基于模糊自调节算法的乒乓球机器人回球速度计算

提出了一种新的回球速度计算模型, 达到以期望的落球速度定点回球的目的. 首先, 忽略了运动过程中马格努斯力的影响, 以多项式拟合乒乓球的运动轨迹, 利用LM算法求解得到回球速度的初始值. 然后, 提出了一种新的基于区域分割的经验数据存储与替换模式, 经线性拟合经验数据得到回球速度, 并与初始速度求加权平均值, 作为模糊调节的初始值. 分析了回球速度的各分量对落点误差的影响, 根据预测的落点误差, 利用模糊算法调节回球速度, 将调整后的结果用于控制回球过程中的球拍位姿与击球速度. 实验结果验证了方法的有效性.     

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.     

基于嵌入式Linux自主鱼的设计及应用

该文以自然界中的盒子鱼为原型,设计了一款基于自主视觉的机器鱼,通过图像传感器采集水下图像信息并进行处理和识别,并根据识别结果做出决策控制自身运动。系统以S3C2440为核心,在嵌入式Linux环境下通过传感器采集各种有效信息,并控制舵机实现自身上升、下潜和转弯等三维运动。本文设计了一种针对自主鱼的水球比赛实验,实验结果显示,该系统具有较高的实时性和很好的灵活性。     

群体机器人觅食任务Eulerian建模与分析

为了提高觅食任务性能,根据蚂蚁觅食原理引入了觅食概率的概念,机器人按照该概率决定是否离开Home区域,通过改变该概率值来调整环境中机器人的数量。为了研究觅食概率对系统性能的影响,利用Eulerian数学建模方法建立了觅食任务模型,并通过仿真实验分析了觅食概率对系统整体性能的影响。实验结果表明:觅食概率越大,环境中机器人数量越增加,机器人之间的干扰也增加,从而降低系统性能。最后通过实验分析给出了觅食概率与环境中任务数量以及机器人数之间的大致关系。     

仿生机器鱼运动学参数优化选择的试验方法研究

建立了仿生机器鱼在一维稳态游动时的运动学数学模型；运用遗传算法对该模型中的运动学参数如何进行优化选择和组合进行了试验研究，并提出了试验的模型和方法．该模型对于开展包括鱼雷在内的无人水下航行体的推进技术研究有较大的参考意义．     

机器人末端臂轨迹跟踪自适应控制设计

本文提出了新颖的机器人末端臂轨迹跟踪自适应控制方法。该方法与已有的神经网络模型不同之处在于数据首先利用运动学反解求出机器人各关节旋转的角度,然后应用径向基函数自组织进行神经网络学习生成模糊规则,利用监督学习算法(SLA)、最小二乘法(LMS)、反向传播算法(BP)和聚类分析的方法在线优化控制规则以及隶属函数的参数。仿真结果表明,该方法不但规则生成的时间少,有效的防止了规则数爆炸,而且在机器人轨迹跟踪控制的应用中效果好。     

A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations

This paper presents a distributed smooth time-varying feedback control law for coordinating motions of multiple nonholonomic mobile robots of the Hilare-type to capture/enclose a target by making troop formations. This motion coordination is a cooperative behavior for security against invaders in surveillance areas. Each robot in this control law has its own coordinate system and it senses a target/invader, other robots and obstacles, to achieve this cooperative behavior without making any collision. Each robot especially has a two-dimensional control input referred to as a “formation vector” and the formation is controllable by the vectors. The validity of this control law is supported by computer simulations.     

Robotic Welding Systems with Vision-Sensing and Self-learning Neuron Control of Arc Welding Dynamic Process

This paper addresses the vision sensing and neuron control techniques for real-time sensing and control of weld pool dynamics during robotic arc welding. Current teaching playback welding robots are not provided with this real-time function for sensing and control of the welding process. In our research, using composite filtering technology, a computer vision sensing system was established and clear weld pool images were captured during robotic-pulsed Gas Tungsten Arc Welding (GTAW). A corresponding image processing algorithm has been developed to pick up characteristic parameters of the weld pool in real-time. Furthermore, an ANN model of the weld pool dynamic process of robotic-pulsed GTAW was developed. Based on neuron self-learning PSD controller design, the real-time control of weld pool dynamics during the pulsed GTAW process has been realized in robotic systems.     

网络传感器在机器人感知系统中的应用研究

本文针对网络环境下机器人感知系统信息获取技术，结合IEEE1451.2网络传感器设计 思想，提出一种即插即用机器人感知系统节点的设计方法，并针对具体感知节点提出一种在 线编程的方法，即可在线修改感知系统节点中具体的传感器／执行器的有关参数，如：生产 厂家、日期、校准参数等；并给出了网络环境下机器人感知系统的网络拓扑结构以及数字接 口．     

Sensor Fusion and Planning with Perception–Action Network

Robot intelligence requires a real-time connection between sensing and action. A new computation principle of robotics that efficiently implements such a connection is utmost important for the new generation of robotics. In this paper, a perception–action network is presented as a means of efficiently integrating sensing, knowledge, and action for sensor fusion and planning. The network consists of a number of heterogeneous computational units, representing feature transformation and decision-making for action, which are interconnected as a dynamic system. New input stimuli to the network invoke the evolution of network states to a new equilibrium, through which a real-time integration of sensing, knowledge, and action can be accomplished. The network provides a formal, yet general and efficient, method of achieving sensor fusion and planning. This is because the uncertainties of signals, propagated in the network, can be controlled by modifying sensing parameters and robot actions. Algorithms for sensor planning based on the proposed network are established and applied to robot self-localization. Simulation and experimental results are shown.