Transition Motion from Ladder Climbing to Brachiation with Optimal Load-Allocation Control

Abstract

This paper describes the transition motion from ladder climbing to brachiation for a multi-locomotion robot (MLR). The MLR has versatile modes of locomotion, such as biped walking, quadruped walking, brachiation and ladder climbing. The transition is a challenging motion, because the environmental boundaries change and the robot has to switch the form of its locomotion depending on its surroundings, situations and purposes. The robot supports itself with three end-effectors that maintain its stability, while one hand transfers from a rung on the vertical ladder to a new rung behind the robot for brachiation. A closed kinematic chain is formed by the robot links and the ladder. In this case, if the number of position-controlled active joints is greater than the number of the chain’s degrees of freedom, an internal stress appears because of unavoidable position errors. The huge internal stress may lead some motors to become overloaded. Since the safety of each motor is very important for a serial-link robot, a load-allocation algorithm is proposed to balance the loads of the joint motors. The algorithm is verified through experiments.     

On rotations and translations with application to robot manipulators

Robot motion is described by a set of positions and orientations of the robot's end-effector relative to some base coordinate system. Different methods to describe this motion are available and most of them are presented in this paper. Methods for position vector, free vector, and line vector transformation are presented and compared, and the advantages and disadvantages, the simplicity, and the number of arithmetic operations required of the different methods are addressed. An example of the application of these methods to a six degree-of-freedom robot is given.     

Iterative Learning Control Algorithm with a Fixed Step

Iterative Learning Control (ILC) captures interests of many scholars because of its capability of high precision control implement without identifying plant mathematical models, and it is widely applied in control engineering. Presently, most ILC algorithms still follow the original ideas of ARIMOTO, in which the iterative-learning-rate is composed by the control error with its derivative and integral values. This kind of algorithms will result in inevitable problems such as huge computation, big storage capacity for algorithm data, and also weak robust. In order to resolve these problems, an improved iterative learning control algorithm with fixed step is proposed here which breaks the primary thought of ARIMOTO. In this algorithm, the control step is set only according to the value of the control error, which could enormously reduce the computation and storage size demanded, also improve the robust of the algorithm by not using the differential coefficient of the iterative learning error. In this paper, the convergence conditions of this proposed fixed step iterative learning algorithm is theoretically analyzed and testified. Then the algorithm is tested through simulation researches on a time-variant object with randomly set disturbance through calculation of step threshold value, algorithm robustness testing,and evaluation of the relation between convergence speed and step size. Finally the algorithm is validated on a valve-serving-cylinder system of a joint robot with time-variant parameters. Experiment results demonstrate the stability of the algorithm and also the relationship between step value and convergence rate. Both simulation and experiment testify the feasibility and validity of the new algorithm proposed here. And it is worth to noticing that this algorithm is simple but with strong robust after improvements, which provides new ideas to the research of iterative learning control algorithms.     

基于深度学习的采摘机器人目标识别定位算法

为了提升采摘机器人对果实的识别准确率以及定位定精度,提出一种基于深度学习Faster-RCNN框架的采摘机器人目标识别和定位算法.首先采用卷积神经网络VGG16模型提取输入图像的特性信息,并利用区域提议网络RPN生成含有目标的候选框,通过引入自适应候选框数的方法有效提升了算法性能,然后利用多任务损失函数对目标进行分类识...     

悬挂式接管焊接机器人机电系统

针对接管相贯线处的焊接特点,研发了悬挂式接管焊接机器人,介绍了机械结构特点和适用性.控制系统采用工业PC作为主控计算机和PMAC多轴控制卡进行多轴控制,利用二级控制结构实现作业管理,协调各轴运动完成焊接作业.进行了在筒体上外接管和内接管的焊接试验与在椭球体上外接管与内接管的焊接试验.结果表明,机器人的机械本体结构和控制系统能够实现多种接管的焊接需求,能够很好地完成接管相贯线处的焊接,提高了工作效率和焊接质量.     

3G智能机器人的研究

随着3G通讯技术、物联网技术、机电控制技术、多媒体技术的日趋成熟,基于相关技术的产品也备受消费者的欢迎.本文结合当前机器人技术和嵌入式技术的融合发展方向,提出了一种基于ARM9与3G网络相结合的智能机器人,给出了系统结构设计,硬件模块设计,软件设计等.     

基于压电陶瓷驱动的并联微动机器人应用现状

介绍了具有刚度大、承载能力强、位置误差不积累等特点的并联微动机器人,在应用上与串联机器人呈互补关系,已经成为微动机器人领域的研究热点。随着科学技术的发展,许多领域越来越迫切地需要微型系统或微动系统。目前,并联微动机器人已经在航天、航空、制造业、计算机辅助医疗设备、生物工程以及微机电系统等方面有着广泛而重要的应用。     

传感器网络环境下人机协同系统研究

针对人与服务机器人协同远程健康监控系统,分析了系统总体结构设计、传感器网络、微型无线生物信号处理模块等关键设备的设计与实现等方面典型问题.实验室应用证明,所设计硬件、软件及算法的有效性.     

多传感器信息融合技术在移动机器人障碍探测中的应用

在移动机器人障碍探测中,需要实时准确的感知环境信息,而单一传感器仅能提供部分环境信息,对环境进行描述时存在局限性。文中提出采用红外传感器和超声波传感器相结合来感知环境信息,完成障碍物信息的采集,并利用自适应加权融合算法实现数据融合的方案。实验仿真结果表明,多传感器数据融合后比单一传感器所采集的数据更接近于真实值,波动性小,并且不易受外界环境的影响。该方案较好地满足了移动机器人障碍探测的需要,具有一定的有效性和实用性。     

机器人的超声电机驱动及其控制研究

研究了超声电机在机器人中应用的可行性，探讨了行波超声电机（USM）的调速机理和调相伺服特性；引入了调相驱动的实现方法。针对超声电机驱动特点，在机器人动力学分析基础上，研究了动力学模型的线性化和解耦问题，提出了PD控制与前馈补偿相结合的机器人位置的控制方法。