Journal of Marine Science and Engineering, 2021, 9(6): Article No. 556 doi: 10.3390/jmse9060556 [77] 杜永浩, 邢立宁, 姚锋, 陈盈果. 航天器任务调度模型、算法与通用求解技术综述. 自动化学报, 2021, 47(12): 2715−2741Du Yong-Hao, Xing Li-Ning, Yao Feng, Chen Ying-Guo. Survey on models, algorithms and general techniques for spacecraft mission scheduling. Acta Automatica Sinica, 2021, 47(12): 2715−2741 [78] Holland J H. Genetic algorithms. Scientific American, 1992, 267(1): 66−72 doi: 10.1038/scientificamerican0792-66 [79] Liau Y Y, Ryu K. Genetic algorithm-based task allocation in multiple modes of human-robot collaboration systems with two cobots. The International Journal of Advanced Manufacturing Technology, 2022, 119(11−12): 7291−7309 doi: 10.1007/s00170-022-08670-x [80] Saeedvand S, Aghdasi H S, Baltes J. Robust multi-objective multi-humanoid robots task allocation based on novel hybrid metaheuristic algorithm. Applied Intelligence, 2019, 49(12): 4097−4127 doi: 10.1007/s10489-019-01475-8 [81] Ye F, Chen J, Tian Y, Jiang T. Cooperative multiple task assignment of heterogeneous UAVs using a modified genetic algorithm with multi-type-gene chromosome encoding strategy. Journal of Intelligent & Robotic Systems, 2020, 100(2): 615−627 [82] Zhou X, Wang H M, Ding B, Hu T J, Shang S N. Balanced connected task allocations for multi-robot systems: An exact flow-based integer program and an approximate tree-based genetic algorithm. Expert Systems With Applications, 2019, 116: 10−20 doi: 10.1016/j.eswa.2018.09.001 [83] Kruekaew B, Kimpan W. Multi-objective task scheduling optimization for load balancing in cloud computing environment using hybrid artificial bee colony algorithm with reinforcement learning. IEEE Access, 2022, 10: 17803−17818 doi: 10.1109/ACCESS.2022.3149955 [84] Pendharkar P C. An ant colony optimization heuristic for constrained task allocation problem. Journal of Computational Science, 2015, 7: 37−47 doi: 10.1016/j.jocs.2015.01.001 [85] de Oca M A M, Stutzle T, Birattari M, Dorigo M. Frankenstein's PSO: A composite particle swarm optimization algorithm. IEEE Transactions on Evolutionary Computation, 2009, 13(5): 1120−1132 doi: 10.1109/TEVC.2009.2021465 [86] Geng N, Chen Z T, Nguyen Q A, Gong D W. Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints. Complex & Intelligent Systems, 2021, 7(2): 873−890 [87] Wei C Y, Ji Z, Cai B L. Particle swarm optimization for cooperative multi-robot task allocation: A multi-objective approach. IEEE Robotics and Automation Letters, 2020, 5(2): 2530−2537 doi: 10.1109/LRA.2020.2972894 [88] Zhu Z X, Tang B W, Yuan J P. Multirobot task allocation based on an improved particle swarm optimization approach. International Journal of Advanced Robotic Systems, 2017, 14(3): Article No. 1729881417710312 [89] Kong X J, Gao Y P, Wang T Y, Liu J H, Xu W T. Multi-robot task allocation strategy based on particle swarm optimization and greedy algorithm. In: Proceedings of the IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). Chongqing, China: IEEE, 2019. 1643–1646 [90] 李炜, 张伟. 基于粒子群算法的多无人机任务分配方法. 控制与决策, 2010, 25(9): 1359−1363Li Wei, Zhang Wei. Method of tasks allocation of multi-UAVs based on particles swarm optimization. Control and Decision, 2010, 25(9): 1359−1363 [91] Pierson A, Wang Z J, Schwager M. Intercepting rogue robots: An algorithm for capturing multiple evaders with multiple pursuers. IEEE Robotics and Automation Letters, 2017, 2(2): 530−537 doi: 10.1109/LRA.2016.2645516 [92] 翟政, 何明, 徐鹏, 彭志新. 基于市场机制的无人集群任务分配研究综述. 计算机应用研究, 2023, 40(7): 1921−1928Zhai Zheng, He Ming, Xu Peng, Peng Zhi-Xin. Research review of task allocation for unmanned swarm based on market mechanism. Application Research of Computers, 2023, 40(7): 1921−1928 [93] 李娟, 张昆玉. 基于改进合同网算法的异构多AUV协同任务分配. 水下无人系统学报, 2017, 25(6): 418−423Li Juan, Zhang Kun-Yu. Heterogeneous multi-AUV cooperative task allocation based on improved contract net algorithm. Journal of Unmanned Undersea Systems, 2017, 25(6): 418−423 [94] Zhen Z Y, Wen L D, Wang B L, Hu Z, Zhang D M. Improved contract network protocol algorithm based cooperative target allocation of heterogeneous UAV swarm. Aerospace Science and Technology, 2021, 119: Article No. 107054 doi: 10.1016/j.ast.2021.107054 [95] 李瑞珍, 杨惠珍, 萧丛杉. 基于动态围捕点的多机器人协同策略. 控制工程, 2019, 26(3): 510−514Li Rui-Zhen, Yang Hui-Zhen, Xiao Cong-Shan. Cooperative hunting strategy for multi-mobile robot systems based on dynamic hunting points. Control Engineering of China, 2019, 26(3): 510−514 [96] 付光远, 李源. 多移动机器人动态联盟围捕策略. 计算机应用, 2019, 39(S1): 1−7Fu Guang-Yuan, Li Yuan. Dynamic alliance pursuit strategy for multiple mobile robots. Journal of Computer Applications, 2019, 39(S1): 1−7 [97] 吴蔚楠, 崔乃刚, 郭继峰. 基于目标信息估计的分布式局部协调任务分配方法. 控制理论与应用, 2018, 35(4): 566−576 doi: 10.7641/CTA.2017.70172Wu Wei-Nan, Cui Nai-Gang, Guo Ji-Feng. Distributed task assignment method based on local information consensus and target estimation. Control Theory & Applications, 2018, 35(4): 566−576 doi: 10.7641/CTA.2017.70172 [98] 王孟阳, 张栋, 唐硕, 许斌, 赵军民. 基于动态联盟策略的无人机集群在线任务规划方法. 兵工学报, 2023, 44(8): 2207−2223Wang Meng-Yang, Zhang Dong, Tang Shuo, Xu Bin, Zhao Jun-Min. UAV swarm on-line mission planning method based on dynamic allocation strategy. Acta Armamentarii, 2023, 44(8): 2207−2223 [99] Sujit P B, Manathara J G, Ghose D, de Sousa J B. Decentralized multi-UAV coalition formation with limited communication ranges. Handbook of Unmanned Aerial Vehicles. Dordrecht: Springer, 2015. 2021–2048 [100] 陈璞, 严飞, 刘钊, 成果达. 通信约束下异构多无人机任务分配方法. 航空学报, 2021, 42(8): Article No. 525844 doi: 10.7527/S1000-6893.2021.25844Chen Pu, Yan Fei, Liu Zhao, Cheng Guo-Da. Communication-constrained task allocation of heterogeneous UAVs. Acta Aeronautica et Astronautica Sinica, 2021, 42(8): Article No. 525844 doi: 10.7527/S1000-6893.2021.25844 [101] Braquet M, Bakolas E. Greedy decentralized auction-based task allocation for multi-agent systems. IFAC-PapersOnLine, 2021, 54(20): 675−680 doi: 10.1016/j.ifacol.2021.11.249 [102] Li X H, Liang Y N. An optimal online distributed auction algorithm for multi-UAV task allocation. In: Proceedings of the 11th International Conference on Logistics, Informatics and Service Sciences. Springer, 2022. 537–548 [103] Choi H L, Brunet L, How J P. Consensus-based decentralized auctions for robust task allocation. IEEE Transactions on Robotics, 2009, 25(4): 912−926 doi: 10.1109/TRO.2009.2022423 [104] 唐嘉钰, 李相民, 代进进, 薄宁. 复杂约束条件下异构多智能体联盟任务分配. 控制理论与应用, 2020, 37(11): 2413−2422 doi: 10.7641/CTA.2020.90868Tang Jia-Yu, Li Xiang-Min, Dai Jin-Jin, Bo Ning. Coalition task allocation of heterogeneous multiple agents with complex constraints. Control Theory & Applications, 2020, 37(11): 2413−2422 doi: 10.7641/CTA.2020.90868 [105] Hunt S, Meng Q G, Hinde C, Huang T W. A consensus-based grouping algorithm for multi-agent cooperative task allocation with complex requirements. Cognitive Computation, 2014, 6(3): 338−350 doi: 10.1007/s12559-014-9265-0 [106] Dong D B, Zhu Y H, Du Z Z, Yu D X. Multi-target dynamic hunting strategy based on improved K-means and auction algorithm. Information Sciences, 2023, 640: Article No. 119072 doi: 10.1016/j.ins.2023.119072 [107] Dong D B, Du Z Z, Min J C, Lu R T, Liu J M, Yu D X. Fuzzy dual-hunting control based on auction algorithm. International Journal of Fuzzy Systems, 2023, 25(7): 2816−2827 doi: 10.1007/s40815-023-01531-z [108] 潘子双, 苏析超, 韩维, 柳文林, 郁大照, 汪节. 基于动态一致性联盟算法的异构无人机集群协同作战联盟组建. 兵工学报, DOI: 10.12382/bgxb.2023.0914Pan Zi-Shuang, Su Xi-Chao, Han Wei, Liu Wen-Lin, Yu Da-Zhao, Wang Jie. Cooperative combat coalition formation with heterogeneous UAV swarm based on dynamic consensus-based grouping algorithm. Acta Armamentarii, DOI: 10.12382/bgxb.2023.0914 [109] Zhou M, Wang Z H, Wang J, Cao Z C. Multi-robot collaborative hunting in cluttered environments with obstacle-avoiding Voronoi cells. IEEE/CAA Journal of Automatica Sinica, 2024, 11(7): 1643−1655 doi: 10.1109/JAS.2023.124041 [110] Bhattacharya P, Gavrilova M L. Roadmap-based path planning-using the Voronoi diagram for a clearance-based shortest path. IEEE Robotics & Automation Magazine, 2008, 15(2): 58−66 [111] Chi W Z, Ding Z Y, Wang J K, Chen G D, Sun L N. A generalized Voronoi diagram-based efficient heuristic path planning method for RRTs in mobile robots. IEEE Transactions on Industrial Electronics, 2022, 69(5): 4926−4937 doi: 10.1109/TIE.2021.3078390 [112] Xia N, Wang C, Yu Y T, Du H Z, Xu C N, Zheng J G. A path forming method for water surface mobile sink using Voronoi diagram and dominating set. IEEE Transactions on Vehicular Technology, 2018, 67(8): 7608−7619 doi: 10.1109/TVT.2018.2832096 [113] Wang J K, Meng M Q H. Optimal path planning using generalized Voronoi graph and multiple potential functions. IEEE Transactions on Industrial Electronics, 2020, 67(12): 10621−10630 doi: 10.1109/TIE.2019.2962425 [114] Bakolas E, Tsiotras P. Optimal pursuit of moving targets using dynamic Voronoi diagrams. In: Proceedings of the 49th IEEE Conference on Decision and Control (CDC). Atlanta, USA: IEEE, 2010. 7431–7436 [115] Wang Y, He G H, Ma Y D, Kong G J, Gong J W. Research on multi-robots self-organizing cooperative pursuit algorithm based on Voronoi graph. In: Proceedings of the 39th Chinese Control Conference (CCC). Shenyang, China: IEEE, 2020. 3840–3844 [116] 张云赫, 苏立晨, 董云帆, 刘瑜, 李宇萌. 基于Voronoi图最近邻协商的多机协同追捕方法. 哈尔滨工程大学学报, 2023, 44(2): 284−291 doi: 10.11990/jheu.202203002Zhang Yun-He, Su Li-Chen, Dong Yun-Fan, Liu Yu, Li Yu-Meng. Cooperative pursuit of multiple UAVs based on Voronoi partition nearest neighbor negotiation. Journal of Harbin Engineering University, 2023, 44(2): 284−291 doi: 10.11990/jheu.202203002 [117] Zhou D J, Wang Z J, Bandyopadhyay S, Schwager M. Fast, on-line collision avoidance for dynamic vehicles using buffered Voronoi cells. IEEE Robotics and Automation Letters, 2017, 2(2): 1047−1054 doi: 10.1109/LRA.2017.2656241 [118] Pierson A, Schwarting W, Karaman S, Rus D. Weighted buffered Voronoi cells for distributed semi-cooperative behavior. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). Paris, France: IEEE, 2020. 5611–5617 [119] Tian B L, Li P P, Lu H C, Zong Q, He L. Distributed pursuit of an evader with collision and obstacle avoidance. IEEE Transactions on Cybernetics, 2022, 52(12): 13512−13520 doi: 10.1109/TCYB.2021.3112572 [120] Wang M Y, Schwager M. Distributed collision avoidance of multiple robots with probabilistic buffered Voronoi cells. In: Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems (MRS). New Brunswick, USA: IEEE, 2019. 169–175 [121] Zhu H, Alonso-Mora J. B-UAVC: Buffered uncertainty-aware Voronoi cells for probabilistic multi-robot collision avoidance. In: Proceedings of the International Symposium on Multi-Robot and Multi-Agent Systems (MRS). New Brunswick, USA: IEEE, 2019. 162–168 [122] Zhu H, Brito B, Alonso-Mora J. Decentralized probabilistic multi-robot collision avoidance using buffered uncertainty-aware Voronoi cells. Autonomous Robots, 2022, 46(2): 401−420 doi: 10.1007/s10514-021-10029-2 [123] Hodgkin A L, Huxley A F. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 1952, 117(4): 500−544 doi: 10.1113/jphysiol.1952.sp004764 [124] Grossberg S. Nonlinear neural networks: Principles, mechanisms, and architectures. Neural Networks, 1988, 1(1): 17−61 doi: 10.1016/0893-6080(88)90021-4 [125] Yang S X, Luo C M. A neural network approach to complete coverage path planning. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2004, 34(1): 718−724 doi: 10.1109/TSMCB.2003.811769 [126] Luo C M, Yang S X. A bioinspired neural network for real-time concurrent map building and complete coverage robot navigation in unknown environments. IEEE Transactions on Neural Networks, 2008, 19(7): 1279−1298 doi: 10.1109/TNN.2008.2000394 [127] Yang S X, Meng M Q H. Real-time collision-free motion planning of a mobile robot using a neural dynamics-based approach. IEEE Transactions on Neural Networks, 2003, 14(6): 1541−1552 doi: 10.1109/TNN.2003.820618 [128] Öǧmen H, Gagné S. Neural network architectures for motion perception and elementary motion detection in the fly visual system. Neural Networks, 1990, 3(5): 487−505 doi: 10.1016/0893-6080(90)90001-2 [129] 王耀南, 潘琪, 陈彦杰. 改进型生物激励神经网络的路径规划方法. 控制工程, 2018, 25(4): 541−548Wang Yao-Nan, Pan Qi, Chen Yan-Jie. Path planning method based on improved biologically inspired neural network. Control Engineering of China, 2018, 25(4): 541−548 [130] 朱大奇, 孙兵, 李利. 基于生物启发模型的AUV三维自主路径规划与安全避障算法. 控制与决策, 2015, 30(5): 798−806Zhu Da-Qi, Sun Bing, Li Li. Algorithm for AUV's 3-D path planning and safe obstacle avoidance based on biological inspired model. Control and Decision, 2015, 30(5): 798−806 [131] 朱大奇, 刘雨, 孙兵, 刘清沁. 自治水下机器人的自主启发式生物启发神经网络路径规划算法. 控制理论与应用, 2019, 36(2): 183−191 doi: 10.7641/CTA.2018.70576Zhu Da-Qi, Liu Yu, Sun Bing, Liu Qing-Qin. Autonomous underwater vehicles path planning based on autonomous inspired Glasius bio-inspired neural network algorithm. Control Theory & Applications, 2019, 36(2): 183−191 doi: 10.7641/CTA.2018.70576 [132] 刘晨霞, 朱大奇, 周蓓, 顾伟. 海流环境下多AUV多目标生物启发任务分配与路径规划算法. 控制理论与应用, 2022, 39(11): 2100−2107 doi: 10.7641/CTA.2022.11019Liu Chen-Xia, Zhu Da-Qi, Zhou Bei, Gu Wei. A novel algorithm of multi-AUVs task assignment and path planning based on biologically inspired neural network for ocean current environment. Control Theory & Applications, 2022, 39(11): 2100−2107 doi: 10.7641/CTA.2022.11019 [133] Lv R F, Gan W Y, Sun B, Zhu D Q. A multi-AUV hunting algorithm with ocean current effect. In: Proceedings of the IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). Shenyang, China: IEEE, 2015. 869–874 [134] Liu Q Q, Sun B, Zhu D Q. A multi-AUVs cooperative hunting algorithm for environment with ocean current. In: Proceedings of the 37th Chinese Control Conference (CCC). Wuhan, China: IEEE, 2018. 5441–5444 [135] Zhu D Q, Lv R F, Cao X, Yang S X. Multi-AUV hunting algorithm based on bio-inspired neural network in unknown environments. International Journal of Advanced Robotic Systems, 2015, 12(11): Article No. 166 doi: 10.5772/61555 [136] Huang Z R, Zhu D Q, Sun B. A multi-AUV cooperative hunting method in 3-D underwater environment with obstacle. Engineering Applications of Artificial Intelligence, 2016, 50: 192−200 doi: 10.1016/j.engappai.2016.01.036 [137] Ni J J, Yang S X. Bioinspired neural network for real-time cooperative hunting by multirobots in unknown environments. IEEE Transactions on Neural Networks, 2011, 22(12): 2062−2077 doi: 10.1109/TNN.2011.2169808 [138] Niu Z Y, Zhong G Q, Yu H. A review on the attention mechanism of deep learning. Neurocomputing, 2021, 452: 48−62 doi: 10.1016/j.neucom.2021.03.091 [139] 李登峰. 微分对策及其应用. 北京: 国防工业出版社, 2000.Li Deng-Feng. Differential Games and Applications. Beijing: National Defense Industry Press, 2000. [140] 方宝富, 潘启树, 洪炳镕, 丁磊, 蔡则苏. 多追捕者−单一逃跑者追逃问题实现成功捕获的约束条件. 机器人, 2012, 34(3): 282−291 doi: 10.3724/SP.J.1218.2012.00282Fang Bao-Fu, Pan Qi-Shu, Hong Bing-Rong, Ding Lei, Cai Ze-Su. Constraint conditions of successful capture in multi-pursuers vs one-evader games. Robot, 2012, 34(3): 282−291 doi: 10.3724/SP.J.1218.2012.00282 [141] Bera R, Makkapati V R, Kothari M. A comprehensive differential game theoretic solution to a game of two cars. Journal of Optimization Theory and Applications, 2017, 174(3): 818−836 doi: 10.1007/s10957-017-1134-z [142] Pan T Y, Yuan Y. A region-based relay pursuit scheme for a pursuit-evasion game with a single evader and multiple pursuers. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(3): 1958−1969 doi: 10.1109/TSMC.2022.3210022 [143] Sun W, Tsiotras P. Sequential pursuit of multiple targets under external disturbances via Zermelo-Voronoi diagrams. Automatica, 2017, 81: 253−260 doi: 10.1016/j.automatica.2017.03.015 [144] 罗亚中, 李振瑜, 祝海. 航天器轨道追逃微分对策研究综述. 中国科学: 技术科学, 2020, 50(12): 1533−1545 doi: 10.1360/SST-2019-0174Luo Ya-Zhong, Li Zhen-Yu, Zhu Hai. Survey on spacecraft orbital pursuit-evasion differential games. Scientia Sinica Technologica, 2020, 50(12): 1533−1545 doi: 10.1360/SST-2019-0174 [145] Nian X H, Niu F X, Yang Z. Distributed Nash equilibrium seeking for multicluster game under switching communication topologies. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(7): 4105−4116 doi: 10.1109/TSMC.2021.3090515 [146] Zhu Y H, Zhao D B. Online minimax Q network learning for two-player zero-sum Markov games. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(3): 1228−1241 doi: 10.1109/TNNLS.2020.3041469 [147] Li M, Qin J H, Ma Q C, Zheng W X, Kang Y. Hierarchical optimal synchronization for linear systems via reinforcement learning: A stackelberg-Nash game perspective. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(4): 1600−1611 doi: 10.1109/TNNLS.2020.2985738 [148] Isaacs R. Differential Games. A Mathematical Theory With Applications to Warfare and Pursuit, Control and Optimization. Wiley, 1965. [149] Ramana M V, Kothari M. Pursuit strategy to capture high-speed evaders using multiple pursuers. Journal of Guidance, Control, and Dynamics, 2017, 40(1): 139−149 doi: 10.2514/1.G000584 [150] Fang X, Wang C, Xie L H, Chen J. Cooperative pursuit with multi-pursuer and one faster free-moving evader. IEEE Transactions on Cybernetics, 2022, 52(3): 1405−1414 doi: 10.1109/TCYB.2019.2958548 [151] García E, Murano D A. State estimation for a class of nonlinear differential games using differential neural networks. In: Proceedings of the American Control Conference. San Francisco, USA: IEEE, 2011. 2486–2491 [152] Xue B, Easwaran A, Cho N J, Fränzle M. Reach-avoid verification for nonlinear systems based on boundary analysis. IEEE Transactions on Automatic Control, 2017, 62(7): 3518−3523 doi: 10.1109/TAC.2016.2615599 [153] Chen M, Bansal S, Fisac J F, Tomlin C J. Robust sequential trajectory planning under disturbances and adversarial intruder. IEEE Transactions on Control Systems Technology, 2019, 27(4): 1566−1582 doi: 10.1109/TCST.2018.2828380 [154] Li W. Formulation of a Cooperative-Confinement-Escape problem of multiple cooperative defenders against an evader escaping from a circular region. Communications in Nonlinear Science and Numerical Simulation, 2016, 39: 442−457 doi: 10.1016/j.cnsns.2016.02.042 [155] Li W. Escape analysis on the confinement-escape problem of a defender against an evader escaping from a circular region. IEEE Transactions on Cybernetics, 2016, 46(9): 2166−2172 doi: 10.1109/TCYB.2016.2541158 [156] Fisac J F, Sastry S S. The pursuit-evasion-defense differential game in dynamic constrained environments. In: Proceedings of the 54th IEEE Conference on Decision and Control (CDC). Osaka, Japan: IEEE, 2015. 4549–4556 [157] Zhang H G, Cui L L, Luo Y H. Near-optimal control for nonzero-sum differential games of continuous-time nonlinear systems using single-network ADP. IEEE Transactions on Cybernetics, 2013, 43(1): 206−216 doi: 10.1109/TSMCB.2012.2203336 [158] Zhao D B, Zhang Q C, Wang D, Zhu Y H. Experience replay for optimal control of nonzero-sum game systems with unknown dynamics. IEEE Transactions on Cybernetics, 2016, 46(3): 854−865 doi: 10.1109/TCYB.2015.2488680 [159] 王龙, 黄锋. 多智能体博弈、学习与控制. 自动化学报, 2023, 49(3): 580−613Wang Long, Huang Feng. An interdisciplinary survey of multi-agent games, learning, and control. Acta Automatica Sinica, 2023, 49(3): 580−613 [160] Sutton R S, Barto A G. Reinforcement Learning: An Introduction (Second edition). Cambridge: MIT Press, 2018. [161] Kaelbling L P, Littman M L, Moore A W. Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 1996, 4: 237−285 doi: 10.1613/jair.301 [162] Zhang K Q, Yang Z R, Basar T. Multi-agent reinforcement learning: A selective overview of theories and algorithms. Handbook of Reinforcement Learning and Control. Cham: Springer, 2021. 321–384 [163] Yang Y D, Wang J. An overview of multi-agent reinforcement learning from game theoretical perspective. arXiv: 2011.00583, 2020. [164] Xie S, Chu X M, Zheng M, Liu C G. A composite learning method for multi-ship collision avoidance based on reinforcement learning and inverse control. Neurocomputing, 2020, 411: 375−392 doi: 10.1016/j.neucom.2020.05.089 [165] Watkins C J C H, Dayan P. Q-learning. Machine Learning, 1992, 8(3): 279−292 [166] Mnih V, Kavukcuoglu K, Silver D, Rusu A A, Veness J, Bellemare M G, et al. Human-level control through deep reinforcement learning. Nature, 2015, 518(7540): 529−533 doi: 10.1038/nature14236 [167] Kocsis L, Szepesvári C. Bandit based Monte-Carlo planning. In: Proceedings of the 17th European Conference on Machine Learning. Berlin, Germany: Springer, 2006. 282–293 [168] Shi X T, Li Y J, Hu W X, Du C L, Chen C Y, Gui W H. Optimal lateral path-tracking control of vehicles with partial unknown dynamics via DPG-based reinforcement learning methods. IEEE Transactions on Intelligent Vehicles, 2024, 9(1): 1701−1710 doi: 10.1109/TIV.2023.3319642 [169] Gao H H, Wang X J, Wei W, Al-Dulaimi A, Xu Y S. Com-DDPG: Task offloading based on multiagent reinforcement learning for information-communication-enhanced mobile edge computing in the internet of vehicles. IEEE Transactions on Vehicular Technology, 2024, 73(1): 348−361 doi: 10.1109/TVT.2023.3309321 [170] Centurelli A, Arleo L, Rizzo A, Tolu S, Laschi C, Falotico E. Closed-loop dynamic control of a soft manipulator using deep reinforcement learning. IEEE Robotics and Automation Letters, 2022, 7(2): 4741−4748 doi: 10.1109/LRA.2022.3146903 [171] Guo D L, Tang L, Zhang X G, Liang Y C. Joint optimization of handover control and power allocation based on multi-agent deep reinforcement learning. IEEE Transactions on Vehicular Technology, 2020, 69(11): 13124−13138 doi: 10.1109/TVT.2020.3020400 [172] Han M H, Zhang L X, Wang J, Pan W. Actor-critic reinforcement learning for control with stability guarantee. IEEE Robotics and Automation Letters, 2020, 5(4): 6217−6224 doi: 10.1109/LRA.2020.3011351 [173] Ge H W, Gao D W, Sun L, Hou Y Q, Yu C, Wang Y X, et al. Multi-agent transfer reinforcement learning with multi-view encoder for adaptive traffic signal control. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 12572−12587 doi: 10.1109/TITS.2021.3115240 [174] Wang Y D, Dong L, Sun C Y. Cooperative control for multi-player pursuit-evasion games with reinforcement learning. Neurocomputing, 2020, 412: 101−114 doi: 10.1016/j.neucom.2020.06.031 [175] Lowe R, Wu Y, Tamar A, Harb J, Abbeel P, Mordatch I. Multi-agent actor-critic for mixed cooperative-competitive environments. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: Curran Associates Inc., 2017. 6382–6393 [176] Yu L L, Huo S X, Wang Z J, Li K Y. Hybrid attention-oriented experience replay for deep reinforcement learning and its application to a multi-robot cooperative hunting problem. Neurocomputing, 2023, 523: 44−57 doi: 10.1016/j.neucom.2022.12.020 [177] 夏家伟, 朱旭芳, 张建强, 罗亚松, 刘忠. 基于多智能体强化学习的无人艇协同围捕方法. 控制与决策, 2023, 38(5): 1438−1447Xia Jia-Wei, Zhu Xu-Fang, Zhang Jian-Qiang, Luo Ya-Song, Liu Zhong. Research on cooperative hunting method of unmanned surface vehicle based on multi-agent reinforcement learning. Control and Decision, 2023, 38(5): 1438−1447 [178] Bilgin A T, Kadioglu-Urtis E. An approach to multi-agent pursuit evasion games using reinforcement learning. In: Proceedings of the International Conference on Advanced Robotics (ICAR). Istanbul, Turkey: IEEE, 2015. 164–169 [179] Vlahov B, Squires E, Strickland L, Pippin C. On developing a UAV pursuit-evasion policy using reinforcement learning. In: Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA). Orlando, USA: IEEE, 2018. 859–864 [180] de Souza C, Newbury R, Cosgun A, Castillo P, Vidolov B, Kulić D. Decentralized multi-agent pursuit using deep reinforcement learning. IEEE Robotics and Automation Letters, 2021, 6(3): 4552−4559 doi: 10.1109/LRA.2021.3068952 [181] Xia J W, Luo Y S, Liu Z K, Zhang Y L, Shi H R, Liu Z. Cooperative multi-target hunting by unmanned surface vehicles based on multi-agent reinforcement learning. Defence Technology, 2023, 29: 80−94 doi: 10.1016/j.dt.2022.09.014 [182] Awheda M D, Schwartz H M. A fuzzy reinforcement learning algorithm using a predictor for pursuit-evasion games. In: Proceedings of the Annual IEEE Systems Conference (SysCon). Orlando, USA: IEEE, 2016. 1–8 [183] Cao X, Zuo F. A fuzzy-based potential field hierarchical reinforcement learning approach for target hunting by multi-AUV in 3-D underwater environments. International Journal of Control, 2021, 94(5): 1334−1343 doi: 10.1080/00207179.2019.1648875 [184] Wang L X, Wang M L, Yue T. A fuzzy deterministic policy gradient algorithm for pursuit-evasion differential games. Neurocomputing, 2019, 362: 106−117 doi: 10.1016/j.neucom.2019.07.038 [185] Foerster J, Farquhar G, Afouras T, Nardelli N, Whiteson S. Counterfactual multi-agent policy gradients. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New Orleans, USA: AAAI, 2018. [186] Zhang Z, Wang X H, Zhang Q R, Hu T J. Multi-robot cooperative pursuit via potential field-enhanced reinforcement learning. In: Proceedings of the International Conference on Robotics and Automation (ICRA). Philadelphia, USA: IEEE, 2022. 8808–8814 [187] Fang B F, Zhu J L, Zhang H, Wang H, Wang Z J. Multi self-interested robot pursuit based on quantum game theory. In: Proceedings of the Chinese Automation Congress (CAC). Jinan, China: IEEE, 2017. 7368–7373 [188] 晏亚林. 基于博弈论的多机器人追捕问题的研究 [硕士学位论文], 哈尔滨工程大学, 中国, 2014.Yan Ya-Lin. Research on Multi-Robot Pursuit-Evasion Problem Based on Game Theory [Master thesis], Harbin Engineering University, China, 2014. [189] 郑延斌, 樊文鑫, 韩梦云, 陶雪丽. 基于博弈论及Q学习的多Agent协作追捕算法. 计算机应用, 2020, 40(6): 1613−1620Zheng Yan-Bin, Fan Wen-Xin, Han Meng-Yun, Tao Xue-Li. Multi-agent collaborative pursuit algorithm based on game theory and Q-learning. Journal of Computer Applications, 2020, 40(6): 1613−1620 [190] Qu X Q, Gan W H, Song D L, Zhou L Q. Pursuit-evasion game strategy of USV based on deep reinforcement learning in complex multi-obstacle environment. Ocean Engineering, 2023, 273: Article No. 114016 doi: 10.1016/j.oceaneng.2023.114016 [191] Zhang R L, Zong Q, Zhang X Y, Dou L Q, Tian B L. Game of drones: Multi-UAV pursuit-evasion game with online motion planning by deep reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(10): 7900−7909 doi: 10.1109/TNNLS.2022.3146976 [192] Gao Z K, Dai X Y, Yao M B, Xiao X M. A data enhancement strategy for multi-agent cooperative hunting based on deep reinforcement learning. In: Proceedings of the IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS). Wuhan, China: IEEE, 2023. 1–8 [193] Asl Z D, Derhami V, Yazdian-Dehkordi M. A new approach on multi-agent multi-objective reinforcement learning based on agents' preferences. In: Proceedings of the Artificial Intelligence and Signal Processing Conference (AISP). Shiraz, Iran: IEEE, 2017. 75–79 [194] Du B, Lin B, Zhang C M, Dong B T, Zhang W D. Safe deep reinforcement learning-based adaptive control for USV interception mission. Ocean Engineering, 2022, 246: Article No. 110477 doi: 10.1016/j.oceaneng.2021.110477 [195] Deghat M, Davis E, See T, Shames I, Anderson B D O, Yu C B. Target localization and circumnavigation by a non-holonomic robot. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Vilamoura-Algarve, Portugal: IEEE, 2012. 1227–1232 [196] Deghat M, Shames I, Anderson B D O, Yu C B. Localization and circumnavigation of a slowly moving target using bearing measurements. IEEE Transactions on Automatic Control, 2014, 59(8): 2182−2188 doi: 10.1109/TAC.2014.2299011 [197] Zheng R H, Liu Y H, Sun D. Enclosing a target by nonholonomic mobile robots with bearing-only measurements. Automatica, 2015, 53: 400−407 doi: 10.1016/j.automatica.2015.01.014 [198] Lee B H, Ahn H S. Distributed formation control via global orientation estimation. Automatica, 2016, 73: 125−129 doi: 10.1016/j.automatica.2016.06.030 [199] Yu X, Xu X, Liu L, Feng G. Circular formation of networked dynamic unicycles by a distributed dynamic control law. Automatica, 2018, 89: 1−7 doi: 10.1016/j.automatica.2017.11.021 [200] Yamamoto Y, Yun X P. Coordinating locomotion and manipulation of a mobile manipulator. In: Proceedings of the 31st IEEE Conference on Decision and Control. Tucson, USA: IEEE, 1992. 2643–2648 [201] Zhao S Y, Pan Y N, Du P H, Liang H J. Adaptive control for non-affine nonlinear systems with input saturation and output dead zone. Applied Mathematics and Computation, 2020, 386: Article No. 125506 doi: 10.1016/j.amc.2020.125506 [202] Chen M, Ge S S, Ren B B. Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints. Automatica, 2011, 47(3): 452−465 doi: 10.1016/j.automatica.2011.01.025 [203] 张红强, 章兢, 周少武, 曾照福, 吴亮红. 基于简化虚拟受力模型的未知复杂环境下群机器人围捕. 电子学报, 2015, 43(4): 665−674 doi: 10.3969/j.issn.0372-2112.2015.04.007Zhang Hong-Qiang, Zhang Jing, Zhou Shao-Wu, Zeng Zhao-Fu, Wu Liang-Hong. Hunting in unknown complex environments by swarm robots based on simplified virtual-force model. Acta Electronica Sinica, 2015, 43(4): 665−674 doi: 10.3969/j.issn.0372-2112.2015.04.007 [204] 张红强, 章兢, 周少武, 曾照福, 吴亮红. 未知动态环境下非完整移动群机器人围捕. 控制理论与应用, 2014, 31(9): 1151−1165 doi: 10.7641/CTA.2014.31243Zhang Hong-Qiang, Zhang Jing, Zhou Shao-Wu, Zeng Zhao-Fu, Wu Liang-Hong. Nonholonomic mobile swarm robots hunting in unknown dynamic environments. Control Theory & Applications, 2014, 31(9): 1151−1165 doi: 10.7641/CTA.2014.31243 [205] 张红强, 吴亮红, 周游, 章兢, 周少武, 刘朝华. 复杂环境下群机器人自组织协同多目标围捕. 控制理论与应用, 2020, 37(5): 1054−1062 doi: 10.7641/CTA.2019.90015Zhang Hong-Qiang, Wu Liang-Hong, Zhou You, Zhang Jing, Zhou Shao-Wu, Liu Zhao-Hua. Self-organizing cooperative multi-target hunting by swarm robots in complex environments. Control Theory & Applications, 2020, 37(5): 1054−1062 doi: 10.7641/CTA.2019.90015 [206] 罗家祥, 许博喆, 刘海明, 蔡鹤, 高焕丽, 姚瞻楠. 感知范围受限的群机器人自主围捕算法. 控制理论与应用, 2021, 38(7): 933−946 doi: 10.7641/CTA.2021.00715Luo Jia-Xiang, Xu Bo-Zhe, Liu Hai-Ming, Cai He, Gao Huan-Li, Yao Zhan-Nan. Autonomous hunting algorithm for swarm robots subject to limited sensing range. Control Theory & Applications, 2021, 38(7): 933−946 doi: 10.7641/CTA.2021.00715 [207] 黄天云, 陈雪波, 徐望宝, 周自维, 任志勇. 基于松散偏好规则的群体机器人系统自组织协作围捕. 自动化学报, 2013, 39(1): 57−68 doi: 10.1016/S1874-1029(13)60007-5Huang Tian-Yun, Chen Xue-Bo, Xu Wang-Bao, Zhou Zi-Wei, Ren Zhi-Yong. A self-organizing cooperative hunting by swarm robotic systems based on loose-preference rule. Acta Automatica Sinica, 2013, 39(1): 57−68 doi: 10.1016/S1874-1029(13)60007-5 [208] Yu X, Liu L, Feng G. Distributed circular formation control of nonholonomic vehicles without direct distance measurements. IEEE Transactions on Automatic Control, 2018, 63(8): 2730−2737 doi: 10.1109/TAC.2018.2790259 [209] Su Y X. Comments on “Controller design for rigid spacecraft attitude tracking with actuator saturation”. Information Sciences, 2016, 342: 150−152 doi: 10.1016/j.ins.2015.12.040 [210] Wen G X, Ge S S, Chen C L P, Tu F W, Wang S N. Adaptive tracking control of surface vessel using optimized backstepping technique. IEEE Transactions on Cybernetics, 2019, 49(9): 3420−3431 doi: 10.1109/TCYB.2018.2844177 [211] Zheng K M, Zhang Q J, Hu Y M, Wu B. Design of fuzzy system-fuzzy neural network-backstepping control for complex robot system. Information Sciences, 2021, 546: 1230−1255 doi: 10.1016/j.ins.2020.08.110 [212] Ding S H, Park J H, Chen C C. Second-order sliding mode controller design with output constraint. Automatica, 2020, 112: Article No. 108704 doi: 10.1016/j.automatica.2019.108704 [213] Huang C R, Fujisawa S, de Lima T F, Tait A N, Blow E C, Tian Y, et al. A silicon photonic-electronic neural network for fibre nonlinearity compensation. Nature Electronics, 2021, 4(11): 837−844 doi: 10.1038/s41928-021-00661-2 [214] Ma H, Ren H R, Zhou Q, Lu R Q, Li H Y. Approximation-based Nussbaum gain adaptive control of nonlinear systems with periodic disturbances. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(4): 2591−2600 doi: 10.1109/TSMC.2021.3050993 [215] Sun Y M, Chen B, Lin C, Wang H H, Zhou S W. Adaptive neural control for a class of stochastic nonlinear systems by backstepping approach. Information Sciences, 2016, 369: 748−764 doi: 10.1016/j.ins.2016.06.010 [216] Ghommam J, Saad M, Mnif F. Finite-time circular formation around a moving target with multiple underactuated ODIN vehicles. Mathematics and Computers in Simulation, 2021, 180: 230−250 doi: 10.1016/j.matcom.2020.08.026 [217] Duan Y, Huang X, Yu X. Multi-robot dynamic virtual potential point hunting strategy based on FIS. In: Proceedings of the IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). Nanjing, China: IEEE, 2016. 332–335 [218] Beke A, Kumbasar T. Game of spheros: A real-world pursuit-evasion game with type-2 fuzzy logic. In: Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). Naples, Italy: IEEE, 2017. 1–6 [219] 胡俊, 朱庆保. 基于动态预测目标轨迹和围捕点的多机器人围捕算法. 电子学报, 2011, 39(11): 2480−2485Hu Jun, Zhu Qing-Bao. A multi-robot hunting algorithm based on dynamic prediction for trajectory of the moving target and hunting points. Acta Electronica Sinica, 2011, 39(11): 2480−2485 [220] Wu Z Y, Cao Z Q, Yu Y Y, Pang L, Zhou C, Chen E K. A multi-robot cooperative hunting approach based on dynamic prediction of target motion. In: Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO). Macao, China: IEEE, 2017. 587–592 [221] Cui J F, Li D C, Liu P, Qin J, Ma Y D, Lu Z G. Game-model prediction hybrid path planning algorithm for multiple mobile robots in pursuit evasion game. In: Proceedings of the IEEE International Conference on Unmanned Systems (ICUS). Beijing, China: IEEE, 2021. 925–930 [222] Cao X, Xu X Y. Hunting algorithm for multi-AUV based on dynamic prediction of target trajectory in 3D underwater environment. IEEE Access, 2020, 8: 138529−138538 doi: 10.1109/ACCESS.2020.3013032 [223] Huang G Q. Visual-inertial navigation: A concise review. In: Proceedings of the International Conference on Robotics and Automation (ICRA). Montreal, Canada: IEEE, 2019. 9572–9582 [224] Yu H, Zhen W K, Yang W, Zhang J, Scherer S. Monocular camera localization in prior LiDAR maps with 2D-3D line correspondences. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Las Vegas, USA: IEEE, 2020. 4588–4594 [225] Kim Y, Jeong J, Kim A. Stereo camera localization in 3D LiDAR maps. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, Spain: IEEE, 2018. 1–9 [226] Srivastava S, Kumar M S, Mishra A, Chopra S, Jagannatham A K, Hanzo L. Sparse doubly-selective channel estimation techniques for OSTBC MIMO-OFDM systems: A hierarchical Bayesian Kalman filter based approach. IEEE Transactions on Communications, 2020, 68(8): 4844−4858 doi: 10.1109/TCOMM.2020.2995585 [227] 张梦轩, 苏治宝, 索旭东. 移动机器人定位方法研究综述. 车辆与动力技术, 2023(4): 56−62 doi: 10.3969/j.issn.1009-4687.2023.04.010Zhang Meng-Xuan, Su Zhi-Bao, Suo Xu-Dong. Overview of research on localization methods for mobile robots. Vehicle & Power Technology, 2023(4): 56−62 doi: 10.3969/j.issn.1009-4687.2023.04.010 [228] 王耀南, 江一鸣, 姜娇, 张辉, 谭浩然, 彭伟星, 等. 机器人感知与控制关键技术及其智能制造应用. 自动化学报, 2023, 49(3): 494−513Wang Yao-Nan, Jiang Yi-Ming, Jiang Jiao, Zhang Hui, Tan Hao-Ran, Peng Wei-Xing, et al. Key technologies of robot perception and control and its intelligent manufacturing applications. Acta Automatica Sinica, 2023, 49(3): 494−513 [229] Yan T M, Gan Y Z, Xia Z Y, Zhao Q F. Segment-based disparity refinement with occlusion handling for stereo matching. IEEE Transactions on Image Processing, 2019, 28(8): 3885−3897 doi: 10.1109/TIP.2019.2903318 [230] Yang L, Xu Y, Wang S R, Yuan C F, Zhang Z Q, Li B, et al. PDNet: Toward better one-stage object detection with prediction decoupling. IEEE Transactions on Image Processing, 2022, 31: 5121−5133 doi: 10.1109/TIP.2022.3193223 [231] Chen Q, Tang S H, Yang Q, Fu S. Cooper: Cooperative perception for connected autonomous vehicles based on 3D point clouds. In: Proceedings of the IEEE 39th International Conference on Distributed Computing Systems (ICDCS). Dallas, USA: IEEE, 2019. 514–524 [232] Du Y C, Qin B H, Zhao C, Zhu Y F, Cao J, Ji Y X. A novel spatio-temporal synchronization method of roadside asynchronous MMW radar-camera for sensor fusion. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 22278−22289 doi: 10.1109/TITS.2021.3119079 [233] 孙志娟. 多机器人协同通信技术研究 [硕士学位论文], 广西科技大学, 中国, 2019.Sun Zhi-Juan. Research on Multi-Robot Cooperative Communication Technology [Master thesis], Guangxi University of Science and Technology, China, 2019. [234] 李莹莹, 刘云辉, 樊玮虹, 蔡宣平, 李波. 基于移动通信网络的机器人遥操作. 通信学报, 2006, 27(5): 52−59 doi: 10.3321/j.issn:1000-436X.2006.05.010Li Ying-Ying, Liu Yun-Hui, Fan Wei-Hong, Cai Xuan-Ping, Li Bo. Teleoperation of robots via the mobile communication networks. Journal on Communications, 2006, 27(5): 52−59 doi: 10.3321/j.issn:1000-436X.2006.05.010 [235] Marangoz S, Amasyal M F, Uslu E, Çakmak F, Altuntaş N, Yavuz S. More scalable solution for multi-robot-multi-target assignment problem. Robotics and Autonomous Systems, 2019, 113: 174−185 doi: 10.1016/j.robot.2019.01.005 [236] Lin E S, Agmon N, Kraus S. Multi-robot adversarial patrolling: Handling sequential attacks. Artificial Intelligence, 2019, 274: 1−25 doi: 10.1016/j.artint.2019.02.004 期刊类型引用(11)
1. 王君，韦娅萍. 事件触发下多智能体系统规定时间二分一致性. 系统科学与数学. 2024(01): 71-85 . 
百度学术2. 郭胜辉，罗世坚，黄大荣，宋家锋. 自动驾驶车辆道路跟驰与状态一致性控制. 中国科学:信息科学. 2024(02): 394-412 . 百度学术3. 赵华荣，彭力，吴治海，谢林柏，于洪年. 随机时延下多输入多输出多智能体系统事件触发双向编队. 控制与决策. 2024(04): 1251-1259 . 百度学术4. 熊勇刚，付茂林，李波，姚焘，张锐. 基于领航-跟随法的多机器人编队控制. 机电工程技术. 2024(03): 136-141 . 百度学术5. 时宏伟，滕婧婧，李凌云，郭胜辉. 含不确定参数的智能网联汽车协同编队控制. 西安工程大学学报. 2024(03): 117-125 . 百度学术6. 刘沛明，郭祥贵. 基于观测器的人在环多机械臂系统预设性能二分一致性. 自动化学报. 2024(09): 1761-1771 . 本站查看7. 张宏宏，李文华，郑家毅，刘宏斌，张鹏，高鹏，甘旭升. 有人/无人机协同作战：概念、技术与挑战. 航空学报. 2024(15): 168-194 . 百度学术8. 彭战松，李铁柱. 基于相对方向的非线性无人机群反步编队跟踪控制. 飞行力学. 2023(01): 34-39 . 百度学术9. 钱贝，周绍磊，肖支才，闫实，祁亚辉，侯鹏森. 通信时延条件下的单一领导者多无人机系统编队控制. 电光与控制. 2023(05): 66-72 . 百度学术10. 刘青松，习晓苗，柴利. 具有类万有引力的有界置信观点动力学分析与应用. 自动化学报. 2023(09): 1967-1975 . 本站查看11. 关睿. 机械制造智能化技术与机电一体化的融合研究. 造纸装备及材料. 2022(12): 126-128 . 百度学术其他类型引用(15)
-
下载:
图(26) / 表(4)
计量
- 文章访问数: 1283
- HTML全文浏览量: 710
- PDF下载量: 470
- 被引次数: 26
