一、Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅱ)（论文文献综述）
JTTE Editorial Office,Jiaqi Chen,Hancheng Dan,Yongjie Ding,Yangming Gao,Meng Guo,Shuaicheng Guo,Bingye Han,Bin Hong,Yue Hou,Chichun Hu,Jing Hu,Ju Huyan,Jiwang Jiang,Wei Jiang,Cheng Li,Pengfei Liu,Yu Liu,Zhuangzhuang Liu,Guoyang Lu,Jian Ouyang,Xin Qu,Dongya Ren,Chao Wang,Chaohui Wang,Dawei Wang,Di Wang,Hainian Wang,Haopeng Wang,Yue Xiao,Chao Xing,Huining Xu,Yu Yan,Xu Yang,Lingyun You,Zhanping You,Bin Yu,Huayang Yu,Huanan Yu,Henglong Zhang,Jizhe Zhang,Changhong Zhou,Changjun Zhou,Xingyi Zhu[1]（2021）在《New innovations in pavement materials and engineering:A review on pavement engineering research 2021》文中研究指明Sustainable and resilient pavement infrastructure is critical for current economic and environmental challenges. In the past 10 years, the pavement infrastructure strongly supports the rapid development of the global social economy. New theories, new methods,new technologies and new materials related to pavement engineering are emerging.Deterioration of pavement infrastructure is a typical multi-physics problem. Because of actual coupled behaviors of traffic and environmental conditions, predictions of pavement service life become more and more complicated and require a deep knowledge of pavement material analysis. In order to summarize the current and determine the future research of pavement engineering, Journal of Traffic and Transportation Engineering（English Edition） has launched a review paper on the topic of "New innovations in pavement materials and engineering: A review on pavement engineering research 2021". Based on the joint-effort of 43 scholars from 24 well-known universities in highway engineering, this review paper systematically analyzes the research status and future development direction of 5 major fields of pavement engineering in the world. The content includes asphalt binder performance and modeling, mixture performance and modeling of pavement materials,multi-scale mechanics, green and sustainable pavement, and intelligent pavement.Overall, this review paper is able to provide references and insights for researchers and engineers in the field of pavement engineering.
Fan Xu,Hesheng Wang[2]（2021）在《Soft Robotics:Morphology and Morphology-inspired Motion Strategy》文中研究说明Robotics has aroused huge attention since the 1950s.Irrespective of the uniqueness that industrial applications exhibit,conventional rigid robots have displayed noticeable limitations,particularly in safe cooperation as well as with environmental adaption.Accordingly,scientists have shifted their focus on soft robotics to apply this type of robots more effectively in unstructured environments.For decades,they have been committed to exploring sub-fields of soft robotics （e.g.,cuttingedge techniques in design and fabrication,accurate modeling,as well as advanced control algorithms）.Although scientists have made many different efforts,they share the common goal of enhancing applicability.The presented paper aims to brief the progress of soft robotic research for readers interested in this field,and clarify how an appropriate control algorithm can be produced for soft robots with specific morphologies.This paper,instead of enumerating existing modeling or control methods of a certain soft robot prototype,interprets for the relationship between morphology and morphology-dependent motion strategy,attempts to delve into the common issues in a particular class of soft robots,and elucidates a generic solution to enhance their performance.
任晓琳[3]（2021）在《基于图像的机械臂视觉伺服定位及跟踪控制方法研究》文中研究指明近年来,随着机器视觉等相关领域的研究不断深入,基于图像信息对机械臂进行视觉伺服控制,不仅加强了机械臂获取信息的多样性,拓展了机械臂的空间认知力和适应性,还提高了机械臂的精确识别与精细操作的能力。除了在工业和医疗领域的应用,机械臂视觉伺服已拓展至作业空间更复杂的领域,如深海探测、爆炸物处理、灾区勘探等。传统的工业机器人按照指定的程序完成伺服任务,然而受限于工作环境的变化,其无法做出有效的决策。由此可见,能够提取精确的图像信息并制定良好的运动决策是机械臂需要掌握的技能之一,也是机械臂视觉在目标定位和跟踪控制中经常面临的一个问题。因此,针对具有自主定位和跟踪能力的机械臂进行研究具有重要现实意义。在机械臂视觉伺服系统中,由于相机在成像过程中参数不能精确获取,标定过程繁琐且容易产生误差,获得的图像本身含有大量噪声,因此图像信息的获取过程需要高效的算法作为辅助;此外,由于相机成像范围存在约束、工作环境照明强度的变化以及图像遮挡等情况,可能导致伺服任务失败;进一步地,机械臂和相机都是复杂非线性系统,且存在强耦合性和动态不确定性。综合上述分析,本文考虑环境噪声、图像特征扰动、视野约束和综合能耗等因素,对机械臂视觉伺服系统的定位与跟踪控制方法进行了深入研究。主要内容包括:考虑图像噪声的机械臂视觉伺服定位及跟踪控制方法、基于面向图像特征约束的机械臂视觉伺服混合定位控制方法、基于受图像遮挡/干扰的机械臂视觉伺服双闭环跟踪控制方法以及基于自适应动态规划的机械臂视觉伺服最优跟踪控制方法,具体研究内容如下:（1）针对视觉伺服系统的运行精度易受环境噪声和外界干扰影响的问题,本文提出了基于改进的卡尔曼滤波的图像雅克比矩阵在线估计策略。在噪声统计特性未知的情况下,利用递推估计状态模型噪声协方差矩阵,选取三段函数描述的学习统计量作为自适应因子,采用自适应抗差卡尔曼滤波（ARKF）动态调整滤波增益。在非高斯噪声条件下,利用基于最大相关熵的卡尔曼滤波（MCKF）估计图像雅可比矩阵,提高了图像特征轨迹的跟踪性能。针对静态目标的定位控制及移动目标的跟踪控制进行了数值仿真及对比分析,进一步验证了系统性能的优越性。（2）为了解决基于图像的视觉伺服控制中的视野约束和噪声干扰而带来的伺服任务失败和系统发散的问题,提出了联合光滑变结构滤波器（SVSF）与双向极限学习机（B-ELM）的混合定位控制算法。该算法估计了交互矩阵,采用B-ELM算法估计图像特征与交互矩阵之间的非线性映射函数,进而提出SVSF算法对B-ELM算法的输出矩阵重新估计,提高了控制系统对噪声的鲁棒性。进一步地,考虑超椭圆边界平滑视觉约束边界,基于双曲正切函数设计了一种新的约束函数,通过划分不同的区域来动态调整运动速度,保证图像特征在运动过程中实时可见性。（3）针对视觉伺服控制中图像遮挡和特征干扰的问题,分析了图像特征被遮挡或出现干扰图像点的多种情行。通过双自适应强跟踪卡尔曼滤波器（ASTKF）调整图像观测数据,有效估计遮挡/干扰的视觉状态和图像雅可比矩阵,便于应用于实际的机械臂平台。同时,综合考虑机械臂视觉伺服的运动学和动力学行为,结合相机不确定性及机械臂模型不确定性等因素,分别设计比例微分和滑模（PD-SMC）算法的外环速度控制器和基于自适应滑模（ASMC）算法的内环关节控制器,提出了基于遮挡/干扰的双闭环图像跟踪控制方法,进一步提高视觉跟踪精度和鲁棒性。（4）针对机械臂视觉伺服系统的能耗优化问题,研究了基于自适应动态规划（ADP）算法的图像跟踪控制方法。基于图像与力矩的映射关系,建立了一个完备的系统模型,利用评判网络获得面向最优图像特征误差的反馈控制律,进而与理想跟踪控制律结合,实现视觉跟踪控制。设计状态观测器对包括建模动力学、外部干扰、关节摩擦等的总体不确定项进行实时观测,将观测值引入代价函数对其进行改进。然后,通过ADP算法提出最优图像特征跟踪误差控制策略,同时结合理想控制律,实现机械臂视觉伺服的图像轨迹的最优控制,并利用Lyapunov稳定性定理保证了机械臂视觉伺服系统的稳定性。
法鲁克巴斯特（Farooq Basit）[4]（2021）在《面向纺纱信息物理系统的多层级预测建模与优化研究》文中认为Spinning is the textile industry’s lifeblood industry,where the fibre is drawn out,twisted,and wrapped onto a bobbin by using state-of-the-art twisting techniques.Textile intelligent spinning systems are highly dynamic and flexible embedded systems suitable for uncertain environments and physically distributed with sporadic connectivity.The apparent path for success is to ensure the complex textile spinning systems’ proper functionality and transform the existing system simultaneously by adding new artificial intelligence（AI）based predictive modelling techniques that make the manufacturing industry future-ready.With the continuous development in the manufacturing industry and after Industry 4.0,digitalization in the manufacturing sector achieved an exceptional and remarkable milestone.Blistering evolution like cyber-physical production systems（CPS）and digital twin（DT）are gaining popularity due to their considerable impacts on the realization of intelligent manufacturing.It allows a highly controlled mechanism based on monitored algorithms within which data and information from different systems used to increase self-awareness,self-prediction,and self-configuration functionalities.The cyber-physical systems（CPS）and the industrial Internet of things（IIo T）are the key technologies to reach this goal.Based on a CPS,the smart textile industry is a highly sophisticated and integrated factory involving tight coupling between digital twins（physical and computational）components.Spinning production is a high-speed and continuous manufacturing process that suffers from various dynamic disturbances such as machine breakdown,low quality,and job release delay.Real-time task processing is critical to improving production efficiency to satisfy the requirements of dynamic production.Several other factors also affect the quality of yarn regarding machine efficiency,including spinning speed,friction,tension phasing,fibre diameter,strand thickness,and load.Therefore,by applying predictive analytics using machine learning（ML）techniques（algorithms）,both quantitatively and qualitatively,an effort made to identify failure modes and mitigate downtime.Downtime leads to an increased cost per unit and elevated operating costs despite the loss in revenue,and low maintenance strategies can reduce a plant’s overall production capacity between 10%—30%.Two main key factors considered during the implementation of our proposed model for predictive modelling and optimization.The first factor in developing a prognostics and health monitoring system（PHM）for device,system,and system of systems（So S）is to identify critical components and their impact on spinning frame performance.The second factor considered for the drawing workshop is intelligent transportation scheduling of the sliver cans distribution,which is inevitable in these smart spinning CPS to improve production efficiency and increase the return of investment（ROI）ratio.Further,intelligent virtual equivalents were generated during the integration of digital twins with CPS that reflect the physical structures’ actions and offer predictive insights for advanced decision-making.This thesis provides a systematic framework to integrate cyber-physical systems different perspectives within the context of textile spinning shop-floor from device-level（DL）,system-level（SL）,and system of systemslevel（So SL）maintenance applications,i.e.predictive control,modelling and optimization solution.Different case studies have been provided to demonstrate the proposed model’s feasibility for the high-tech spinning industry:i.In the first case study,the author presents a machine learning approach for condition monitoring（CM）on a DL,based on a regularised neural network using automated diagnostics for spinning manufacturing.Digitalization encapsulates the importance of machine CM,which is subjected to predictive analytics for realizing significant improvements in the performance and reliability of rotating equipment,i.e.,spinning.A part of the thesis contributes to finding disturbances in a running system through real-time data sensing and signal processing via the IIo T.Because the controlled sensor network comprises different critical components（device-level）of the same type of machines,a multi-sensor performance assessment and prediction strategy were developed for the system,which worked as intelligent maintenance and diagnostic.ii.In the second case study,a new data-driven prescriptive maintenance and architectural impulse on an SL,based on a regularised neural network using predictive analytics,proposed successfully for ring-spinning.The paradigm shift in computational infrastructures enormously pressured large-scale linear and non-linear automated assembly systems to eliminate and cut down unscheduled downtime and unexpected stoppages.The fundamental process of predictive modelling is PHM,and it is the tool resulting in the development of many algorithms to predict the remaining useful life（RUL）of industrial equipment,hence improving its efficiency.The spinning sensor network designed for the scheduling process comprises different critical components of the same spinning machine frames containing more than thousands of spindles attached to them.Results showed that it operates with a relatively less amount of training data sets but takes advantage of large volumes of data.This integrated system aims to prognosticate abnormalities,disturbances,and failures by providing condition-based monitoring for each component.iii.In the third case study,aiming at the path-planning and decision-making problem,multiAGV have played an increasingly important role in the multi-stage industries,.e.g.,textile spinning.We recast a framework to investigate the improved genetic algorithm on multi-AGV path optimization within spinning drawing frames to solve the complex multi-AGV manoeuvring scheduling decision and path planning problem on So SL.The study reported in this chapter simplifies the scheduling model to meet the draw-out（drawing）workshop’s real-time application requirements.According to the characteristics of decision variables,the model divides into two decision variables: time-independent variables and time-dependent variables.The first step is to use a genetic algorithm to solve the AGV resource allocation problem based on the AGV resource pool strategy and specify the sliver can’s transportation task.The second step is to determine the AGV transportation scheduling problem based on the sliver cans-AGV matching information obtained in the first step.One significant advantage of the presented approach is that the fitness function is calculated based on the machine selection strategy,AGV resource pool strategy,and the process constraints,determining the scheduling sequence of the AGVs to deliver cans.Moreover,it discovered that double-path decision-making constraints minimize the total path distance of all AGVs,and minimizing single-path distances of each AGVs exerted.By using the improved GA,simulation results showed that the entire path distance was shortened.iv.The fourth and last case study pointed at a problem that leads to the high complexity of the production management tasks in the multi-stage spinning industry,i.e.manual handling slivercans have many jobs,and there is a long turnover period in their semi-finished products.A novel heuristic research was conducted that considered mixed-flow shops So SL scheduling problems with AGV distribution and path planning to prevent conflict and dead-lock by optimizing distribution efficiency and improving the automation degree of sliver-cans distribution in a drawout workshop.In this context,a cross-region shared resource pool and an inter-regional independent resource pool,two AGV predictive scheduling strategies were established for the ring-spinning combing process.Besides completion time,AGV utilization rate and unit AGV time were also analyzed with the production line’s bottleneck process.In the optimal computational experiment,results proved that a draw frame equipped with multi-AGV and coordinated scheduling optimization significantly improves the distribution efficiency.Moreover,flow-shop predictive modelling for multi-AGV resources is scarce in the literature,even though this modelling also produces a control mode for each AGV and,if essential,a preventive maintenance plan.
徐嘉骏[5]（2021）在《多模态下肢康复机器人的设计与人机交互控制方法研究》文中认为近年来,随着人口老龄化的不断加剧,脑卒中等疾病的发生日渐普遍,由此导致的下肢瘫痪患者也与日俱增。医学研究表明,经过及时有效的康复治疗,患者的下肢运动功能会得到显着改善。然而由于昂贵且稀缺的理疗医师人力资源,传统的康复治疗需要花费大量时间和金钱,为了解决这个问题,人们开发出康复机器人来辅助下肢瘫痪患者的治疗。当前大多数康复机器人驱动缺少灵活可控性,且训练模式单一,不满足康复治疗的高效性与安全性需求。本文设计了一款多模态下肢康复机器人,其配置的磁流变驱动器可以快速响应电流变化,以更低的功耗输出更加灵活柔顺的动力。此外,康复机器人与人体紧密交互,人体状态对机器人运动起着关键作用,本文在此多模态机器人基础上设计了几种人机交互控制方案,对促进下肢瘫痪患者的康复疗效具有重要意义。该项研究内容主要包含以下几个方面:首先,设计并实现了一种多模态下肢康复机器人。经过对现有下肢康复机器人的调研,面向下肢瘫痪患者康复运动的需求,设计了机器人的机械结构,所设计的机器人能够为患者提供坐卧姿态的康复训练,为患肢提供四自由度运动,分别是髋关节外展/内收、髋关节屈曲/伸展、膝关节屈曲/伸展和踝关节背屈/跖屈。为了让磁流变驱动器在结构紧凑的基础上输出高扭矩动力,运用有限元方法优化其结构,并且通过理论分析与实验验证探究其性能。利用磁流变驱动器柔顺输出的优势,结合人机交互控制算法,实现机器人多种工作模式,包括被动模式和主动模式。在被动模式下,磁流变驱动器用作离合器,将动力传递给机器人关节,从而带动肢体的运动。在主动模式下,患者肢体主动引导机器人,磁流变驱动器用作制动器提供阻尼,帮助患者进行抗阻训练,达到增强肌肉的目的。完成机器人设计后,构建人机耦合模型并进行生物动力学分析,制造机器人样机,搭建控制系统,为进一步的探索奠定基础。其次,提出了一种基于人体运动意图估算的多模态人机交互控制方法。人体运动意图可以通过皮肤表面肌电信号准确、快速反映,本文建立了一个估算人体关节扭矩的肌电信号驱动肌肉骨骼模型,并且使用生物动力学分析平台AnyBody软件优化模型参数。随之提出了一种自适应阻抗控制器,可以根据人体运动意图,智能选择符合患者康复需求的训练模式,并在不同模式之间自动切换。经过实验验证,提出的控制策略可以根据患者的运动意图生成适当的机器人助力或阻力强度,从而实现按需辅助训练。最后,建立了一个基于强化学习的机器人镜像治疗框架。该镜像疗法通过主从式机器人系统实现,供下肢偏瘫患者进行康复训练。训练时,患者健肢穿戴主动机器人,患肢穿戴从动机器人。健肢带动主动机器人运动,健肢动作由机器人为媒介传递至患侧,患肢在从动机器人辅助下重复健肢动作,形成机器人镜像治疗。其中,从动机器人采用强化学习控制器,探索患肢的最佳康复策略,旨在使患肢肌肉活动度最大化,同时减少镜像传递的轨迹跟踪误差。此外,本文还提出了一个异步深度确定性策略梯度算法,以适应不同患者在多个机器人平台上并行训练,加快数据采集,减少学习时间,提高康复效率。通过实验表明,该机器人镜像治疗系统可以帮助不同偏瘫患者在保证安全的前提下进行自主训练,并快速取得最佳康复疗效。综上所述,本文提出的下肢康复机器人可以根据用户运动能力,灵活调整训练模态,提供适当训练强度,为不同的下肢瘫痪患者提供更高效的康复训练。
张慧[6]（2021）在《环境约束下冗余度机械臂在线运动规划研究》文中研究表明在面向环境约束下多功能维护机械臂操作与作业任务中,在线运动规划能够使机器人根据有限传感资源对任务进行合理分解,并自动做出安全、有序、平稳的动作。目前在线运动规划在理论研究中存在局限性,如收敛速度慢、避障依赖模型、关节轨迹规划复杂度高等问题,一定程度上限制了运动规划理论在多功能维护臂上的实际应用。另外,现有机器人系统以上层运动学开放接口为主,有利于融合先进智能算法实现机器人的自动化和智能化。因此,致力于在线运动规划方法的研究对于维护机械臂运动规划能力提升和在线运动规划理论进一步发展具有十分重要的现实意义。本文以环境约束下多功能维护臂面向任务操作背后引申出的运动规划基础理论为研究切入点,基于运动学展开对平面冗余机械臂动态环境下运动规划、三维空间构型冗余度机械臂运动规划、关节角加速度规划和多臂运动规划等四个方面进行研究。首先,在平面冗余机械臂运动规划研究方面,本文针对传统规划方法收敛速度慢的问题,设计了一种基于末端位姿误差的自适应虚拟控制器,提高了规划方法的收敛速度。将运动观测、路径预测融入到运动规划中,研究了基于样条滤波融合多项式的运动路径拟合方法。利用相似度评判标准,验证了路径拟合和预测方法的精度和有效性。定义了可行性最短路径判断标准,确保末端运动路径可达性。同时,提出基于模型的局部旋转坐标法的末端执行器避障方法,克服了传统避障方法小范围避障、易陷入局部最小值等不足。最后形成了平面冗余度机械臂动态环境下在线运动规划算法。其次,三维空间构型冗余度机械臂在线运动规划研究方面,本文基于广义逆运动学,采用单神经元PID对末端执行器位姿误差进行建模,利用无监督式主成分分析在线学习规则自适应调整神经元权重系数,无需误差作为导师信号,通过神经元权重之间的竞争使整个神经元模型达到稳定状态,实现了机器人起始运动平滑、快速收敛性,避免了迭代法起始增益大、易失稳问题。采用能量描述机械臂运动状态,为末端执行器避障定义了描述绕过障碍物运动的能量和描述趋向于目标运动的能量,对机械臂臂杆定义了描述保持与障碍物安全距离的能量和描述远离障碍物运动的能量,设计了不同能量之间平滑转化的连续可导的S函数,保证了机械臂在有动态障碍物环境下的安全、无碰撞。提出了末端执行器避障姿态调整方式,避免了机械臂因末端姿态问题导致避障失败。另外,融合了自适应实时学习算法和基于能量概念的避障手段,形成了三维空间构型机械臂在线运动规划方法。然后,关节角加速度规划研究方面,本文定义了笛卡尔空间位姿误差模型,建立了以广义逆运动学关节空间映射关系,证明了冗余度机械臂零空间与主任务向量之间的垂直正交、线性无关关系。提出将逆运动学映射形成的关节速度视为系统误差,进而推导出关节角加速度模型,简化了系统高度非线性复杂项,并实现规划问题向控制问题的转化。基于高阶滑模控制理论,设计了双曲正切超扭曲控制算法,并作为控制输入实时抑制系统扰动、保证系统收敛,消除了由传统高阶滑模引起的关节震动。为防止出现积分饱和,设计了以零空间速度和系统误差为变量的增益函数,结合控制输入实时积分,实现关节角加速度的实时控制。利用李雅普诺夫理论详细推导并分析了系统稳定性和参数的取值范围。讨论了各参数对收敛性、末端执行器运动路径的影响,比较了所提方法和传统方法在冗余度操作臂零空间避障中的性能。再次,冗余度多臂在线运动规划方法研究方面,本文研究了具有公共运动物理耦合的冗余度多臂机器人的规划问题,将多臂机器人任务划分为独立操作任务和协作操作任务。基于所提基于能量转化策略避障方法,实现了多臂机器人无碰撞独立追踪任务。在多臂协作任务中,提出了子基法划分机器人构型方式,利用阻尼最小二乘雅可比逆,分析了多臂机器人基于子基法的逆运动学求解。基于内星学习规则,采用自组织竞争神经网络模型思想,设计了面向多臂协调任务的运动规划方法,提高了多臂运动的同步性和协调性。利用李雅普诺夫理论并根据内星学习规则原理,探讨了所提基于自组织竞争神经网络的运动规划方法的稳定性和收敛性。讨论了所提规划方法在冗余度双臂和三臂机器人上的应用,以及在具有固定物理耦合的多机械臂上的适用性和同步性。最后,设计并搭建了基于单目视觉引导的7自由度单臂机器人和基于深度视觉的13自由度双臂机器人实验平台。验证了所提平面冗余机械臂运动规划、基于能量转化避障策略的运动规划、关节角加速度规划和基于自组织竞争神经网络的多臂运动规划等方法的可行性、有效性。实时运动目标追踪对比实验验证了所提方法中,局部旋转坐标方法大范围转向避障、虚拟控制器快速收敛性、基于能量的无模型避障、基于主成分分析学习规则单神经元PID自主规划快速平滑特性。关节角加速度规划实验验证了所提方法动态障碍物环境下运动目标追踪过程中关节角度、角速度轨迹的光滑性和误差的收敛性。六种双臂协调操作实验验证了多臂规划方法的可行性、同步性。
Zhaoxuan LIU,Kaiquan CAI,Yanbo ZHU[7]（2021）在《Civil unmanned aircraft system operation in national airspace: A survey from Air Navigation Service Provider perspective》文中研究表明Unmanned Aircraft Systems（UASs） have advanced technologically and surged exponentially over recent years. Currently, due to safety concerns, most civil operations of UAS are conducted in low-level uncontrolled area or in segregated controlled airspace. As the industry progresses, both operational and technological capabilities have matured to the point where UASs are expected to gain greater freedom of access to both controlled and uncontrolled airspace. Extensive technical and regulatory surveys have been conducted to enable the expanded operations. However, most surveys are derived from the perspective of UAS own operating mechanism and barely consider interactions of their non-segregated activities with the Air Traffic Management（ATM） system. Hence, to fill the gap, this paper presents a survey conducted from the perspective of Air Navigation Service Provider（ANSP）, which serves to accommodate these new entrants to the overall national airspace while continuing flight safety and efficiency. The primary objectives of this paper are to:（A） describe what typical ANSP-supplied UAS Traffic Management（UTM） architecture is required to facilitate all types of civil UAS operations;（B） identify three major ANSP considerations on how UAS can be accommodated safely in civil airspace;（C） outline future directions and challenges related with UAS operations for the ANSP.
Xinwei WANG,Jie LIU,Xichao SU,Haijun PENG,Xudong ZHAO,Chen LU[8]（2020）在《A review on carrier aircraft dispatch path planning and control on deck》文中研究指明As an important part in sortie/recovery process, the dispatch of carrier aircraft not only affects the sortie/recovery efficiency and safety, but also has severe influence on the carrier’s combat efficiency and the comprehensive support capability. Path planning is the key to improve the efficiency and safety during the dispatch process. The main purpose of this paper is to propose a comprehensive investigation of techniques and research progress for the carrier aircraft’s dispatch path planning on the deck. Three different dispatch modes of carrier aircraft and the corresponding modeling technologies are investigated, and the aircraft’s dispatch path planning techniques and algorithms have been classified into different classes. Moreover, their assumptions and drawbacks have been discussed for single aircraft and multiple aircraft. To make the research work more comprehensive, the corresponding tracking control methodologies are also discussed. Finally, due to the similarity of path planning problem between the carrier aircraft’s dispatch and those in other fields,this paper provides an exploratory prospect of the knowledge or method learned from other fields.
Xiaohu YOU,Cheng-Xiang WANG,Jie HUANG,Xiqi GAO,Zaichen ZHANG,Mao WANG,Yongming HUANG,Chuan ZHANG,Yanxiang JIANG,Jiaheng WANG,Min ZHU,Bin SHENG,Dongming WANG,Zhiwen PAN,Pengcheng ZHU,Yang YANG,Zening LIU,Ping ZHANG,Xiaofeng TAO,Shaoqian LI,Zhi CHEN,Xinying MA,Chih-Lin I,Shuangfeng HAN,Ke LI,Chengkang PAN,Zhimin ZHENG,Lajos HANZO,Yingjie Jay GUO,Zhiguo DING,Harald HAAS,Wen TONG,Peiying ZHU,Ganghua YANG,Jun WANG,Erik G.LARSSON,Hien Quoc NGO,Wei HONG,Haiming WANG,Debin HOU,Jixin CHEN,Zhe CHEN,Zhangcheng HAO,Geoffrey Ye LI,Rahim TAFAZOLLI,Yue GAO,H.Vincent POOR,Gerhard P.FETTWEIS,Ying-Chang LIANG[9]（2021）在《Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts》文中研究说明The fifth generation（5G） wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability,and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation（6G） wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle（UAV） communication networks, thus achieving a space-airground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6GHz, millimeter wave（mmWave）, terahertz（THz）,and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence（AI） and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.
Mohamed Mostafa Abulmaati Elshami[10]（2020）在《面向目标跟踪的DELTA机器人控制方法研究》文中指出近年来并联Delta机器人运动学和动力学研究受到越来越多的关注。为构建不受人类干扰的,具有完成所需任务能力的自动控制系统,需要建立它的运动学、动力学和运动控制模型。在处理机器人（尤其是串行结构的机器人）时,自主操作是一个非常普遍问题,研究人员对其给予了极大的关注,以使其成为全自动机器人,并具有在不同情况下不要与人类互动便可以做出决定的能力。但对并行Delta机器人而言相关研究相对较少。本文针对这一并联机器人系统的精度、刚度的提高和改进问题,并试图增加其智能化程度。本文挖掘并联Delta机器人的潜力,使它每分钟可以执行200个操作周期,可用于检查PCB板的工业生产线中的产品,PCB要求短时间内进行测试数百万个电子元器件。为在这种并联Delta机器人上增加目标检测和跟踪功能,扩大其使用范围,首先开展了末端执行器的运动学分析,寻找一种并联Delta机器人自动系统适用的运动学控制方法。在对系统建模中,多体系统（MBS）建模是使控制算法考虑系统所有组成物体的一种运动学和动力学方法。本文将MBS建模和目标检测结合起来实现了并联Delta机器人的自主跟踪。为获取和处理摄像机视频信号中的非结构化运动目标,需要用跟踪系统对其进行观察,然后基于摄像机传感器获得的信息获得精确的控制信号,并利用它将并联Delta机器人末端执行器引导到目标的确切位置上。本文建立了一个目标检测算法来使并联Delta机器人处理目标的位置,该算法不需要复杂计算,具有较高的效率,在线应用时,据此搭建的控制系统相应特性良好,可以适应目标运动的突然变化,它拓展了此类机器人的使用范围。
二、Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅱ)（论文开题报告）
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三、Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅱ)（论文提纲范文）
（1）New innovations in pavement materials and engineering:A review on pavement engineering research 2021（论文提纲范文）
| 1. Introduction |
| (1) With the society development pavement engineering facing unprecedented opportunities and challenges |
| (2) With the modern education development pavement engineering facing unprecedented accumulation of scientific manpower and literature |
| 2. Asphalt binder performance and modeling |
| 2.1. Binder damage,healing and aging behaviors |
| 2.1.1. Binder healing characterization and performance |
| 2.1.1. 1. Characterizing approaches for binder healing behavior. |
| 2.1.1. 2. Various factors influencing binder healing performance. |
| 2.1.2. Asphalt aging:mechanism,evaluation and control strategy |
| 2.1.2. 1. Phenomena and mechanisms of asphalt aging. |
| 2.1.2. 2. Simulation methods of asphalt aging. |
| 2.1.2. 3. Characterizing approaches for asphalt aging behavior. |
| 2.1.2. 4. Anti-aging additives used for controlling asphalt aging. |
| 2.1.3. Damage in the characterization of binder cracking performance |
| 2.1.3. 1. Damage characterization based on rheological properties. |
| 2.1.3. 2. Damage characterization based on fracture properties. |
| 2.1.4. Summary and outlook |
| 2.2. Mechanism of asphalt modification |
| 2.2.1. Development of polymer modified asphalt |
| 2.2.1. 1. Strength formation of modified asphalt. |
| 2.2.1. 2. Modification mechanism by molecular dynamics simulation. |
| 2.2.1. 3. The relationship between microstructure and properties of asphalt. |
| 2.2.2. Application of the MD simulation |
| 2.2.2. 1. Molecular model of asphalt. |
| 2.2.2. 2. Molecular configuration of asphalt. |
| 2.2.2. 3. Self-healing behaviour. |
| 2.2.2. 4. Aging mechanism. |
| 2.2.2. 5. Adhesion mechanism. |
| 2.2.2. 6. Diffusion behaviour. |
| 2.2.3. Summary and outlook |
| 2.3. Modeling and application of crumb rubber modified asphalt |
| 2.3.1. Modeling and mechanism of rubberized asphalt |
| 2.3.1. 1. Rheology of bituminous binders. |
| 2.3.1. 2. Rheological property prediction of CRMA. |
| 2.3.2. Micromechanics-based modeling of rheological properties of CRMA |
| 2.3.2. 1. Composite system of CRMA based on homogenization theory. |
| 2.3.2. 2. Input parameters for micromechanical models of CRMA. |
| 2.3.2. 3. Analytical form of micromechanical models of CRMA. |
| 2.3.2. 4. Future recommendations for improving micro-mechanical prediction performance. |
| 2.3.3. Design and performance of rubberized asphalt |
| 2.3.3. 1. The interaction between rubber and asphalt fractions. |
| 2.3.3. 2. Engineering performance of rubberized asphalt. |
| 2.3.3. 3. Mixture design. |
| 2.3.3. 4. Warm mix rubberized asphalt. |
| 2.3.3. 5. Reclaiming potential of rubberized asphalt pavement. |
| 2.3.4. Economic and Environmental Effects |
| 2.3.5. Summary and outlook |
| 3. Mixture performance and modeling of pavement materials |
| 3.1. The low temperature performance and freeze-thaw damage of asphalt mixture |
| 3.1.1. Low temperature performance of asphalt mixture |
| 3.1.1. 1. Low temperature cracking mechanisms. |
| 3.1.1. 2. Experimental methods to evaluate the low temperature performance of asphalt binders. |
| 3.1.1. 3. Experimental methods to evaluate the low temperature performance of asphalt mixtures. |
| 3.1.1. 4. Low temperature behavior of asphalt materials. |
| 3.1.1.5.Effect factors of low temperature performance of asphalt mixture. |
| 3.1.1. 6. Improvement of low temperature performance of asphalt mixture. |
| 3.1.2. Freeze-thaw damage of asphalt mixtures |
| 3.1.2. 1. F-T damage mechanisms. |
| 3.1.2. 2. Evaluation method of F-T damage. |
| 3.1.2. 3. F-T damage behavior of asphalt mixture. |
| (1) Evolution of F-T damage of asphalt mixture |
| (2) F-T damage evolution model of asphalt mixture |
| (3) Distribution and development of asphalt mixture F-T damage |
| 3.1.2. 4. Effect factors of freeze thaw performance of asphalt mixture. |
| 3.1.2. 5. Improvement of freeze thaw resistance of asphalt mixture. |
| 3.1.3. Summary and outlook |
| 3.2. Long-life rigid pavement and concrete durability |
| 3.2.1. Long-life cement concrete pavement |
| 3.2.1. 1. Continuous reinforced concrete pavement. |
| 3.2.1. 2. Fiber reinforced concrete pavement. |
| 3.2.1. 3. Two-lift concrete pavement. |
| 3.2.2. Design,construction and performance of CRCP |
| 3.2.2. 1. CRCP distress and its mechanism. |
| 3.2.2. 2. The importance of crack pattern on CRCP performance. |
| 3.2.2. 3. Corrosion of longitudinal steel. |
| 3.2.2. 4. AC+CRCP composite pavement. |
| 3.2.2. 5. CRCP maintenance and rehabilitation. |
| 3.2.3. Durability of the cementitious materials in concrete pavement |
| 3.2.3. 1. Deterioration mechanism of sulfate attack and its in-fluence on concrete pavement. |
| 3.2.3. 2. Development of alkali-aggregate reaction in concrete pavement. |
| 3.2.3. 3. Influence of freeze-thaw cycles on concrete pavement. |
| 3.2.4. Summary and outlook |
| 3.3. Novel polymer pavement materials |
| 3.3.1. Designable PU material |
| 3.3.1. 1. PU binder. |
| 3.3.1.2.PU mixture. |
| 3.3.1. 3. Material genome design. |
| 3.3.2. Novel polymer bridge deck pavement material |
| 3.3.2. 1. Requirements for the bridge deck pavement material. |
| 3.3.2.2.Polyurethane bridge deck pavement material(PUBDPM). |
| 3.3.3. PU permeable pavement |
| 3.3.3. 1. Permeable pavement. |
| 3.3.3. 2. PU porous pavement materials. |
| 3.3.3. 3. Hydraulic properties of PU permeable pavement materials. |
| 3.3.3. 4. Mechanical properties of PU permeable pavement ma-terials. |
| 3.3.3. 5. Environmental advantages of PU permeable pavement materials. |
| 3.3.4. Polyurethane-based asphalt modifier |
| 3.3.4. 1. Chemical and genetic characteristics of bitumen and polyurethane-based modifier. |
| 3.3.4. 2. The performance and modification mechanism of polyurethane modified bitumen. |
| 3.3.4. 3. The performance of polyurethane modified asphalt mixture. |
| 3.3.4. 4. Environmental and economic assessment of poly-urethane modified asphalt. |
| 3.3.5. Summary and outlook |
| 3.4. Reinforcement materials for road base/subrgrade |
| 3.4.1. Flowable solidified fill |
| 3.4.1. 1. Material composition design. |
| 3.4.1. 2. Performance control. |
| 3.4.1. 3. Curing mechanism. |
| 3.4.1. 4. Construction applications. |
| 3.4.1.5.Environmental impact assessment. |
| 3.4.1. 6. Development prospects and challenges. |
| 3.4.2. Stabilization materials for problematic soil subgrades |
| 3.4.2.1.Stabilization materials for loess. |
| 3.4.2. 2. Stabilization materials for expansive soil. |
| 3.4.2. 3. Stabilization materials for saline soils. |
| 3.4.2. 4. Stabilization materials for soft soils. |
| 3.4.3. Geogrids in base course reinforcement |
| 3.4.3. 1. Assessment methods for evaluating geogrid reinforce-ment in flexible pavements. |
| (1) Reinforced granular material |
| (2) Reinforced granular base course |
| 3.4.3. 2. Summary. |
| 3.4.4. Summary and outlook |
| 4. Multi-scale mechanics |
| 4.1. Interface |
| 4.1.1. Multi-scale evaluation method of interfacial interaction between asphalt binder and mineral aggregate |
| 4.1.1. 1. Molecular dynamics simulation of asphalt adsorption behavior on mineral aggregate surface. |
| 4.1.1. 2. Experimental study on absorption behavior of asphalt on aggregate surface. |
| 4.1.1. 3. Research on evaluation method of interaction between asphalt and mineral powder. |
| (1) Rheological mechanical method |
| (2) Microscopic test |
| 4.1.1. 4. Study on evaluation method of interaction between asphalt and aggregate. |
| 4.1.2. Multi-scale numerical simulation method considering interface effect |
| 4.1.2. 1. Multi-scale effect of interface. |
| 4.1.2. 2. Study on performance of asphalt mixture based on micro nano scale testing technology. |
| 4.1.2. 3. Study on the interface between asphalt and aggregate based on molecular dynamics. |
| 4.1.2. 4. Study on performance of asphalt mixture based on meso-mechanics. |
| 4.1.2. 5. Mesoscopic numerical simulation test of asphalt mixture. |
| 4.1.3. Multi-scale investigation on interface deterioration |
| 4.1.4. Summary and outlook |
| 4.2. Multi-scales and numerical methods in pavement engineering |
| 4.2.1. Asphalt pavement multi-scale system |
| 4.2.1. 1. Multi-scale definitions from literatures. |
| 4.2.1. 2. A newly-proposed Asphalt Pavement Multi-scale System. |
| (1) Structure-scale |
| (2) Mixture-scale |
| (3) Material-scale |
| 4.2.1. 3. Research Ideas in the newly-proposed multi-scale sys- |
| 4.2.2. Multi-scale modeling methods |
| 4.2.2. 1. Density functional theory (DFT) calculations. |
| 4.2.2. 2. Molecular dynamics (MD) simulations. |
| 4.2.2. 3. Composite micromechanics methods. |
| 4.2.2. 4. Finite element method (FEM) simulations. |
| 4.2.2. 5. Discrete element method (DEM) simulations. |
| 4.2.3. Cross-scale modeling methods |
| 4.2.3. 1. Mechanism of cross-scale calculation. |
| 4.2.3. 2. Multi-scale FEM method. |
| 4.2.3. 3. FEM-DEM coupling method. |
| 4.2.3. 4. NMM family methods. |
| 4.2.4. Summary and outlook |
| 4.3. Pavement mechanics and analysis |
| 4.3.1. Constructive methods to pavement response analysis |
| 4.3.1. 1. Viscoelastic constructive models. |
| 4.3.1. 2. Anisotropy and its characterization. |
| 4.3.1. 3. Mathematical methods to asphalt pavement response. |
| 4.3.2. Finite element modeling for analyses of pavement mechanics |
| 4.3.2. 1. Geometrical dimension of the FE models. |
| 4.3.2. 2. Constitutive models of pavement materials. |
| 4.3.2. 3. Variability of material property along with different directions. |
| 4.3.2. 4. Loading patterns of FE models. |
| 4.3.2. 5. Interaction between adjacent pavement layers. |
| 4.3.3. Pavement mechanics test and parameter inversion |
| 4.3.3. 1. Nondestructive pavement modulus test. |
| 4.3.3. 2. Pavement structural parameters inversion method. |
| 4.3.4. Summary and outlook |
| 5. Green and sustainable pavement |
| 5.1. Functional pavement |
| 5.1.1. Energy harvesting function |
| 5.1.1. 1. Piezoelectric pavement. |
| 5.1.1. 2. Thermoelectric pavement. |
| 5.1.1. 3. Solar pavement. |
| 5.1.2. Pavement sensing function |
| 5.1.2. 1. Contact sensing device. |
| 5.1.2.2.Lidar based sensing technology. |
| 5.1.2. 3. Perception technology based on image/video stream. |
| 5.1.2. 4. Temperature sensing. |
| 5.1.2. 5. Traffic detection based on ontology perception. |
| 5.1.2. 6. Structural health monitoring based on ontology perception. |
| 5.1.3. Road adaptation and adjustment function |
| 5.1.3. 1. Radiation reflective pavement.Urban heat island effect refers to an increased temperature in urban areas compared to its surrounding rural areas (Fig.68). |
| 5.1.3. 2. Catalytical degradation of vehicle exhaust gases on pavement surface. |
| 5.1.3. 3. Self-healing pavement. |
| 5.1.4. Summary and outlook |
| 5.2. Renewable and sustainable pavement materials |
| 5.2.1. Reclaimed asphalt pavement |
| 5.2.1. 1. Hot recycled mixture technology. |
| 5.2.1. 2. Warm recycled mix asphalt technology. |
| 5.2.1. 3. Cold recycled mixture technology. |
| (1) Strength and performance of cold recycled mixture with asphalt emulsion |
| (2) Variability analysis of asphalt emulsion |
| (3) Future prospect of cold recycled mixture with asphalt emulsion |
| 5.2.2. Solid waste recycling in pavement |
| 5.2.2. 1. Construction and demolition waste. |
| (1) Recycled concrete aggregate |
| (2) Recycled mineral filler |
| 5.2.2. 2. Steel slag. |
| 5.2.2. 3. Waste tire rubber. |
| 5.2.3. Environment impact of pavement material |
| 5.2.3. 1. GHG emission and energy consumption of pavement material. |
| (1) Estimation of GHG emission and energy consumption |
| (2) Challenge and prospect of environment burden estimation |
| 5.2.3. 2. VOC emission of pavement material. |
| (1) Characterization and sources of VOC emission |
| (2) Health injury of VOC emission |
| (3) Inhibition of VOC emission |
| (4) Prospect of VOC emission study |
| 5.2.4. Summary and outlook |
| 6. Intelligent pavement |
| 6.1. Automated pavement defect detection using deep learning |
| 6.1.1. Automated data collection method |
| 6.1.1. 1. Digital camera. |
| 6.1.1.2.3D laser camera. |
| 6.1.1. 3. Structure from motion. |
| 6.1.2. Automated road surface distress detection |
| 6.1.2. 1. Image processing-based method. |
| 6.1.2. 2. Machine learning and deep learning-based methods. |
| 6.1.3. Pavement internal defect detection |
| 6.1.4. Summary and outlook |
| 6.2. Intelligent pavement construction and maintenance |
| 6.2.1. Intelligent pavement construction management |
| 6.2.1. 1. Standardized integration of BIM information resources. |
| 6.2.1. 2. Construction field capturing technologies. |
| 6.2.1. 3. Multi-source spatial data fusion. |
| 6.2.1. 4. Research on schedule management based on BIM. |
| 6.2.1. 5. Application of BIM information management system. |
| 6.2.2. Intelligent compaction technology for asphalt pavement |
| 6.2.2. 1. Weakened IntelliSense of ICT. |
| 6.2.2. 2. Poor adaptability of asphalt pavement compaction index. |
| (1) The construction process of asphalt pavement is affected by many complex factors |
| (2) Difficulty in model calculation caused by jumping vibration of vibrating drum |
| (3) There are challenges to the numerical stability and computational efficiency of the theoretical model |
| 6.2.2. 3. Insufficient research on asphalt mixture in vibratory rolling. |
| 6.2.3. Intelligent pavement maintenance decision-making |
| 6.2.3. 1. Basic functional framework. |
| 6.2.3. 2. Expert experience-based methods. |
| 6.2.3. 3. Priority-based methods. |
| 6.2.3. 4. Mathematical programming-based methods. |
| 6.2.3. 5. New-gen machine learning-based methods. |
| 6.2.4. Summary and outlook |
| (1) Pavement construction management |
| (2) Pavement compaction technology |
| (3) Pavement maintenance decision-making |
| 7. Conclusions |
| Conflict of interest |
（2）Soft Robotics:Morphology and Morphology-inspired Motion Strategy（论文提纲范文）
| I.Introduction |
| II.Soft Continuum Robot |
| III.Soft Gripper |
| IV.Soft Mobile Robot |
| V.Conclusion and Discussion |
（3）基于图像的机械臂视觉伺服定位及跟踪控制方法研究（论文提纲范文）
| 摘要 |
| abstract |
| 第1章 绪论 |
| 1.1 课题研究背景及意义 |
| 1.2 视觉伺服系统的研究概述 |
| 1.3 视觉伺服系统的分类概述 |
| 1.3.1 基于相机数目和安装位置的分类 |
| 1.3.2 基于反馈信号和相机参数的分类 |
| 1.3.3 基于视觉控制信号的分类 |
| 1.4 基于图像的机械臂视觉伺服关键技术研究现状 |
| 1.4.1 基于图像的视觉伺服运动学控制 |
| 1.4.2 基于图像的视觉伺服动力学控制 |
| 1.4.3 带有约束和不确定因素的视觉伺服系统 |
| 1.5 本文的主要研究内容 |
| 第2章 考虑图像噪声影响的机械臂视觉伺服定位控制方法 |
| 2.1 引言 |
| 2.2 问题描述 |
| 2.3 改进ARKF算法的图像雅可比矩阵估计方法 |
| 2.3.1 传统的KF算法的图像雅克比矩阵估计问题 |
| 2.3.2 改进ARKF算法的视觉伺服控制 |
| 2.3.3 数值仿真与分析 |
| 2.4 基于MCKF算法的图像雅可比矩阵在线估计方法 |
| 2.4.1 MCKF方法概述 |
| 2.4.2 基于MCKF算法的估计方法 |
| 2.4.3 数值仿真与分析 |
| 2.4.4 实验分析 |
| 2.5 本章小结 |
| 第3章 基于图像特征约束的视觉伺服混合定位控制方法 |
| 3.1 引言 |
| 3.2 B-ELM-SVSF算法的雅克比矩阵估计方法 |
| 3.2.1 B-ELM函数逼近方法 |
| 3.2.2 SVSF状态估计方法 |
| 3.3 考虑视野约束的视觉伺服控制方法 |
| 3.4 基于特征约束的视觉伺服系统混合定位控制系统 |
| 3.5 数值仿真与分析 |
| 3.5.1 不同图像噪声类型的数值仿真 |
| 3.5.2 B-ELM-SVSF算法与其它算法对比分析 |
| 3.5.3 视野约束性能的仿真分析 |
| 3.6 实验分析 |
| 3.7 本章小结 |
| 第4章 基于双ASTKF算法的视觉伺服双闭环跟踪控制方法 |
| 4.1 引言 |
| 4.2 图像遮挡及图像干扰分析 |
| 4.3 双ASTKF算法的遮挡/干扰图像估计方法 |
| 4.3.1 系统模型描述 |
| 4.3.2 基于ASTKF的状态估计方法 |
| 4.3.3 数值仿真分析 |
| 4.4 基于滑模控制方法的双闭环视觉伺服跟踪控制 |
| 4.4.1 基于PD-SMC的运动控制器设计 |
| 4.4.2 基于ASMC的动力学控制器设计 |
| 4.5 考虑图像遮挡/干扰滤波的视觉伺服双闭环控制方法 |
| 4.6 系统仿真及结果分析 |
| 4.6.1 基于PD-SMC的运动学控制器对比仿真分析 |
| 4.6.2 基于双闭环控制器的视觉伺服仿真分析 |
| 4.7 本章小结 |
| 第5章 基于ADP算法的机械臂视觉伺服最优跟踪控制方法 |
| 5.1 引言 |
| 5.2 问题描述 |
| 5.3 基于ADP算法的机械臂视觉伺服轨迹跟踪控制方法 |
| 5.3.1 基于ADP算法的最优跟踪控制 |
| 5.3.2 稳定性分析 |
| 5.4 改进ADP算法的机械臂视觉伺服轨迹跟踪控制方法 |
| 5.4.1 问题描述 |
| 5.4.2 神经网络观测器设计 |
| 5.4.3 评价网络设计 |
| 5.4.4 稳定性分析 |
| 5.5 数值仿真分析 |
| 5.5.1 基于ADP算法的控制系统仿真分析 |
| 5.5.2 基于改进的ADP算法的控制系统仿真分析 |
| 5.6 本章小结 |
| 第6章 结论与展望 |
| 6.1 全文总结 |
| 6.2 研究展望 |
| 参考文献 |
| 致谢 |
| 作者简历及攻读学位期间发表的学术论文与研究成果 |
（4）面向纺纱信息物理系统的多层级预测建模与优化研究（论文提纲范文）
| DEDICATION |
| ABSTRACT |
| LIST OF ACRONYMS |
| CHAPTER 1 INTRODUCTION |
| 1.1.Background |
| 1.2.Related Work on CPS |
| 1.2.1.CPS and its Application in Textile |
| 1.2.2.Concept and Definition of CPS |
| 1.2.3.Extension for CPS |
| 1.2.4.Layers of CPS |
| 1.3.Research Question |
| 1.3.1.Engineering Challenges |
| 1.3.2.Academic Challenges |
| 1.3.3.Discussion |
| 1.4.Motivation,and Innovation |
| 1.4.1.Motivation |
| 1.4.2.Innovation |
| 1.4.3.Research Scope and Limitations |
| 1.5.Dissertation Structure |
| CHAPTER 2 MULTI-LEVEL DEFINITION AND MAINTENANCE STRATEGY OF SPINNING CPS |
| 2.1.The definition of spinning CPS |
| 2.1.1.Fiber-Flow |
| 2.1.2.Data-Flow |
| 2.1.3.Control Flow |
| 2.2.Components of Spinning CPS |
| 2.2.1.Interoperability |
| 2.2.2.Virtualization |
| 2.2.3.Real-time Decision Support |
| 2.3.Smart Maintenance for Spinning CPS |
| 2.3.1.Maintenance Strategy |
| 2.3.2.Possible Alternative Maintenance Strategies in Textile Industry |
| 2.4.Conclusions |
| CHAPTER3 CONDITION MONITORING FRAMEWORK FOR SPINNING CPS |
| 3.1.Introduction |
| 3.1.1.The Process of Spinning Manufacturing |
| 3.1.2.A Framework of Condition Monitoring CPS |
| 3.2.State of the Art Analytics of CPS |
| 3.3.Maintenance Approach for CM of Spinning Manufacturing |
| 3.3.1.Data-Driven Prognostics and Health Management |
| 3.3.2.Degraded Incipient Sensor Failure Scenarios |
| 3.3.3.Predictive Analytics Using Machine Learning |
| 3.3.4.System Prototype |
| 3.4.Chapter Summary |
| CHAPTER4 PROGNOSTICS AND HEALTH MANAGEMENT FOR HIGH-SPEED SPINNING ROTARY COMPONENTS ON DEVICE-LEVEL |
| 4.1.Introduction |
| 4.2.Problems |
| 4.2.1.High-Speed Highly Dynamic Spinning |
| 4.2.2.Necessary Procedures of Prognostics and Maintenance |
| 4.3.Predictive Maintenance Based on Data-Driven Approach |
| 4.3.1.A PHM Workflow for Spinning CPS Environment |
| 4.3.2.Predictive Analytics Combined with PCA and BPNN |
| 4.4.Case Study |
| 4.4.1.Spinning Cyber-physical Production System |
| 4.4.2.Results and Discussions |
| 4.5.Chapter Summary |
| CHAPTER5 MULTI-AGV SCHEDULING FOR KERNEL AREA TRANSPORT ON SYSTEMS-LEVEL |
| 5.1.Introduction |
| 5.2.Facility Layout Model |
| 5.2.1.Machine Allocation |
| 5.2.2.Machine Distribution Strategy |
| 5.2.3.Path Planning |
| 5.2.4.Mathematical Model |
| 5.3.Multi-AGV Path Planning Scheduling |
| 5.3.1.Process Parameters |
| 5.3.2.Optimization |
| 5.3.3.The Raw Material Scheduling Decision |
| 5.3.4.Algorithm Design |
| 5.3.5.AGV Transport Collection Scheduling Decision |
| 5.4.Experimental Analysis |
| 5.4.1.Comparative Analysis of Two Machine Selection Strategies |
| 5.4.2.Optimization Analysis of Two Resource Pool Strategies |
| 5.4.3.Simulation |
| 5.5.Chapter Summary |
| CHAPTER6 PREDICTIVE SCHEDULING FOR WHOLE SPINNING PLANT ON SYSTEM OF SYSTEMS-LEVEL |
| 6.1.Introduction |
| 6.2.Analysis of the Production Process of the Spinning |
| 6.2.1.Mathematical Model |
| 6.2.2.Model Assumptions |
| 6.2.3.Object Function |
| 6.2.4.Uniqueness Constraint |
| 6.3.Multi-AGV Scheduling Simulation Modeling of Whole Spinning Workshop |
| 6.4.Simulation Model Construction |
| 6.4.1.Machine Module |
| 6.4.2.AVG Transportation Simulation |
| 6.4.3.Scheduling Policy |
| 6.4.4.Simulation Model |
| 6.5.AGV Resource Pool Strategy Based on Bottleneck Analysis |
| 6.5.1.Product One(55%/45%): |
| 6.5.2.Product Two(60%/40%): |
| 6.6.Simulation Results Analysis |
| 6.6.1.Comparative Analysis of Two AGV Resource Pool Strategies |
| 6.6.2.Comprehensive Analysis of Multi-AGV Scheduling in the Workshop |
| 6.7.Chapter Summary |
| CHAPTER7 CONCLUSIONS AND FUTURE RECOMMENDATIONS |
| 7.1.Conclusions |
| 7.2.New Contributions |
| 7.3.Future recommendations |
| REFERENCES |
| LIST OF ACADEMIC PUBLICATIONS |
| RESEARCH PROJECT |
| ACKNOWLEDGEMENT |
（5）多模态下肢康复机器人的设计与人机交互控制方法研究（论文提纲范文）
| 摘要 |
| ABSTRACT |
| Chapter1 Introduction |
| 1.1 Background |
| 1.2 Statement of the Problems |
| 1.3 Research Objectives |
| 1.4 Methodology and Significance |
| 1.5 Conclusion |
| Chapter2 Literature Review |
| 2.1 Introduction |
| 2.2 Mechanical Structure |
| 2.2.1 Exoskeleton Robot |
| 2.2.2 End-Effector Robot |
| 2.3 Actuation Method |
| 2.3.1 Electric Motor |
| 2.3.2 Hydraulic Actuator |
| 2.3.3 Pneumatic Actuator |
| 2.3.4 Series Elastic Actuator |
| 2.3.5 Magnetorheological Actuator |
| 2.4 Control Strategy |
| 2.4.1 Position Control |
| 2.4.2 Force Control |
| 2.4.3 Biological Signal Control |
| 2.4.4 Assist-As-Needed Control |
| 2.5 Conclusion |
| Chapter3 Design and Implementation |
| 3.1 Introduction |
| 3.2 Mechanical Design |
| 3.2.1 Standing/Walking Robot with Cylindrical MR Actuators |
| 3.2.2 Standing/Walking Robot with Discal MR Actuators |
| 3.2.3 Sitting/Lying Robot with Discal MR Actuators |
| 3.3 Magnetorheological Actuator |
| 3.4 Biomechanical Analysis |
| 3.5 Control System |
| 3.6 Experiments |
| 3.7 Conclusion |
| Chapter4 Multi-Modal Control Based on Human Motion Intention |
| 4.1 Introduction |
| 4.2 Human Motion Intention Estimation |
| 4.3 Multi-Modal Control |
| 4.3.1 Controller Design |
| 4.3.2 Stability Analysis |
| 4.3.3 Passivity Analysis |
| 4.3.4 Robustness Analysis |
| 4.4 Experiments |
| 4.5 Conclusion |
| Chapter5 Mirror Therapy with Reinforcement Learning |
| 5.1 Introduction |
| 5.2 Mirror Therapy Architecture |
| 5.3 Master Robot Controller |
| 5.4 Slave Robot Controller |
| 5.5 Asynchronous Reinforcement Learning |
| 5.6 Experiments |
| 5.6.1 Validation |
| 5.6.2 Comparison |
| 5.7 Conclusion |
| Chapter6 Conclusion and Future Work |
| 6.1 Conclusion |
| 6.2 Future Work |
| 参考文献 |
| 致谢 |
| 在读期间发表的学术论文与取得的其他研究成果 |
| 中文简介 |
| 研究背景 |
| 存在问题 |
| 研究目标 |
| 方法和意义 |
（6）环境约束下冗余度机械臂在线运动规划研究（论文提纲范文）
| 摘要 |
| Abstract |
| 第1章 绪论 |
| 1.1 课题背景及研究意义 |
| 1.1.1 课题研究背景 |
| 1.1.2 课题研究意义 |
| 1.2 运动规划研究现状综述 |
| 1.2.1 冗余度单机械臂运动规划研究现状 |
| 1.2.2 冗余度多臂运动规划研究现状 |
| 1.3 运动规划文献综述的分析 |
| 1.3.1 冗余度单机械臂运动规划方面研究 |
| 1.3.2 冗余度多机械臂运动规划方面研究 |
| 1.4 主要研究内容 |
| 第2章 基于虚拟控制器的平面冗余臂运动规划研究 |
| 2.1 引言 |
| 2.2 无障碍物环境下运动规划 |
| 2.2.1 逆运动学映射 |
| 2.2.2 虚拟控制器设计 |
| 2.3 动态环境下运动规划 |
| 2.3.1 观察、路径预测、路径规划 |
| 2.3.2 机械臂避障方法 |
| 2.3.3 动态环境下运动规划算法 |
| 2.4 仿真验证 |
| 2.4.1 路径预测方法的仿真验证 |
| 2.4.2 局部旋转坐标法避障验证 |
| 2.4.3 平面臂运动规划算法仿真 |
| 2.5 本章小结 |
| 第3章 基于能量转化策略避障的三维运动规划研究 |
| 3.1 引言 |
| 3.2 广义逆运动学 |
| 3.3 无障碍物环境约束 |
| 3.4 基于单神经元自适应PID模型 |
| 3.5 基于主成分分析的收敛性分析 |
| 3.6 有障碍物环境约束 |
| 3.6.1 基于能量转化的末端避障规划 |
| 3.6.2 避障过程中末端执行器姿态调整 |
| 3.6.3 基于能量转化的机械臂手臂避障 |
| 3.7 仿真验证 |
| 3.7.1 无障碍物环境下仿真 |
| 3.7.2 动态障碍物环境下仿真 |
| 3.8 本章小结 |
| 第4章 基于逆运动学控制的关节角加速度规划研究 |
| 4.1 引言 |
| 4.2 笛卡尔空间到关节角加速度的映射 |
| 4.3 超扭曲算法设计 |
| 4.4 稳定性分析 |
| 4.4.1 矩阵正定条件 |
| 4.4.2 参数取值范围 |
| 4.5 参数对收敛性影响 |
| 4.5.1 参数对收敛速度影响 |
| 4.5.2 参数对运动路径的影响 |
| 4.6 仿真验证 |
| 4.6.1 规划性能仿真 |
| 4.6.2 零空间性能分析 |
| 4.7 本章小结 |
| 第5章 基于自组织竞争神经网络的多臂运动规划研究 |
| 5.1 引言 |
| 5.2 多臂正逆运动学 |
| 5.3 多臂机器人独立任务 |
| 5.3.1 冗余度双臂机器人仿真 |
| 5.3.2 冗余度三臂机器人仿真 |
| 5.4 多臂机器人面向协调任务运动规划 |
| 5.4.1 协调操作运动学 |
| 5.4.2 子基法 |
| 5.4.3 基于自组织竞争神经网络的运动规划 |
| 5.4.4 稳定性分析 |
| 5.5 多臂运动规划理论仿真验证 |
| 5.5.1 双臂机器人广义逆运动学 |
| 5.5.2 双臂机器人运动规划 |
| 5.5.3 三臂机器人广义逆运动学 |
| 5.5.4 三臂机器人运动规划 |
| 5.6 本章小结 |
| 第6章 实验平台搭建与实验验证 |
| 6.1 引言 |
| 6.2 实验平台 |
| 6.2.1 冗余度单臂系统 |
| 6.2.2 冗余度双臂系统 |
| 6.3 平面冗余机械臂在线运动规划实验 |
| 6.3.1 基于虚拟控制器规划实验 |
| 6.3.2 静障碍物约束下规划性能比对实验 |
| 6.3.3 多障碍物约束下平面臂运动规划实验 |
| 6.4 三维在线运动规划实验 |
| 6.4.1 基于单神经元PID规划实验 |
| 6.4.2 基于能量转化策略避障性能比对实验 |
| 6.4.3 静态障碍物约束下运动规划实验 |
| 6.4.4 动态障碍物约束下运动规划实验 |
| 6.5 关节角加速度规划实验 |
| 6.5.1 规划性能比对实验 |
| 6.5.2 动态环境约束下角加速度规划实验 |
| 6.6 多臂运动规划实验 |
| 6.6.1 多臂独立任务实验 |
| 6.6.2 多臂协调操作运动规划实验 |
| 6.7 本章小结 |
| 结论 |
| 参考文献 |
| 攻读学位期间发表的学术论文及其他成果 |
| 致谢 |
| 个人简历 |
（9）Towards 6G wireless communication networks:vision, enabling technologies, and new paradigm shifts（论文提纲范文）
| 1 Introduction |
| 2 Performance metrics,application scenarios,and example industry verticals |
| 2.1 Performance metrics and application scenarios |
| 2.2 Example industry verticals |
| 2.2.1 Cloud VR |
| 2.2.2 IoT industry automation |
| 2.2.3 C-V2X |
| 2.2.4 Digital twin body area network |
| 2.2.5 Energy efficient wireless network control and federated learning systems |
| 3 Enabling technologies |
| 3.1 Air interface and transmission technologies |
| 3.1.1 New waveforms |
| 3.1.2 Multiple access |
| 3.1.3 Channel coding |
| 3.1.4 CF massive MIMO |
| 3.1.5 Dynamic and intelligent spectrum sharing and accessing |
| 3.1.6 Blockchain-based wireless accessing and networking |
| 3.1.7 Photonics defined radio |
| 3.1.8 MCo for uRLLC |
| 3.2 Network architecture |
| 3.2.1 SDN/NFV |
| 3.2.2 Network slicing and its improvement |
| 3.2.3 SBA and its evolution |
| 3.2.4 CSA |
| 3.2.5 DEN2 |
| 3.2.6 CF architecture |
| 3.2.7 Cloud/fog/edge computing |
| 4 New paradigm shifts |
| 4.1 Global coverage:space-air-ground-sea integrated networks |
| 4.1.1 Satellite communication network |
| 4.1.2 UAV communication network |
| 4.1.3 Maritime machine-type communication network |
| 4.1.4 Space-air-ground-sea integrated networks |
| 4.2 All spectra:sub-6 GHz,mmWave,THz,and optical frequency bands |
| 4.2.1 Sub-6 GHz bands |
| 4.2.2 mmWave bands |
| 4.2.3 THz bands |
| 4.2.4 Optical frequency bands |
| 4.2.5 Channel measurements and models for 5G and beyond |
| 4.3 Full applications:AI enabled wireless networks |
| 4.3.1 AI and ML technologies:an overview |
| 4.3.2 Physical layer applications |
| 4.3.3 Upper layer applications |
| 4.3.4 Resource allocation applications |
| 4.3.5 Intelligence endogenous networks (IENs) |
| 4.3.6 Toward ICDT convergence in 6G |
| 4.4 Endogenous network security |
| 4.4.1 Current status and main issues |
| 4.4.2 Network security concerns in 6G |
| 4.4.3 Possible countermeasures to the security and privacy issues in 6G networks |
| 5 Conclusion |
| Appendix A |
（10）面向目标跟踪的DELTA机器人控制方法研究（论文提纲范文）
| 摘要 |
| Abstract |
| List of symbols |
| Chapter 1 Introduction |
| 1.1 The Background |
| 1.2 Introduction of Robots |
| 1.2.1 Two Kinematic Types Robots |
| 1.2.2 Advantages of Parallel Robots Over Serial Robots |
| 1.2.3 Other Industrial Robots` Structures |
| 1.2.4 Object Tracking and Gripping |
| 1.3 Research Significance |
| 1.4 Related Research and Literature Review Analysis |
| 1.4.1 Literature Review |
| 1.4.2 Literature Review Analysis |
| 1.5 Main Content of Research |
| 1.5.1 Research Objectives |
| 1.5.2 Research Contents and Research Plan |
| 1.6 Research Contribution |
| Chapter 2 Methodology and Mathematical Background |
| 2.1 Multibody systems |
| 2.2 Advantages of MBS approaches |
| 2.3 Spatial transformation of rigid bodies |
| 2.3.1 Angle of rotation and rotation axis technique |
| 2.3.2 Rotations about triad orthogonal axes techniques |
| 2.3.3 DH parameters |
| 2.3.4 Comparative analysis between different methodologies |
| 2.3.5 Position of a generalized vector in space |
| 2.3.6 Euler parameters and Euler angles |
| 2.4 Angular velocity in terms of Euler angles |
| 2.5 Spatial kinematic analysis |
| 2.5.1 Degrees of freedom |
| 2.5.2 Generalized coordinates |
| 2.5.3 Kinematic constraints |
| 2.5.4 Spherical and revolute joints modeling |
| 2.6 Summary |
| Chapter 3 Delta Robot Model and Simulation |
| 3.1 System parameters assignment |
| 3.2 Delta robot model simplification |
| 3.3 MBS model parameters of the D3S-800 delta robot |
| 3.3.1 Parameters of revolute joints |
| 3.3.2 Parameters of spherical joints |
| 3.4 FLARM constraints and driving constraints |
| 3.5 Constraints equations and Jacobian matrix |
| 3.6 Exact instantaneous dependent coordinates calculations |
| 3.7 Simplified MBS model analysis and verification |
| 3.8 Error sources in the simplified MBS model |
| 3.9 Summary |
| Chapter 4 Object Detection and Tracking |
| 4.1 Introduction |
| 4.2 Problems associated to object tracking algorithms |
| 4.2.1 Variations of the moving object appearance |
| 4.2.2 Illumination, shadow and occlusion problems |
| 4.2.3 Presence of abrupt motion |
| 4.2.4 Surveillance Camera related problems |
| 4.3 Methods of objects classification |
| 4.4 Methods of moving objects detection |
| 4.4.1 Background modeling and subtraction algorithm |
| 4.4.2 Trajectory path compensation methods |
| 4.4.3 Object tracking methods |
| 4.5 Summary |
| Conclusion |
| 结论 |
| References |
| ACKNOWLEDGEMENT |
| Appendix 1: Matlab subroutine to calculate the Jacobian Cqd |
| Appendix 2: Matlab subroutine to calculate ψ_(arm), φ_(FA) and ψ_(FA) |
四、Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅱ)（论文参考文献）
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