Design of Self‑Reconfigurable Multiarm Robot Mechanism Based on Deployable Kinematic Chains

As the structures of multiarm robots are serially arranged,the packaging and transportation of these robots are often inconvenient.The ability of these robots to operate objects must also be improved.Addressing this issue,this paper presents a type of multiarm robot that can be adequately folded into a designed area.The robot can achieve different operation modes by combining different arms and objects.First,deployable kinematic chains(DKCs)are designed,which can be folded into a designated area and be used as an arm structure in the multiarm robot mechanism.The strategy of a platform for storing DKCs is proposed.Based on the restrictions in the storage area and the characteristics of parallel mechanisms,a class of DKCs,called base assembly library,is obtained.Subsequently,an assembly method for the synthesis of the multiarm robot mechanism is proposed,which can be formed by the connection of a multiarm robot mechanism with an operation object based on a parallel mechanism structure.The formed parallel mechanism can achieve a reconfigurable characteristic when different DKCs connect to the operation object.Using this method,two types of multiarm robot mechanisms with four DKCs that can switch operation modes to perform different tasks through autonomous combination and release operation is proposed.The obtained mechanisms have observable advantages when compared with the traditional mechanisms,including optimizing the occupied volume during transportation and using parallel mechanism theory to analyze the switching of operation modes.

Structural Synthesis and Workspaces of Serial Robotic Chains with 7 Axes

Redundant serial robot kinematic chains with seven axes are an extension of classical 6-axis ones. The structural synthesis of these structures is useful to provide a working basis, including for the promotion of new structures with 7 axes, besides some already existing and applied structures. This paper summarizes kinematic chain structures using a combinatorial method by listing all possible variants of the structures with 7 axes, obtained by adding a rotational or translational coupling, in a parallel or perpendicular position, against the guiding structure with 6 axes consisting of two distinct modules： positioning module （3 axes） and orientation module （3 axes）. Representation of proper workspaces can help the designer in choosing the structure with maximum functionality for a given application.

Adaptive proportional integral differential control based on radial basis function neural network identification of a two-degree-of-freedom closed-chain robot

A closed-chain robot has several advantages over an open-chain robot, such as high mechanical rigidity, high payload, high precision. Accurate trajectory control of a robot is essential in practical-use. This paper presents an adaptive proportional integral differential （PID） control algorithm based on radial basis function （RBF） neural network for trajectory tracking of a two-degree-of-freedom （2-DOF） closed-chain robot. In this scheme, an RBF neural network is used to approximate the unknown nonlinear dynamics of the robot, at the same time, the PID parameters can be adjusted online and the high precision can be obtained. Simulation results show that the control algorithm accurately tracks a 2-DOF closed-chain robot trajectories. The results also indicate that the system robustness and tracking performance are superior to the classic PID method.

基于RobotStudio的工业机器人输送链跟踪仿真

为提高工业机器人在智能化生产线设计中的效率和可行性分析,提出以ABB机器人仿真软件RobotStudio在输送链跟踪中的实际应用;利用SolidWorks/Pro-E等三维建模软件,对机器人末端吸盘及外围设备进行三维实体建模,转换为Step格式文件导入RobotStudio软件中,完成整个工作站的空间布局;对机器人运动学进行理论分析,添加输送链Smart组件并进行属性设置,对输送链各个窗口进行参数标定;通过工作站逻辑,建立起各个Smart组件和机器人之间的I/O通讯。仿真结果得出:机器人输送链跟踪程序可以同步至实际生产中,产品输送链速度和机器人拾取率有密切关系,在输送链跟踪对机器人的设置方法,也可以对其它用途的工业机器人仿真作为参考,在缩短生产线研发周期和提高设计效率等方面,具有良好的作用。

工业机器人应用与供应链效率提升——基于供应链治理与营运效率的双重视角

文章基于2011—2019年中国A股上市企业财务数据,研究工业机器人应用对企业供应链效率的影响。研究发现:工业机器人应用显著提高了企业供应链效率,该结果在一系列稳健性检验中得到验证;机制分析表明,工业机器人应用通过供应链治理渠道和营运效率渠道来提高供应链效率,在中西部地区、传统行业、竞争性行业、高市场优势地位、高资产专用性和高职工薪酬企业中作用更明显;进一步研究表明,工业机器人应用存在正向的供应链溢出效应,并可以实现兼顾供应链安全的效率提升。本研究为进一步优化工业机器人应用的相关政策制定和构建高质量供应链体系提供了政策启示。

Biped Robot with Triangle Configuration

A new biped robot with a triangle configuration is presented and it is a planar closed chain mechanism. The scalability of three sides of the triangle is realized by three actuated prismatic joints. The three vertexes of the triangle are centers of three passive revolute joints coincidently. The biped mechanism for straight walking is proposed and its walking principle and mobility are explained. The static stability and the height and span of one step are analyzed. Kinematic analysis is performed to plan the gaits of walking on an even floor and going upstairs. A prototype is developed and experiments are carried out to validate the straight walking gait. Two additional revolute joints are added to form a modified biped robot which can follow the instruction of turning around. The turning ability is verified by experiments. As a new member of biped robots, its triangle configuration is used to impart geometry knowledge. Because of its high stiffness, some potential applications are on the way.

基于RobotStudio的Delta机器人双输送链跟踪分拣仿真研究

为解决巧克力分拣包装生产线上人工分拣易造成巧克力破碎、污染等问题,提出工业机器人分拣解决方案,采用两条输送链跟踪分拣巧克力并统一装盒。研究了并联机器人的输送链跟踪技术,讨论了在虚拟仿真环境中输送链和机器人运行速度之间的关系。结果表明,通过将输送链与机器人的速度设定为一定比值,机器人可以达到100%的分拣率,同时保证生产节拍和工作效率,对布局企业分拣生产线具有现实指导意见。

Emotional Gait Generation for a Humanoid Robot

In this paper, an emotional mathematical model and affective state probability description space of a humanoid robot are set up on the basis of psycho-dynamics＇ psychological energy and affective energy conservation law. The emotional state transferring process and hidden Markov chain algorithm of stimulating transition process are then studied. The simulation results show that the mathematical model is applicable to the authentic affective state change rule of human beings. Finally, the gait generation experiment results of control signal and electric current tracking wave-form are presented to demonstrate the validity of the proposed mathematical model.

Robust Stabilization of Nonholonomic Chained Form Systems with Uncertainties

有关为 nonholonomic 的柔韧的稳定问题的最近的开发的一篇摘要与无常锁住形式系统被提供。第一，在为不明确的锁住的形式系统的过去的十年的各种各样的模型，主要途径，和结果被介绍。然后，特殊兴趣的几个新令人激动的不明确的锁住的形式模型为 nonholonomic wheeled 被建议活动机器人。他们被基于视觉 servoing 反馈使用状态和输入转变获得。最后，新奇柔韧的规定控制器被使用二拍子的圆舞技术为一些新不明确的锁住的模型探讨，视觉反馈，州可伸缩、切换的策略。这调查将为不明确的锁住的形式系统关于柔韧的稳定的开发提供好介绍，这被期望。

基于RobotStudio的多工业机器人制链生产线的仿真设计

以自动化生产线中多机器人协调运作及工序流程智能化控制为研究对象,介绍一种利用RobotStudio多机协调运作的虚拟生产方案。采用四台机器人与多台设备搭建的制链生产线,其中三台机器人共用一个控制系统,另一台机器人使用一个控制系统,各设备通过Smart形成相对独立的组件,再进行机器人系统与组件之间I/O端口的连接,探索优化生产工序流程,形成自适应的智能生产线。该方案可以为自动化生产线设计、调试、改进提供理论依据,指导现场生产。

The Fuzzy Neural Network Control Scheme With H∞ Tracking Characteristic of Space Robot System With Dual-arm After Capturing a Spin Spacecraft

In this paper,the dynamic evolution for a dualarm space robot capturing a spacecraft is studied,the impact effect and the coordinated stabilization control problem for postimpact closed chain system are discussed.At first,the pre-impact dynamic equations of open chain dual-arm space robot are established by Lagrangian approach,and the dynamic equations of a spacecraft are obtained by Newton-Euler method.Based on the results,with the process of integral and simplify,the response of the dual-arm space robot impacted by the spacecraft is analyzed by momentum conservation law and force transfer law.The closed chain system is formed in the post-impact phase.Closed chain constraint equations are obtained by the constraints of closed-loop geometry and kinematics.With the closed chain constraint equations,the composite system dynamic equations are derived.Secondly,the recurrent fuzzy neural network control scheme is designed for calm motion of unstable closed chain system with uncertain system parameter.In order to overcome the effects of uncertain system inertial parameters,the recurrent fuzzy neural network is used to approximate the unknown part,the control method with H∞tracking characteristic.According to the Lyapunov theory,the global stability is demonstrated.Meanwhile,the weighted minimum-norm theory is introduced to distribute torques guarantee that cooperative operation between manipulators.At last,numerical examples simulate the response of the collision,and the efficiency of the control scheme is verified by the simulation results.

Optimum Design of Stair-Climbing Robots Using Taguchi Method

Environmental issues like pollution are major threats to human health.Many systems are developed to reduce pollution.In this paper,an optimal mobile robot design to reduce pollution in Green supply chain management system.Green supply chain management involves as similating environmentally and eco-nomically feasible solutions into the supply chain life-cycle.Smartness,advanced technologies,and advanced networks are becoming pillars of a sustainable supply chain management system.At the same time,there is much change happening in the logistics industry.They are moving towards a new logistics model.In the new model,robotic logistics has a vital role.The reasons for this change are the rapid growth of the e-commerce business and the shortage of workers.The advantages of using robotic logistics are reduction in human errors,faster delivery speed,better customer satisfaction,more safety for workers,and high workforce adaptability.A robot with rocker-bogie suspension is a six-wheeled mobile platform that has a distinctive potential to keep all wheels on the ground continuously.It has been designed to traverse rough and uneven terrain by distributing the load over its wheels equally.However,there is a limitation to achieving high-speed mobility against vertical barriers.In this research,an optimal design of product delivery wheeled robots for a sustainable supply chain system is proposed to ensure higher adaptability and maximum stability during climbing staircases.The design parameters of the proposed robot are optimized using Taguchi Method.The aim is to get a smooth trajectory of the robot’s center-of-mass.The proposed approach realizes a robot with much-improved stability which can climb over heights more than the size of the wheel(i.e.,3 times the radius of wheels).The results reveal that the modified rocker-bogie system not only increases the stair-climbing capability but also thwarts instability due to overturning of a wheel of the robot.

人工智能与产业链韧性提升

提升产业链韧性是推动高质量发展、维护国家安全的关键内容。基于亚洲开发银行编制的多区域投入产出表(ADB-MRIO)测算产业链韧性水平指标,并结合国际机器人联合会(IFR)提供的工业机器人数据,深入探究人工智能对产业链韧性的影响效应和作用机制。研究发现:(1)总体而言,人工智能会显著提高产业链的韧性水平,这一结果在处理内生性问题、更换变量、样本范围和估计方法等稳健性检验下,依然显著成立。(2)机制分析表明,技术创新效应和治理体系优化效应是人工智能助力产业链韧性提升的关键内在机制。(3)异质性分析表明,在国家层面,人工智能对发达国家和数字基础设施普及程度更高国家的产业链韧性的提升作用更明显;在行业层面,相比低技术制造业,人工智能对中高技术类制造业的产业链韧性提升作用更为突出。(4)考虑2008年金融危机的影响时发现,金融危机会在一定程度上削弱人工智能对产业链韧性的提升效应。据此提出应培育壮大人工智能行业、推进产业链和创新链融合发展以及加快建设数字化政府。

A Novel Analytical Model of Brain Tumor Based on Swarm Robotics

A tumor is referred to as“intracranial hard neoplasm”if it grows near the brain or central spinal vessel(neoplasm).In certain cases,it is possible that the responsible cells are neurons situated deep inside the brain’s structure.This article discusses a strategy for halting the progression of brain tumor.A precise and accurate analytical model of brain tumors is the foundation of this strategy.It is based on an algorithm known as kill chain interior point(KCIP),which is the result of a merger of kill chain and interior point algorithms,as well as a precise and accurate analytical model of brain tumors.The inability to obtain a clear picture of tumor cell activity is the biggest challenge in this endeavor.Based on the motion of swarm robots,which are considered a subset of artificial intelligence,this article proposes a new notion of this kind of behavior,which may be used in various situations.The KCIP algorithm that follows is used in the analytical model to limit the development of certain cell types.According to the findings,it seems that different KCIP speed ratios are beneficial in preventing the development of brain tumors.It is hoped that this study will help researchers better understand the behavior of brain tumors,so as to develop a new drug that is effective in eliminating the tumor cells.

人工智能与中国制造业全球价值链嵌入——基于工业机器人应用的证据

选取国际机器人联合会数据,实证检验人工智能对中国制造业全球价值链嵌入的影响。研究发现:人工智能显著提升了中国制造业全球价值链嵌入程度,这一作用在“一带一路”沿线省份中尤为显著。传导机制分析发现,人工智能通过技术进步、产业融合间接影响中国制造业全球价值链嵌入。门槛检验显示,人工智能对中国制造业全球价值链嵌入的影响存在产业结构高级化水平和人力资本质量的门槛效应,在跨越门槛值后,人工智能的赋能效应显著增强。

机器人应用对产业链创新链融合的影响研究

本文基于竞争成功函数和共容利益假设构建产业链创新链互动理论模型,从资本积累和就业极化两方面分析机器人应用对产业链创新链融合的影响,并使用中国2006—2021年的省级面板数据,构建计量模型进行实证研究。研究结果显示:机器人应用能够促进产业链创新链融合,主要作用机制包括资本积累效应和就业极化效应;进一步地,在积极应用机器人、就业政策完善的欠发达地区和创新基础薄弱的行业中,机器人应用对产业链创新链融合的促进作用更大,且以不同规模企业为主体的产业链创新链融合均受到机器人应用的促进。本文主要揭示了机器人应用对产业链创新链融合的作用机制和异质性影响,可为优化产业创新发展环境提供经验支撑与政策依据。

混联支路并联腿运动学分析与尺度综合

针对步行机器人在核环境下作业时,动力源分散,不便于防护的问题,提出一种动力源集中布置,可用于构建步行机器人的四自由度混联支路并联腿部机构。运用螺旋理论分析其自由度数目、性质及机构特性。建立了运动学反解模型,并分别推导出驱动空间和关节空间的速度雅克比矩阵。以两个雅克比矩阵条件数为基础,提出了一种带权重的混合条件数,用于综合评价机构的力传递与运动传递性能。以混合条件数最小为尺度综合目标,先通过单调性分析得到结构参数的取值区间,再应用粒子群优化算法获得一组最优结构参数,为样机加工和其他混联支路并联机构尺度综合提供参考。该新型腿部机构动力源统一后置,便于集中防护,驱动空间具有驱动置换功能和半解耦特性,便于实现位姿控制,在核辐射、高温等恶劣环境下有着良好的应用前景。

基于区块链的嵌入式机器人运行数据防篡改传输控制系统设计

在嵌入式机器人中,包含大量重要的运行数据,这些数据需要连接到网络上进行通信和传输,使其容易受到多种网络攻击;如果这些数据被篡改则会导致机器人无法正确执行任务,造成安全隐患;因此,为了确保数据的完整性和安全性,设计基于区块链的嵌入式机器人运行数据防篡改传输控制系统;在嵌入式主控板电路的配合下,协调机器人运行数据解算模块、传输数据矫正模块、控制系统微处理器之间的实时连接关系,完成传输控制系统硬件部分设计;设计控制系统环境和角色对象,并根据不同角色身份,确定运行数据在区块链RBAC组织中的访问能力,基于区块链中的哈希值实现对运行数据防篡改链码的恢复处理,完成基于区块链的嵌入式机器人运行数据防篡改方案的设计;提取运行数据中的关键嵌入部分,建立防篡改要求下的机器人运行数据聚集索引,计算篡改率数值,并实施针对性控制,完成基于区块链的嵌入式机器人运行数据防篡改传输控制系统设计;实验结果表明,经过上述系统的防篡改控制作用后,数据传输效率明显提高,非关联信息难以入侵嵌入式机器人运行系统,也无法篡改关键传输数据,有助于保障机器人运行数据的安全性。

基于RBF神经网络的闭链下肢康复机器人自适应补偿控制

在下肢康复机器人的康复训练过程中,模型参数、环境干扰等不确定性因素会影响机器人轨迹跟踪的精度。针对这一问题,提出了一种基于径向基函数(Radial Basis Function,RBF)神经网络的自适应补偿控制,该控制方法能够提高机械系统轨迹跟踪的精确性。首先,设计一款具有4种工作模式、运动稳定的闭链卧式下肢康复机器人结构;然后,利用拉格朗日方法求解动力学名义模型,将康复装置的模型参数以及外界干扰等不确定性因素分离出来,并设计基于RBF神经网络的自适应补偿算法对其进行逼近控制;最后,通过Matlab/Simulink环境对其进行仿真验证,证明了该控制策略的有效性。结果显示,在人体步态曲线轨迹跟踪中,提出的基于RBF神经网络的自适应补偿算法相比传统的模糊比例-积分-微分(Proportional Integral Derivative,PID)控制的方法响应速度快、跟踪效果好,且髋关节和膝关节轨迹跟踪的角度误差峰值分别为0.08°和0.13°,远小于患者下肢在康复运动中的转动角度。设计了单腿样机试验,试验结果表明,采用的RBF补偿自适应控制器能够实现高精度的跟踪结果,也能够满足患者在康复训练中安全性的要求。

基于卷积神经网络的肌电信号人体运动模式识别技术

随着外骨骼机器人等肌电控制设备的快速发展,表面肌电信号此类非平稳、非周期信号在高性能运动识别系统中的应用已成为相关研究领域的重点。为实现肌电信号跨域特征融合,提出一种基于肌电信号的双卷积链神经网络模型,采集7块关键肌肉的原始肌电信号,经特征提取,转化为能量核相图和离散小波变换系数特征图,分别输入双卷积链神经网络的卷积神经网络分支和MobileNetV2分支,利用融合模块提取高层隐藏特征并进行充分交互。制备包括以上两种特征图像和传统肌电信号图谱在内的3种数据集。3组交叉实验结果表明:所提方法对6种自测下肢运动的平均识别准确率达94.19%,显著优于其他特征组合与网络架构;在ENABL3S开源数据集识别7种下肢运动中取得98.32%的稳态识别准确率,进一步验证了所提方法优良的肌电特征捕捉能力和模式识别准确性。