Visual recognition of pointing gestures for human–robot interaction

In this paper, we present an approach for recognizing pointing gestures in the context of human–robot interaction. In order to obtain input features for gesture recognition, we perform visual tracking of head, hands and head orientation. Given the images provided by a calibrated stereo camera, color and disparity information are integrated into a multi-hypothesis tracking framework in order to find the 3D-positions of the respective body parts. Based on the hands’ motion, an HMM-based classifier is trained to detect pointing gestures. We show experimentally that the gesture recognition performance can be improved significantly by using information about head orientation as an additional feature. Our system aims at applications in the field of human–robot interaction, where it is important to do run-on recognition in real-time, to allow for robot egomotion and not to rely on manual initialization.     

下肢康复机器人的研究进展与临床应用

社会老龄化进程中,由于疾病或事故导致下肢受损的患者越来越多,患者的运动能力康复变得尤为重要.相对于传统的物理康复治疗方法,利用下肢康复机器人进行运功功能康复训练可以准确评估、大幅改善康复效果,提高康复效率,节约医疗康复资源.作为一种涉及康复医学、机械学、控制学、机器人学、计算机学及人工智能等诸多学科的智能仿生机电设备,下肢康复机器人按照患者的康复作业姿态主要分为坐卧式下肢康复机器人和站立式下肢康复机器人.其中坐卧式下肢康复机器人可以分为脚踏板式和外骨骼式机器人,站立式下肢康复机器人分为悬挂减重式和独立可穿戴式机器人.本文调研了国际上应用较为广泛的几类下肢康复机器人的代表性产品,详细分析了下肢康复机器人的主被动自由度、驱动方式、传动关节、感知技术、运动控制策略和训练模式等关键技术,并对各类机器人典型临床测试案例进行了比较和探讨,对下肢康复机器人未来的技术发展做出展望.     

自动化学科面临的挑战

本文分析了控制理论与应用、模式识别与智能系统、导航制导与控制、系统科学与工程、人工智能与自动化交叉等领域的发展现状. 结合科技发展、国内国际研究前沿和新兴领域对自动化科学技术的需求, 提出重点发展智能控制理论和方法、高性能作业机器人、信息物理系统、导航与控制技术、重大装备自动化技术、自主智能系统和人工智能驱动的自动化技术优先领域, 加强数据驱动控制理论、人工智能基础理论研究, 进一步发展人机协同、跨域融合的智能自动化, 为实现国家社会的全面信息化智能化提供理论和技术保障.     

面向巡检机器人的电力设备状态检测算法研究

为更高效利用变电站巡检机器人开展电力巡检工作,满足电力行业发展对智能化巡检的需求,研究了面向电力巡检机器人的电力设备状态检测算法.首先,根据深度网络部署硬件芯片应用情况与性能对比,选择海思Hi3559A芯片作为算法移植的嵌入式平台.然后综合考虑各种检测算法的精度与速度,选用YOLOv3算法作为设备状态检测的基本判别模型...     

Soft Robotics: Morphology and Morphology-inspired Motion Strategy

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., cutting-edge 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.      

基于信息物理融合的骨折复位机器人系统构建

针对骨折治疗中传统复位方法（手法复位、牵引复位、手术复位）存在的二次损伤、肌能失调、患肢僵硬、破坏血运及术后矫正动态性能差等问题,提出了一种人-机-物融合的信息物理系统（CPHS）用于指导机器人的复位动作。首先,从CPHS的数字孪生、信息感知、系统集成、手术流程、模拟复位等方面论证了并联机器人信息物理系统的构成,将机器人的高定位精度及可重复性与微创方法有效结合,从而指导医生完成仿真规划及术中监控等一系列操作;其次,根据临床骨折复位过程,在机器人操作下对5组不同骨折姿态的模拟骨折病例进行了复位实验;最后,计算出每组实验复位后的移位残余及角度误差,并与传统复位方法的相应数据进行对比。实验结果表明,CPHS骨折复位机器人相较于传统复位方法,在骨折复位过程和患者术后康复上均有着明显的优势。     

基于AT89C52单片机的搬运机器人控制系统设计

针对传统搬运机器人控制系统搬运路径准确性较差,控制耗时较长的问题,基于AT89C52单片机设计了一种新的搬运机器人控制系统;系统硬件主要由驱动模块、控制模块、显示模块和地面勘测模块组成,驱动模块具有很强的信息驱动能力,能确保机器人在运动过程得到充足的电量支持,显示模块及时进行信息显示,同时配合控制模块实现实时控制,地面勘测模块确保机器人在工作过程中对地面状况进行分析处理,从而得到更优的搬运路径;软件部分设计了搬运机器人最佳的分组搬运策略,基于AT89C52单片机将搬运指令传输至搬运机器人控制算法,快速纠正机器人的错误行为,使机器人成功完成搬运任务;实验结果表明,该搬运机器人控制系统能够有效缩短控制时间,提高控制精度,具有良好的系统规划、优化能力及智能化高.     

仿尺蠖爬壁机器人自适应吸附及摇杆控制

为了解决真空吸附爬壁机器人爬行时,需要吸盘和壁面紧密贴合,不易操控的问题。建立了吸盘足至壁面的空间几何模型;基于D-H参数,建立了运动学模型,根据速度运动学逆解,设计了一种用于对称机构爬壁机器人的摇杆操作方法,将摇杆轴映射为关节速度闭环;根据位置运动学逆解,设计了自适应吸附动作,吸盘足与壁面距离小于设定阈值时,自动触发自适应吸附动作;搭建了机器人控制系统和自适应吸附装置,在水平壁面上进行爬行实验,验证了该方法可减小操控难度。     

Design of Adaptive Robot Control System Using Recurrent Neural Network

The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.     

基于改进A*算法的移动机器人路径规划

为了解决较大场景下A^*寻路算法存在的内存开销大、计算时间长等问题,本文在A^*算法的基础上,结合跳点搜索算法,提出一种改进的A^*算法．该算法通过筛选跳点进行扩展,直到生成最终路径,扩展过程中使用跳点代替A^*算法中大量可能被添加到OpenList和ClosedList的不必要节点,从而减少计算量．为了验证改进A^*算法的有效性,分别在不同尺寸的2维栅格地图中进行仿真,仿真结果表明,相比A^*算法,改进A^*算法在寻路过程中扩展更少的节点,寻路速度更快,且加速效果随环境地图的增大更加明显．最后将改进A^*算法应用于移动机器人Turtlebot2进行对比实验．实验结果表明,在生成相同路径的基础上,改进A^*算法的寻路速度较A^*算法提高了约200%,能够满足移动机器人路径规划的要求．