基于状态机的巡线机器人控制系统设计

通过对巡线机器人运动控制系统的分析,提出采用有限状态机实现运动控制系统中的运动保护及机构定位系统的方法.结合巡线机器人运动控制的特殊需求,通过对运动控制系统的时序分析,设计了运动保护及定位系统的状态机.并在可编程器件上实现了巡线机器人运动控制器,从而给出了基于可编程器件设计控制系统的一种方法.     

智能空间环境下基于本体的机器人服务自主认知及规划

为了提高机器人服务的自主性,针对动态家庭环境提出基于本体的机器人服务自主认知及规划方法.首先,利用本体技术为智能空间系统建立本体模型,并通过构建语义规则的方法,建立以用户为中心自适应调整的数据-概念转换机制,实现智能空间信息的整合.在此基础上,建立服务任务推理规则库对本体模型进行扩展,通过匹配实时更新的智能空间本体与规则库中的知识,推理出机器人需要执行的服务序列,实现机器人对用户所需服务的自主认知.最后,利用分层任务网络的思想,在JSHOP2规划器上实现服务任务的具体规划.智能空间环境下的任务执行实验结果表明,利用该方法服务机器人能够根据环境信息和用户信息实现对任务的自主认知,进而主动地为用户提供个性化的服务,其服务的智能化水平得以显著提高.     

基于Adaboost的架空输电线路巡线机器人障碍识别

巡线机器人沿电力线行走时必须探测和识别各种障碍物,同时根据障碍类型规划越障行为。文章提出了一种基于Adaboost算法的架空输电线路巡线机器人障碍物识别方法。Adaboost是一个构造准确分类器的学习方法,它把一簇弱分类器通过一定的规则结合成为一个强分类器,再把这些强分类器级联成为一个快速、准确的分类器。实验结果证明了该方法的可行性。     

一种输电线路巡检机器人越障规划方法

针对输电线路巡检机器人的局部自主与远程控制方式,提出了一种基于有限状态机的越障规划方法.将巡检机器人的越障运动过程分解成若干离散化的关键状态,建立了运动规划的有限状态机模型.采用一种基于模糊方法的产生式系统用于推理越障模式,并产生运动序列.仿真与现场实验验证了越障规划方法的正确性和有效性,该方法可应用于输电线路巡检机器人运动控制.     

巡线机器人仓储管理系统设计与开发

为实现自动化仓储信息管理,设计开发了一种基于巡线机器人技术的仓储信息管理系统。机器人的核心控制器采用双DSP主从式结构,主DSP用于实现机器人的运动控制,从DSP实现对货物条形码信息和货位信息的采集与存储。该系统利用巡线机器人遍历所有仓储货位,完成仓储货物信息的自动采集、存储、传输,并通过上位机实现对信息的数据库管理。机器人与上位机之间采用无线方式实现通讯。     

基于PID控制算法的自动巡线机器人的设计与实现

自动巡线移动机器人能在各种环境下独立执行巡线任务,适用于工业、农业、物流等领域。传统机器人在复杂或需快速响应的环境中效率低。为此,采用PID（比例-积分-微分）控制算法优化机器人的路径追踪和运动控制,提高巡线的准确性和稳定性。通过硬件和软件的综合设计,实现了一款基于PID算法的高效、稳定的自动巡线机器人,其设计的有效性通过装配、调试和实验得到了验证,为自动巡线机器人在各行业的应用提供了理论和实践基础,有助于扩大自动化技术的应用范围和提高效率。     

基于AVR单片机的新型巡线机器人系统设计与实现

提出了一种基于AVR单片机Atmega8L为核心处理器的巡线机器人控制系统设计方案,详细介绍了颜色传感器MCS3AO在巡线机器人中的应用。本文给出了系统的硬件组成以及软件算法。最后通过实验论证:由此方法设计出的巡线机器人硬件电路简单,算法实现容易,机器人系统稳定,可靠性高,扩展性强。     

基于CLIPS的卫星任务规划专家系统设计

本文提出了基于CLIPS的卫星任务规划专家系统的设计方法,详细分析了系统的结构和功能,重点讨论了中文产生式系统的BNF范式、基于上下文的推理机制和集合运算符。中文产生式系统的BNF范式基于CLIPS标准BNF范式定义,并依据BNF范式进行规则表示和规则自定义获取;推理机采用上下文限制的规则控制策略,依据不同的上下文加载相关的事实和规则,提高推理机的运行效率;利用规则中的对象逻辑子式进行了集合运算符的设计,并对极值运算符、属性差值运算符和均值运算符等三类集合运算符进行了探讨。该系统解决了卫星任务规划中知识表示和知识获取问题,提高了卫星任务规划推理效率,为卫星任务规划人员提供有效的辅助决策功能。     

面向手机3D动画自动生成的空间分层布局规划

手机3D动画自动生成系统是将用户发送的短信,经过信息抽取、情节规划等一系列的处理,最终生成一段与短信内容相关的三维动画并发送给接收方这一过程.布局规划是系统的一个重要组成部分,其功能是将模型放置到场景的可用空间上.通过对布局问题的研究,本文提出了一种基于区域布局和模型布局的分层布局方法,其包括定性规划和定量计算两个部分,利用语义网技术建立分层布局知识库,基于规则的方法推导出分层布局定性规划方案,并对模型位置进行动态计算.最后,通过实验验证了分层布局规划的有效性和多样性,满足了自动生成系统对布局规划的要求.     

EMM:一种用于产生式系统的高速匹配算法

ＥＭＭ：一种用于产生式系统的高速匹配算法＠张大力＠毛祖楫＠阎平凡￥清华大学自动化系产生式系统，编码，规则库，推理机ＥＭＭ：一种用于产生式系统的高速匹配算法张大力毛祖楫阎平凡（清华大学自动化系北京１０００８４）关键词产生式系统，编码，规则库，推理机收稿日期１...     

基于神经-模糊控制系统的移动机器人动态路径规划

针对机器人在未知、复杂环境下从源到目标之间,避开各种类型的障碍的问题,设计了系统的神经-模糊控制算法进行动态路径规划：设计了合理的模糊推理体系,实现输入模糊化、模糊推理规则库、输出去模糊化控制;根据规则库设计神经网络结构,简化网络结构和参数;采用QPSO算法训练网络;状态变量的存储和管理策略,解决了“U”型障碍物内的死循环路径问题。实验结果表明,在以上算法的控制下,机器人能够朝着目标,规划产生合理的路径,不会陷入死循环。     

Real-time exploration of a multi-robot rescue system in disaster areas

A system procedure is proposed for a multi-robot rescue system that performs real-time exploration over disaster areas. Real-time exploration means that every robot exploring the area always has a communication path to human operators standing by at a base station and that the communication path is configured by ad hoc wireless networking. Real-time exploration is essential in multi-robot systems for USAR (urban search and rescue) because operators must communicate with every robot to support the victim detection process and ad hoc networking is suitable to configure a communication path among obstacles. The proposed system procedure consists of the autonomous classification of robots into search and relay types and behavior algorithms for each class of robot. Search robots explore the areas and relay robots act as relay terminals between search robots and the base station. The rule of the classification and the behavior algorithm refer to the forwarding table of each robot constructed for ad hoc networking. The table construction is based on DSDV (destination-sequenced distance vector) routing that informs each robot of its topological position in the network and other essentials. Computer simulations are executed with a specific exploration strategy of search robots. The results show that a multi-robot rescue system can perform real-time exploration with the proposed system procedure and reduce exploration time in comparison with the case where the proposed scheme is not adopted.     

基于置信规则库的飞控系统故障诊断

针对传统飞控系统故障诊断中存在的因引入专家知识引起的主观偏差问题和使用数据驱动方法因数据量不足导致的过拟合问题,提出了基于置信规则库推理的飞控系统故障诊断。根据已有故障知识构建飞控系统故障诊断置信规则库,利用测试过程中获得的故障数据,以数值样本优化学习模型对置信规则库参数进行训练。实例表明,经少量样本训练后的置信规则库可以很好地解决初始置信规则库参数存在主观偏差的问题,经实验证明该方法能够实现高效可靠的飞控系统故障诊断。     

Kinematic calibration for collaborative robots on a mobile platform using motion capture system

For modern robotic applications that go beyond the typical industrial environment, absolute accuracy is one of the key properties that make this possible. There are several approaches in the literature to improve robot accuracy for a typical industrial robot mounted on a fixed frame. In contrast, there is no method to improve robot accuracy when the robot is mounted on a mobile base, which is typical for collaborative robots. Therefore, in this work, we proposed and analyzed two approaches to improve the absolute accuracy of the robot mounted on a mobile platform using an optical measurement system. The first approach is based on geometric operations used to calculate the rotation axes of each joint. This approach identifies all rotational axes, which allows the calculation of the Denavit–Hartenberg (DH) parameters and thus the complete kinematic model, including the position and orientation errors of the robot end-effector and the robot base. The second approach to parameter estimation is based on optimization using a set of joint positions and end-effector poses to find the optimal DH parameters. Since the robot is mounted on a mobile base that is not fixed, an optical measurement system was used to dynamically and simultaneously measure the position of the robot base and the end-effector. The performance of the two proposed methods was analyzed and validated on a 7-DoF Franka Emika Panda robot mounted on a mobile platform PAL Tiago-base. The results show a significant improvement in absolute accuracy for both proposed approaches. By using the proposed approach with the optical measurement system, we can easily automate the estimation of robot kinematic parameters with the aim of improving absolute accuracy, especially in applications that require high positioning accuracy.     

Quasi sliding mode‐based single input fuzzy self‐tuning decoupled fuzzy PI control for robot manipulators with uncertainty

This paper presents a robust adaptive control strategy for robot manipulators, based on the coupling of the fuzzy logic control with the so‐called sliding mode control (SMC) approach. The motivation for using SMC in robotics mainly relies on its appreciable features. However, the drawbacks of the conventional SMC, such as chattering effect and required a priori knowledge of the bounds of uncertainties can be destructive. In this paper, these problems are suitably circumvented by adopting a reduced rule base single input fuzzy self tuning decoupled fuzzy proportional integral sliding mode control approach. In this new approach a decoupled fuzzy proportional integral control is used and a reduced rule base single input fuzzy self‐tuning controller as a supervisory fuzzy system is added to adaptively tune the output control gain of the decoupled fuzzy proportional integral control. Moreover, it is proved that the fuzzy control surface of the single‐input fuzzy rule base is very close to the input/output relation of a straight line. Therefore, a varying output gain decoupled fuzzy proportional integral sliding mode control approach using an approximate line equation is then proposed. The stability of the system is guaranteed in the sense of the Lyapunov theorem. Simulations using the dynamic model of a 3DOF planar manipulator with uncertainties show the effectiveness of the approach in high speed trajectory tracking problems. The simulation results that are compared with the results of conventional SMC indicate that the control performance of the robot system is satisfactory and the proposed approach can achieve favorable tracking performance, and it is robust with regard to uncertainties and disturbances. Copyright © 2011 John Wiley & Sons, Ltd.     

Adaptive fuzzy regulation of the DC-bus capacitor voltage in a wind energy conversion system (WECS)

This paper proposes a new voltage regulator of the DC-bus capacitor of a variable speed wind power generation system based on adaptive fuzzy system. The change in the fuzzy rule base is done using a variable-structure direct adaptive control algorithm to achieve the pre-defined control objectives. This algorithm has two merits. First, it has a good performance in the training phase as it makes use of the initial rule base defined for the fuzzy logic controller. Second, it has a robust estimator since it depends on variable structure technique. The adaptive nature of the new controller significantly reduces the rule base size and improves its performance.     

基于规则库的巡线机器人自主越障动作规划

在电力线巡检机器人的控制系统中,采用分层规划的方式,利用产生式推理从行为规划的层次上进行在线规划,生成行为序列,然后将生成的行为序列与离线规划出的知识库相结合,生成自主越障的动作序列,实现机器人越障的自动控制．本文探讨了整个越障规划的控制过程和规划方法,并通过实验验证了动作规划的可行性．     

Multi-robot coordination for jams in congested systems

This paper describes a novel coordination methodology of autonomous mobile robots for jams in a congested system with bottlenecks. This methodology consists of two approaches based on an interaction force and behavior regulation rule for a robot. The former is for directly controlling velocity of a robot in the behavioral dynamics, and the latter is for amplifying the interaction force so that velocity of a robot is externally reduced in a certain place. In the first approach, a previously-proposed robot behavior control technique by the authors that utilizes the interaction force among robots is improved, and it enables the robots to reduce their velocity in response to not only a jam but also a decelerating robot immediately in front of them. In the second approach, a behavior regulation rule in connection with the interaction force is designed and provided in congested segments on a lane. Thus, the amplified interaction force causes the robots to move more slowly in the congested segments. The improved robot behavior control technique and behavior regulation rule are implemented in simulation experiments and compared to the previous robot behavior control technique and adaptive cruise control (ACC) that has been proposed for vehicles. Furthermore, the improved interaction force and behavior regulation rule are appended to ACC, and the potential of using ACC with the two approaches is discussed. Finally, the effectiveness of the improved interaction force and the behavior regulation rule for multi-robot coordination in a congested system with bottlenecks is shown.     

Constructing a nutrition diagnosis expert system

This paper presents a research of constructing a web-based expert system for nutrition diagnosis by utilizing the expert system techniques in artificial intelligence. The research implements Nutritional Care Process and Model (NCPM) defined by American Dietetic Association (ADA) in 2008 and integrate the nutrition diagnosis knowledge from dietetics professionals to establish the basics of building the rule-based expert system with its knowledge base. The system is built using Microsoft Visual Studio 2008 on .NET Framework 3.5SP1 utilizing the built in rule engine which comes with Windows Workflow Foundation.With the help of this system, it is easier for dietetics professionals to adapt to the newly introduced concept of nutrition diagnosis. At the heart of the web based expert system is a knowledge base, it has a rule engine which contains the nutrition diagnosis rules converted from signs and symptoms for nutrition diagnosis from dietetics professionals and are expressed in XML format which are then stored in a SQL database. A knowledge engineer will be able to use a rule editor to add new rules or to update existing rules within the rule database. Dietetics professionals would be able to enter patient’s basic data, anthropometric data, physical exam findings, biochemical data, and food/nutrition history into the program. After dietetics professionals complete nutrition assessment, the program will make inference to the rule base and make nutrition diagnosis. Dietetics professionals could then make the final diagnosis decision for the patient based on the diagnosis report generated by the web based nutrition diagnosis expert system.For this study, I have selected 100 chronic kidney disease patients under hemodialysis from a university hospital, recorded their albumin, cholesterol, creatinine before dialysis, height, and dry weight and then use these data to perform nutrition diagnosis with both the expert system and a practicing dietitian. After comparing the result, I found that the expert system is faster and more accurate than human dietitian.     

Input Displacement Neuro-fuzzy Control and Object Recognition by Compliant Multi-fingered Passively Adaptive Robotic Gripper

The requirement for new flexible adaptive grippers is the ability to detect and recognize objects in their environments. It is known that robotic manipulators are highly nonlinear systems, and an accurate mathematical model is difficult to obtain, thus making it difficult make decision strategies using conventional techniques. Here, an adaptive neuro fuzzy inference system (ANFIS) for controlling input displacement and object recognition of a new adaptive compliant gripper is presented. The grasping function of the proposed adaptive multi-fingered gripper relies on the physical contact of the finger with an object. This design of the each finger has embedded sensors as part of its structure. The use of embedded sensors in a robot gripper gives the control system the ability to control input displacement of the gripper and to recognize particular shapes of the grasping objects. Fuzzy based controllers develop a control signal according to grasping object shape which yields on the firing of the rule base. The selection of the proper rule base depending on the situation can be achieved by using an ANFIS strategy, which becomes an integrated method of approach for the control purposes. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back propagation algorithm. The simulation results presented in this paper show the effectiveness of the developed method.