穿刺手术机器人遥操作系统的控制策略研究

在分析穿刺机器人系统功能需求的基础上,搭建了主从遥操作系统的半实物仿真平台,并给出雅克比矩阵方法和PD控制律的联合控制方法。通过设计数字滤波器,以消除外科医生的手部低频抖动对穿刺手术机器人精度的影响。实验结果表明,从机器人末端执行器在笛卡尔空间坐标下能够精确、快速、安全地跟随主机器人末端执行器的位置变化,并且外科医生的手部抖动能够被有效消除。     

主从同构穿刺手术机器人遥操作控制策略研究

文章在分析了穿刺机器人系统功能需求的基础上,搭建了主从遥操作系统的半实物仿真平台。基于等效微分变换的思想,提出雅克比矩阵(Jacobian Matrix)方法和比例微分(Proportional-Derivative,PD)控制律的联合控制方法。通过设计数字滤波器来消除外科医生的手部低频抖动对穿刺手术机器人精度的影响。实验结果表明从机器人末端执行器在笛卡尔空间坐标下能够精确快速安全地跟随主机器人末端执行器的位置变化,并且主机器人端的手部抖动能够被消除。     

基于工业机器人的汽车轮毂表面缺陷的视觉检测系统设计

摘要：由于汽车轮毂结构复杂,为解决当前轮毂检测效率低,检测速度慢,工作量大等问题,该文给出了一种基于工业机器人的视觉检测方法。通过把视觉系统安装在机器人的末端执行器上,利用机器人的精确运动来改变视觉系统的姿态,以实现对汽车轮毂各表面的检测,进而可实现对多规格轮毂进行动态检测,并针对多次重复测量时定位累计误差导致成像质量退化的问题,通过对轮毂图像的预处理找到了轮毂的气阀作为检测的初始位置,对机器人调节相应的位姿进行校正。该文以HONDA 1.6X17摩托车轮毂作为研究对象,实验表明,该系统在准确检测的同时,提高了检测效率。     

基于wMPS的工业机器人定位精度自动补偿方法

针对智能制造场景下工业机器人高精度定位控制需求,引入具有大空间多点并行测量能力的新型工作空间测量定位系统(workspace Measuring and Positioning System:wMPS)及机器人操作系统(ROS:Robot Operating System),实现工业机器人末端执行器位置的自主感知及自动补偿。通过设计具有周向感知能力定位的工业机器人末端定位补偿传感器,解决了复杂操作现场定位遮挡问题;针对wMPS与机器人自身坐标系标定的问题,设计基于坐标点集匹配的坐标转换标定方法,实现了坐标系转换参数精确解算,坐标系转换算精度在0.25 mm;针对工业机器人在大范围移动后定位精度降低的问题,设计了基于快速扩展随机树(Rapidly-exploring Random Trees, RRT)的控制补偿算法,通过wMPS测量反馈提供控制补偿坐标,在末端实现闭环小范围移动,以ABB机器人为研究对象,实现了wMPS与ROS系统集成,并实验验证了补偿算法的精确性,机器人定位精度提升90.14%。     

基于目标检测的机器人手眼标定方法

智能协作机器人依赖视觉系统感知未知环境中的动态工作空间定位目标,实现机械臂对目标对象的自主抓取回收作业。RGB-D相机可采集场景中的彩色图和深度图,获取视野内任意目标三维点云,辅助智能协作机器人感知周围环境。为获取抓取机器人与RGB-D相机坐标系之间的转换关系,提出基于yolov3目标检测神经网络的机器人手眼标定方法。将3D打印球作为标靶球夹持在机械手末端,使用改进的yolov3目标检测神经网络实时定位标定球的球心,计算机械手末端中心在相机坐标系下的3D位置,同时运用奇异值分解方法求解机器人与相机坐标系转换矩阵的最小二乘解。在6自由度UR5机械臂和Intel RealSense D415深度相机上的实验结果表明,该标定方法无需辅助设备,转换后的空间点位置误差在2 mm以内,能较好满足一般视觉伺服智能机器人的抓取作业要求。     

协作环境下的人机距离建模与最小距离迭代算法

为了使机器人适应更广泛、更复杂的任务需求,实现人与机器人的协作与共融,精确并实时地计算人与机器人之间的相对距离成为了不可避免的问题.针对该问题,提出了一种协作环境下的人机距离建模方法以及计算人机间最小距离的迭代算法.首先,利用机器人的3D模型构建机器人结构特征,并通过3D视觉传感器提取人体骨骼特征,将以上2组特征映射到同一坐标空间中建立协作环境下的人机距离模型.然后,在此模型的基础上迭代计算人与机器人间的最小距离并给出对应的空间位置点.最后以ABB公司的YuMi机器人为测试对象进行人机最小距离测量实验,实验结果表明该方法降低了建模难度、实现了计算的实时性,验证了该建模方法与迭代算法的有效性和实用性.     

智能协作机器人制餐服务系统研制

针对当前传统早餐制作、售卖需要值守服务人员,人力成本高且容易产生污染等问题,本文研制一套自动制备早餐的智能协作机器人制餐服务系统.该系统采用协作机器人作为执行机构,机器视觉系统作为识别定位检测装置,PLC作为主控制系统,通过工业网络进行软硬件通信.经验证,该系统具有较好的操作性和稳定性,用户可通过点餐应用软件在线下单,...     

协作机器人的力/位混合控制系统设计

协作机器人完成装配、焊接等精度要求较高的工作时,其末端执行器必须能够同时控制末端的位置以及接触力,避免接触力对机器人和环境造成损坏。主要基于xMate3柔顺协作机器人（7轴机械臂）,在机械臂的末端执行器建立适当的坐标系,然后将控制问题分解成两个独立的解耦子问题——力控制问题和位置控制问题,从而实现力位混合控制。     

视觉引导技术在SCARA机器人装配任务中的应用

基于实际生产项目,提出了一种针对平面关节型机器人(SCARA)平面装配任务的手眼标定方法。对线性标定法的输出通过多次迭代使得累积误差和逐渐减小,相较于使用线性标定法来进行手眼标定,视觉定位的平均定位误差降低了0. 4 mm,最大定位误差降低了0. 6 mm。在使用SCARA机器人进行毫米(mm)级作业时,使用迭代线性标定法比传统的线性标定法有更高的视觉定位精度。同时,末端执行器完成对目标工件的抓取任务之后,使用解线性方程的思想求解SCARA机器人完成装配任务的末端位姿,通过实际工程项目的验证,装配成功率可达98. 6%。     

基于GPR和深度强化学习的分层人机协作控制

提出了一种基于高斯过程回归与深度强化学习的分层人机协作控制方法,并以人机协作控制球杆系统为例检验该方法的高效性.主要贡献是:1)在模型未知的情况下,采用深度强化学习算法设计了一种有效的非线性次优控制策略,并将其作为顶层期望控制策略以引导分层人机协作控制过程,解决了传统控制方法无法直接应用于模型未知人机协作场景的问题; 2)针对分层人机协作过程中人未知和随机控制策略带来的不利影响,采用高斯过程回归拟合人体控制策略以建立机器人对人控制行为的认知模型,在减弱该不利影响的同时提升机器人在协作过程中的主动性,从而进一步提升协作效率; 3)利用所得认知模型和期望控制策略设计机器人末端速度的控制律,并通过实验对比验证了所提方法的有效性.     

Autonomy and Common Ground in Human-Robot Interaction: A Field Study

The use of robots, especially autonomous mobile robots, to support work is expected to increase over the next few decades. However, little empirical research examines how users form mental models of robots, how they collaborate with them, and what factors contribute to the success or failure of human-robot collaboration. A two-year observational study of a collaborative human-robot system suggests that the factors disrupting the creation of common ground for interactive communication change at different levels of robot autonomy. Our observations of users collaborating with the remote robot showed differences in how the users reached common ground with the robot in terms of an accurate, shared understanding of the robot's context, planning, and actions - a process called grounding. We focus on how the types and levels of robot autonomy affect grounding. We also examine the challenges a highly autonomous system presents to people's ability to maintain a shared mental model of the robot     

Collision-free human-robot collaboration based on context awareness

Recent advancements in human-robot collaboration have enabled human operators and robots to work together in a shared manufacturing environment. However, current distance-based collision-free human-robot collaboration system can only ensure human safety but not assembly efficiency. In this paper, the authors present a context awareness-based collision-free human-robot collaboration system that can provide human safety and assembly efficiency at the same time. The system can plan robotic paths that avoid colliding with human operators while still reach target positions in time. Human operators’ poses can also be recognised with low computational expenses to further improve assembly efficiency. To support the context-aware collision-free system, a complete collision sensing module with sensor calibration algorithms is proposed and implemented. An efficient transfer learning-based human pose recognition algorithm is also adapted and tested. Two experiments are designed to test the performance of the proposed human pose recognition algorithm and the overall system. The results indicate an efficiency improvement of the overall system.     

Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function

Aiming at human-robot collaboration in manufacturing, the operator's safety is the primary issue during the manufacturing operations. This paper presents a deep reinforcement learning approach to realize the real-time collision-free motion planning of an industrial robot for human-robot collaboration. Firstly, the safe human-robot collaboration manufacturing problem is formulated into a Markov decision process, and the mathematical expression of the reward function design problem is given. The goal is that the robot can autonomously learn a policy to reduce the accumulated risk and assure the task completion time during human-robot collaboration. To transform our optimization object into a reward function to guide the robot to learn the expected behaviour, a reward function optimizing approach based on the deterministic policy gradient is proposed to learn a parameterized intrinsic reward function. The reward function for the agent to learn the policy is the sum of the intrinsic reward function and the extrinsic reward function. Then, a deep reinforcement learning algorithm intrinsic reward-deep deterministic policy gradient (IRDDPG), which is the combination of the DDPG algorithm and the reward function optimizing approach, is proposed to learn the expected collision avoidance policy. Finally, the proposed algorithm is tested in a simulation environment, and the results show that the industrial robot can learn the expected policy to achieve the safety assurance for industrial human-robot collaboration without missing the original target. Moreover, the reward function optimizing approach can help make up for the designed reward function and improve policy performance.     

A virtual environment-based system for the navigation of underwater robots

Efficient teleoperation of underwater robot requires clear 3D visual information of the robot's spatial location and its surrounding environment. However, the performance of existing telepresence systems is far from satisfactory. In this paper, we present our virtual telepresence system for assisting tele-operation of an underwater robot. This virtual environment-based telepresence system transforms robot sensor data into 3D synthetic visual information of the workplace based on its geometrical model. It provides the operators with a full perception of the robot's spatial location. In addition, we propose a robot safety domain to overcome the robot's location offset in the virtual environment caused by its sensor errors. The software design of the system and how a safety domain can be used to overcome robot location offset in virtual environment will be examined. Experimental tests and its result analysis will also be presented in this paper.     

康复机器人具有速度决策的每步限时学习控制方法

为了提高康复步行训练器人的智能性和安全性,提出一种运动速度决策的康复训练机器人限时学习迭代控制方法,目的是抑制训练者位姿不确定性和人机速度不协调对系统安全性能的影响.建立具有系统不确定偏移量的康复步行训练机器人动力学模型,通过比较康复训练机器人当前的运动速度和训练者的实际步行速度,提出机器人运动速度的决策方法,从而使康复者在主动训练模式下实现人机速度协调运动;进一步,利用机器人决策的运动速度和动力学模型建立跟踪误差系统,提出有限学习时间的迭代控制方法,并基于Lyapunov理论验证跟踪误差系统的有限时间稳定性.仿真对比分析和实验结果表明,所提出的速度决策方法和跟踪控制方法能使人机系统协调地进行主动模式的康复训练.     

协作机器人及其运动规划方法研究综述

协作机器人是一种新型工业机器人,利用物联网感知等新兴技术进行智能人机交互,实现与人类近距离协同工作以提高生产效率。因此,协作机器人近年来备受关注,成为了机器人领域最热门的研究方向之一。介绍了协作机器人的基本情况,包括主要产品、机器人本体设计以及应用案例;介绍了常见的人机协作方法,重点围绕人机协作中的高效、简单、安全三个特点,分别对协作机器人编程、安全协作方法、高效协作方法三个方面的研究成果展开讨论;总结了常见的机器人运动规划方法,分成路径规划方法和轨迹规划方法两部分介绍,并分析了各类方法的优缺点;最后,对目前协作机器人研究的发展方向进行了展望。     

Towards seamless collaboration of humans and high-payload robots: An automotive case study

Despite the ergonomic challenges that large-sized product assemblies underlay for human operators, related processes are deselected from hybrid automation mostly due to the payload limitations of collaborative robots. Driven by industrial requirements for ergonomic and performance improvement, this paper presents the implementation of a human-high payload robot symbiotic workstation and discusses the key enabling technologies for seamless collaboration, namely: a) a multi-modal human-robot interaction pipeline, b) a gesture based contactless manual guidance module, c) fenceless safety monitoring system and logic, and d) an augmented reality-based training application for operator inclusion. An automotive case study is used for validating the complete hybrid system's usability and performance besides improved operator ergonomics and well-being. This work's findings prove that high-payload robots can support operators through intuitive means of interaction and businesses can rely on collaborative systems for improved performance metrics and job openness.     

A Human-Robot Collaboration Framework for Improving Ergonomics During Dexterous Operation of Power Tools

In this work, we present a novel control approach to human-robot collaboration that takes into account ergonomic aspects of the human co-worker during power tool operations. The method is primarily based on estimating and reducing the overloading torques in the human joints that are induced by the manipulated external load. The human overloading joint torques are estimated and monitored using a whole-body dynamic state model. The appropriate robot motion that brings the human into the suitable ergonomic working configuration is obtained by an optimisation method that minimises the overloading joint torques. The proposed optimisation process includes several constraints, such as the human arm muscular manipulability and safety of the collaborative task, to achieve a task-relevant optimised configuration. We validated the proposed method by a user study that involved a human-robot collaboration task, where the subjects operated a polishing machine on a part that was brought to them by the collaborative robot. A statistical analysis of ten subjects as an experimental evaluation of the proposed control framework is provided to demonstrate the potential of the proposed control framework in enabling ergonomic and task-optimised human-robot collaboration.     

Behavioral overlays for non-verbal communication expression on a humanoid robot

This research details the application of non-verbal communication display behaviors to an autonomous humanoid robot, including the use of proxemics, which to date has been seldom explored in the field of human-robot interaction. In order to allow the robot to communicate information non-verbally while simultaneously fulfilling its existing instrumental behavior, a “behavioral overlay” model that encodes this data onto the robot's pre-existing motor expression is developed and presented. The state of the robot's system of internal emotions and motivational drives is used as the principal data source for non-verbal expression, but in order for the robot to display this information in a natural and nuanced fashion, an additional para-emotional framework has been developed to support the individuality of the robot's interpersonal relationships with humans and of the robot itself. An implementation on the Sony QRIO is described which overlays QRIO's existing EGO architecture and situated schema-based behaviors with a mechanism for communicating this framework through modalities that encompass posture, gesture and the management of interpersonal distance.