基于混合结构的机器人Agent控制结构的研究

该文面向分布Agent多移动机器人系统,提出了一种适合于多移动机器人的机器人Agent分层式体系结构,包括状态监测层、决策规划层、协调控制层和行为控制层,其中状态监测层主要实现整个系统对外部环境的状态监测。决策规划层设定系统的全局目标和单个机器人的局部目标,合理快速地完成任务的分解和分配,实现机器人之间任务级之间的协作。协调控制层完成机器人之间的运动协调。行为控制器主要采用基于行为的方法实现具体的运动控制。该结构应用于RoboCup环境下的分布多机器人系统中,满足复杂的、动态的应用环境和系统要求。     

MRCS中机器人控制体系框架结构

针对机器人协作系统中任务协作和运动协调的两个过程,考虑机器人自主性和协作性的要求,设计一种分层的机器人控制体系结构,其中包括协作规划层、协调规划层和行为控制层,该控制体系采用分层、模块式结构,有利于功能的扩充。     

多移动机器人协作任务的分布式控制系统

本文以多移动机器人协作执行围捕任务为背景，探讨了多机器人系统行为层规划及任务层中的任务分解和分配问题，提出了一种能有效降低计算量的编队中子任务的分配方法．为多移动机器人提供了一种在不确定环境中，动态变化的协作任务下进行基于传感器信息和通信信息的在线路径规划的方法．     

基于行为控制的半自主移动机器人系统

针对工作于动态、非结构化环境中的半自主移动机器人,构建了一个基于三层客户/服务器结构的机器人远程控制系统实验平台,论述了机器人客户端的软件结构,最后给出基于加权合成的多行为协作,从而实现机器人的复杂行为控制。实验证明系统实时性、稳定性高。整个系统基于开放源代码软件技术,采用面向对象思想,易于实现和扩展。     

基于多模式交互的多移动机器人分布式合作系统

本文研究合作型多移动机器人系统的分布式控制方法.为了保证多机器人间合作的实 时性和高效率,采用了一种分组策略,并提出了将局部感知和组内通信相集成的多模式机器人 间交互方法.考虑到任务的复杂性和真实环境的非结构化特点,构建了将递阶规划技术与基于 行为的反应式控制相结合的递阶混合式协调结构,并采用有限状态机模型实现了规划层与行为 层的协调机制.合作垃圾清运的实验结果证明了上述方法的有效性.     

服务机器人导航与调度系统技术研究

针对服务机器人导航和调度系统问题进行了研究。首先根据事先确定的任务进行自主全局路径规划,移动机器人在执行路径跟踪时,还要不断感知周围的局部环境信息;其次,移动机器人不仅要避开附近的移动障碍物,而且要进行局部规划或局部路径修正;然后,完成了地图创建、机器人全局定位、规划局部路径并控制车体运动;最后,介绍了服务机器人调度系统终端软件。实现了机器人与调度系统的结合,完成通过调度系统进行机器人运动控制的功能。     

自组分层式多机器人体系结构

本文旨在从宏观上建立一个通用、开放、适应于非结构及不确定环境下面向复杂任务的多机器人体系结构:自组分层式结构(SCLA).系统由上而下分成三层:指挥层、协调层和执行层.指挥层负责系统的全局规划和任务分配,对协调层直接管理.协调层负责带领所"管辖"的执行层成员完成指挥层分配的任务.执行层利用环境感知模块和运动控制模块顺序完成具体动作序列.系统采用"指令"通信模式和结构重组的策略来实施不确定环境下的协作任务.仿真实验表明自组分层式多机器人体系结构具有较高的鲁棒性和容错性,对任务和环境具有较好自适应性.     

AUV控制系统规划层使命与任务协调方法研究

以地形勘查使命为背景,对AUV(自治式水下机器人)规划层使命与任务协调方法进行了研究．介绍了AUV控制系统的逻辑结构,采用分层Petri网对AUV规划层离散事件系统进行了建模,通过对向目标区域航行任务库所进行扩展阐述了任务规划层的分层Petri网模型．提出了一种基于改进RW离散事件监控理论的协调方法,分别阐述了AUV使命与任务规划层离散事件系统的监控与协调．最后,通过湖试试验验证了AUV规划层使命与任务协调方法的正确性．     

基于栅格法的移动机器人运动规划研究

研究移动机器人运动规划问题.为实现机器人独立自主地完成任务行为,提出了一种利用栅格法对移动机器人运动进行规划,算法将运动规划分解为路径规划和运动控制两部分.在路径规划时,对栅格环境设定机器人的假想窗口栅格,并根据假想窗口和环境信息的可信度,设定实现环境信息优化的路径规划策略.根据机器人的运动学模型推导出机器人的运动控制律,以实现问题的求解.最后对运动规划方法进行了仿真验证,仿真结果证明了控制律的有效性.     

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)。     

A hybrid navigation strategy for multiple mobile robots

A hybrid navigation strategy is proposed in this paper for solving the navigation problem of multiple mobile robots. The proposed strategy integrates three algorithms that represent three different types of existing methods in a layered system. The bottom-up architecture of this system is the main contribution of this paper. This architecture pursues reliable low-level layers that can independently work in as much cases as possible, and the high-level layer is used only when it is necessary for guaranteeing convergence in complex situations. The simulation results show that the proposed strategy has well combined the algorithms of different types from the perspective of pursuing reactivity in the premise of ensuring convergence. Compared with the traditional top-down hybrid architecture, the bottom-up architecture proposed in this paper is more suitable for multi-robot navigation since it can better utilize the advantages of different algorithms to deal with different situations. The experiments on real robots have further verified the applicability of the proposed strategy.     

Integrated architecture for industrial robot programming and control

As robot control systems are traditionally closed, it is difficult to add supplementary intelligence. Accordingly, as based on a new notion of user views, a layered system architecture is proposed. Bearing in mind such industrial demands as computing efficiency and simple factory-floor operation, the control layers are parameterized by means of functional operators consisting of pieces of compiled code that can be passed as parameters between the layers. The required interplay between application-specific programs and built-in motion control is thereby efficiently accomplished. The results from experimental evaluation and several case studies suggest the architecture to be very useful also in an industrial context.     

Theory and Application of Multi-robot Service-oriented Architecture

In order to solve the problem of heterogeneity in multi-robot cooperation, a new service-oriented architecture is proposed for multi-robot cooperation. Service provision and energy consumption are the basic cooperative behaviors. A set of basic concepts of robot service are proposed. A layered multi-robot service-oriented architecture is designed. Finally, the experiments illustrate the superiority of the proposed architecture which makes robot's underlying functional components be transparently encapsulated and the services in upper layer be transparently invoked, which will effectively avoid the impact of heterogeneous characteristics in multi-robot cooperation and facilitate the system construction, expansion, restructuring and maintenance.      

多移动机器人合作系统中的单机控制体系结构研究

随着机器人技术的不断发展,出现了合作多移动机器人系统这一新的研究和应用领域,随之而来的是对机器人控制体系的新的要求．本文分析了合作多移动机器人系统对单机控制体系结构的要求,并以此为背景,在比较两种典型的智能机器人体系结构的基础上,提出一种混合分层的体系结构     

仿人机器人的分布式控制系统设计

针对仿人机器人系统自由度多,实时性与可靠性要求高的特点,设计了基于CAN总线的具有Windows与RT-Linux系统的双主机的主控层结构的分布式控制系统,整个控制系统采用集中管理分散控制的方式,按照控制系统的结构和功能划分为主控层、通信层、协调执行层3层。CAN总线与一般通信总线相比,它的数据通信具有较强的实时性,并且CAN总线连线简单,降低了系统连线的复杂程度,增强了系统的可靠性。其中基于Windows的控制系统负责仿人机器人关节电机的调试以及传感数据的显示;基于RT-Linux的系统实现机器人的实时运动控制。实验表明提出的分布式控制系统操作简便、安全可靠、实时性强,能充分满足仿人机器人系统调试与运动控制的要求。     

Transfer control of a large object by a group of mobile robots

In this paper, the authors propose a practical cooperative planning architecture of multiple mobile robots by means of a layered architecture consisting of a physical layer, a geometrical layer, and an informational layer. A “Virtual Impedance Model” is used in order to integrate the three layers. The effectiveness of the proposed architecture is verified by experiments and simulations on the task of transferring a large object by multiple mobile robots.     

THBIP-I拟人机器人研究进展

清华大学THBIP-I拟人机器人研究项目，由精密仪器系、机械工程系和自动化系组 成研究小组进行系统研究，其研究目的是发展先进机器人理论和技术，开发自主式拟人机器 人样机．THBIP-I机器人是具有头、手臂、躯干、腿和脚的拟人机器人，共32个自由度，并 具有视觉及语音识别等智能功能．结构设计方面，由直流无刷电机、滚珠丝杠、曲柄连杆机 构、谐波减速器组成，各驱动关节轴独立运动．控制系统分三层：组织层、协调层、执行层 ，分别完成任务规划、关节协调运动控制、关节伺服控制等任务．传感系统由关节位置检测 、地面反力检测、姿态检测、视觉系统、语音识别系统组成．电源系统采用机载电池系统供 电．本文将介绍THBIP-I机器人研究进展．     

A context layered locally recurrent neural network for dynamic system identification

This paper puts forward a novel recurrent neural network (RNN), referred to as the context layered locally recurrent neural network (CLLRNN) for dynamic system identification. The CLLRNN is a dynamic neural network which appears in effective in the input–output identification of both linear and nonlinear dynamic systems. The CLLRNN is composed of one input layer, one or more hidden layers, one output layer, and also one context layer improving the ability of the network to capture the linear characteristics of the system being identified. Dynamic memory is provided by means of feedback connections from nodes in the first hidden layer to nodes in the context layer and in case of being two or more hidden layers, from nodes in a hidden layer to nodes in the preceding hidden layer. In addition to feedback connections, there are self-recurrent connections in all nodes of the context and hidden layers. A dynamic backpropagation algorithm with adaptive learning rate is derived to train the CLLRNN. To demonstrate the superior properties of the proposed architecture, it is applied to identify not only linear but also nonlinear dynamic systems. The efficiency of the proposed architecture is demonstrated by comparing the results to some existing recurrent networks and design configurations. In addition, performance of the CLLRNN is analyzed through an experimental application to a dc motor connected to a load to show practicability and effectiveness of the proposed neural network. Results of the experimental application are presented to make a quantitative comparison with an existing recurrent network in the literature.