自主移动机器人混合式体系结构的设计与实现

针对以往自主移动机器人体系结构在实时处理方面的不足,提出了一种基于多智能体的混合式体系结构,统一规划了机器人系统的软硬件结构,在该体系结构中设计并实现了协调Agent和推理Agent两种智能体,针对紧急事件进行了更实时的Agent实现,有效的提高的自主移动机器人在突发事件时的实时性,提出了使用多样化的信息组织形式,增强了系统的自适应能力和易扩展性。本文的实验结果表明在紧急状态下,系统的反应时间有效缩短,增加了系统的智能性和实时性。     

自主式移动机器人的递阶分布式监控

本文提出面向任务集成的自主式移动机器人体系结构,组成一个由分布式黑板协调 和组织的多智能体系统,具有自组织、自适应的特点．应用离散事件系统的监控理论,建立 单个智能体的DES模型,给出其满足一定运行指标的监控器,并证明整个系统的可控性．     

自主式移动机器人的递阶分布式监控

本文提出面向任务集成的自主式移动机器人体系结构,组成一个由分布式黑板协调和组织的多智能体系统,具有自组织、自适应的特点．应用离散事件系统的监控理论,建立单个智能体的ＤＥＳ模型,给出其满足一定运行指标的监控器,并证明整个系统的可控性     

自主微小型移动机器人的协作学习

自主微小型移动机器人的协作学习研究是多智能体机器人系统理论的主要研究方向。因为单个微小型移动机器人能力有限，所以机器人之间的协作在某些重要的基础工业和生物医学领域方面显得非常重要。该文介绍了几种用于协作学习的方法并且比较了它们之间的优点和缺点。最后，简要介绍了一些研究工作。     

多移动机器人的智能协作与导航

论文将从应用角度设计机器人智能协作和导航系统。主要技术包括:利用多MobileAgent模型实现多移动机器人的智能协作;设计KQML(知识查询和操作语言)来实现移动机器人的交流;利用GSM(移动通信系统)网解决多移动机器人的通讯问题;基于GPS(全球定位系统)、GIS(地理信息系统)系统实现导航。该研究结果适用于广域的DAI(分布式人工智能)领域和AIS(自主式智能系统)。     

神经网络的多传感器数据融合基于新算法在障碍物识别中的应用

本文提出了一种用于自主式移动机器人的障碍物类型识别的数据融合新方法，有两种不同的神经网络－小脑模型联接控制器和多层前向网分别来自ＣＣＤ摄象机的二维图象和来自超声测距系统的距离信息进行数据融合。     

化工企业优化多智能体系统的通信机制与协调规则

分析了用于复杂化工企业生产优化的多智能体系统。采取将大部分运算时间用于各智能体自身的局部优化求解，而只花少量时间将优化任务和局部优化结果通过网络进行交换作为基本准则，提出了适合化工企业多层优化多智能体系统的通信机制与协调规则。     

基于演化规划的多智能体系统中动态行为的建模

在多智能体系统中，每个智能体必须使自身适应环境动态地同其它智能体协调。为达到此目标，智能体须有预测其他智能体的行为及与其它智能体协作的能力，应动态地建立起自身的行为模型并且不断的演化它。     

MAS体系结构在遥操作系统中的应用研究

设计了一个基于多智能体系统MAS结构的遥操作系统框架模型，它结合离散事件状态DES控制模型，可用于在目前广泛采用的将虚拟现实与自主智能系统相结合来克服时延影响的遥操作系统中，解决现场环境的几何学、动力学模型参数未知或不准确引起的相关问题。并以在遥操作系统中的直升飞机作为执行端为例，说明该系统框架模型的具体应用和可用性。     

基于DCOM的动态运输调度多智能体系统研究*

针对物流运输调度中的客户需求动态性和随机性问题的解决,设计了一种基于DCOM的动态运输调度多智能体系统。在多智能体系统中,设计了包括预规划智能体、实时监控智能体、决策智能体和车辆智能体四类智能体。智能体以DCOM式组件形式实现,智能体之间的通信和协调由决策智能体集中执行。通过一个实例验证了系统算法的有效性,同时也为动态运输调度问题的解决提供了一个可参考的思路。     

基于弹簧负载倒立摆模型的仿袋鼠机器人稳定跳跃控制

仿生跳跃机器人具备很强的越障和环境适应能力,但是由于机器人运动过程中较短的可控时间以及腾空阶段运动的不确定性,运动的稳定性对于仿生跳跃机器人至关重要.本文对仿袋鼠机器人跳跃运动过程中的稳定跳跃控制问题进行了研究.首先采用双质量弹簧负载倒立摆模型(spring-loaded inverted pendulum,SLIP)模型对袋鼠机器人的结构进行简化,建立了机器人系统的动力学模型,并对机器人的运动过程以及着地相与腾空相的切换条件进行了分析.然后采用解耦控制的思想,将SLIP模型的运动控制分解为水平速度控制和跳跃高度控制两个方面,分别通过控制着地角度实现对水平运动速度的控制,通过能量补偿实现对跳跃高度的控制.最后在ADAMS仿真环境中建立机器人模型并进行了机器人运动仿真实验.实验结果表明,本文提出的方法可以实现仿袋鼠机器人稳定的周期性跳跃运动.     

A Study on Acquiring Underlying Behavioral Criteria for Manipulator Motion by Focusing on Learning Efficiency

Conventional humanoid robotic behaviors are directly programmed depending on the programmer's personal experience. With this method, the behaviors usually appear unnatural. It is believed that a humanoid robot can acquire new adaptive behaviors from a human, if the robot has the criteria underlying such behaviors. The aim of this paper is to establish a method of acquiring human behavioral criteria. The advantage of acquiring behavioral criteria is that the humanoid robots can then autonomously produce behaviors for similar tasks with the same behavioral criteria but without transforming data obtained from morphologically different humans every time for every task. In this paper, a manipulator robot learns a model behavior, and another robot is created to perform the model behavior instead of being performed by a person. The model robot is presented some behavioral criteria, but the learning manipulator robot does not know them and tries to infer them. In addition, because of the difference between human and robot bodies, the body sizes of the learning robot and the model robot are also made different. The method of obtaining behavioral criteria is realized by comparing the efficiencies with which the learning robot learns the model behaviors. Results from the simulation have demonstrated that the proposed method is effective for obtaining behavioral criteria. The proposed method, the details regarding the simulation, and the results are presented in this paper.     

Fuzzy-neuro position/force control for robotic manipulators with uncertainties

The performance of a controller for robot force tracking is affected by the uncertainties in both the robot dynamic model and the environmental stiffness. This paper aims to improve the controller’s robustness by applying the neural network to compensate for the uncertainties of the robot model at the input trajectory level rather than at the joint torque level. A self-adaptive fuzzy controller is introduced for robotic manipulator position/force control. Simulation results based on a two-degrees of freedom robot show that highly robust position/force tracking can be achieved, despite the existence of large uncertainties in the robot model.     

基于超声影像导航的肝癌消融机器人系统的误差传递

建立了基于超声影像导航的肝癌消融机器人系统误差传递模型．该系统主要由超声影像设备、导航 软件子系统、定位装置和穿刺机器人四个模块组成．机器人系统首先通过三维超声重建、术前模型和术中实体的 配准以及定位装置和机器人之间的坐标转换将肿瘤的目标靶点转化到机器人坐标系中,然后再控制机器人运动到 指定的靶点位置进行治疗．首先分析上述流程,指出误差源．然后,利用齐次变换矩阵的微分矩阵建立靶点映射 误差传递模型,并通过仿真实验验证了靶点误差模型的正确性．最后,对系统进行了精度测试实验,实验结果表 明该系统的总体误差小于5 mm,满足消融治疗肝癌的需求,能有效地提高肝癌的治疗效果．     

Control of an articulated wheeled mobile robot in pipes

We propose a control method in which an articulated wheeled mobile robot moves inside straight, curved and branched pipes. This control method allows the articulated wheeled mobile robot to inspect a larger area. The articulated wheeled mobile robot comprises pitch and yaw joints is and propelled by active wheels attached to the robot. Via the proposed control method, the robot takes on two different shapes; one prevents the robot from slipping inside straight pipes and the other allows movement in a pipe that curves in any direction. The robot is controlled by a simplified model for the robot's joint angles. The joint angles of the robot are obtained by fitting to a continuous curve along the pipe path. In addition, the angular velocity of the robot's active wheels is determined by a simplified model. The effectiveness of the proposed the control method was demonstrated with a physical implementation of the robot, and the robot was able to move inside straight, curved and branched pipes.     

机械手臂的多模型预测控制

针对机械手臂的非线性特点,提出了基于隶属度函数的多模型预测控制方法。该方法首先根据机械手臂的特点,选择合适的调度变量,将机械手臂的工作空间划分为若干个工作子空间,在每个子空间内的平衡点处对机械手臂进行线性化处理,得到相应的线性子模型,从而得到机械手臂的多模型表示;其次针对每个线性子模型设计局部预测控制器,使其在相应的子空间内达到控制要求;最后选择梯形隶属度函数与局部预测控制器进行加权求和,获得全局多模型预测控制器,以对机械手臂进行控制。仿真结果表明,当机械手臂的工作条件在大范围内变化时,全局多模型预测控制器的控制性能远优于常规PD控制器,达到了预期的控制目的。     

基于ADAMS的双足机器人建模与仿真

为了提高双足机器人的设计效率,可以通过虚拟样机技术对其进行设计与仿真。针对机器人设计双足行走步态,首先以实际的物理样机为原型,建立双足机器人的七连杆模型,并用解析法求得机器人的逆运动学模型;然后在ADAMS软件中建立参数化的虚拟样机模型,在Matlab软件中规划双足机器人在平地上的完整行走步态;最后将规划的步态导入ADAMS中,在虚拟样机上实现了双足机器人的行走仿真。仿真结果与规划的行走步态基本一致,验证了虚拟样机的有效性,从而为双足机器人的设计与步态规划提供了一种新的方法和可靠依据。     

Tri-tier Immune System in Anti-virus and Software Fault Diagnosis of Mobile Immune Robot Based on Normal Model

In this paper, anti-virus problem and software fault diagnosis of mobile robot, an immune robot, is discussed with proposal of a novel tri-tier immune system (TTIS). TTIS is a novel artificial immune system, which is comprised of three computing tiers and based on the normal model. The three tiers include inherent immune tier, adaptive immune tier and parallel immune tier. The tri-tier immune model is built on some theories of human immune system and has many good features, such as adaptability, immunity, memory, learning, and robustness. At the same time, for such immune robot, a novel normal model for the robot software is also proposed. The normal model is built on the space–time properties of each component in the robot software and can uniquely identify the normal state of the robot software. Such tri-tier immune system based on the normal model is suitable for anti-virus and fault diagnosis, which enable the immune robot to detect all viruses and faults in the robot software, recognize many viruses and faults, eliminate the viruses and faults, and repair the damaged robot software to its normal state. Meanwhile, simulation results show that the tri-tier immune system has the properties of immunity, security and robustness.     

机器人情感交互模型研究

为了实现机器人与人的和谐交互,该文给出了基于Multi-agent的情感机器人结构模型,提出了机器人情感交互模型构建方法,介绍了基于灰色系统的情感模型和情感关联模型,构造了机器人学习模型,实现了情感机器人交互系统,结果表明机器人能够和人进行有智能和情感的交互。     

Remote control of space robots

A method for the remote control of a space robot is proposed for the case of large delays in the transmission of control signals from the Earth to the local robot control system and in feedback signals. The method involves the use of the model of the space robot and its current environment with the simulation of gravity conditions at the ground control center. In this model environment, the operator should carry out the required actions by controlling the space robot model in the master-slave mode using an arm with six degrees of freedom capable of reflecting the interaction force of a model robot working tool with models of the objects of the environment. The arm movement trajectory and the law of time variation of the reflected interaction force vector are program-based for the local space robot control system and should be executed by it upon reception from the ground control center. The robot’s possible erroneous actions generated by the inevitable inaccuracy of the environment model are compensated by the proposed method of programmed trajectory correction. In accordance with it, in order to generate correction signals, additional information received from different sensors is used. These sensors can be installed on both the model and space robot itself. This information includes data on the mutual position of a robot’s working tool and models of the objects of the environment, as well as on the interaction forces between them. The paper presents a detailed theoretical justification of the proposed approach and experimental results that confirm the theoretical conclusions.