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Jingdong book

移动机器人技术基础与制作

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Jingdong book

移动机器人技术基础与制作

Name: 移动机器人技术基础与制作
Brand: Jingdong book
SKU: 3118077321
Price: 42.32 USD
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《移动机器人技术基础与制作》按照移动机器人技术所涉及的内容和主要方向来安排，从移动机器人基础、控制理论应用及移动机器人制作应用三个层面上对移动机器人技术作了系统的阐述。

Content Description

《移动机器人技术基础与制作》共分11章，内容包括移动机器人运动学与动力学、移动机器人控制技术、移动机器人的传感器、移动机器人动力学、机器人视觉、移动机器人的通信、移动机器人人机接口、移动机器人人工智能、智能轮椅、智能声控机器人的制作等。本书内容丰富、条理清晰，从理论与实践出发，结合作者研究过程中的一些心得体会，在简单阐述理论的基础上，通过实际例子来讲解，力求使读者能较快掌握移动机器人基础与制作方面的相关知识。
《移动机器人技术基础与制作》可作为自动化、计算机、电子信息、机械工程等相关专业本科生和研究生教材，也可供相关教学、科研与工程技术人员阅读和参考。

Catalogue

第1章绪论
1.1 移动机器人概述
1.1.1 移动机器人的定义
1.1.2 移动机器人的发展概况
1.2 移动机器人的结构与分类
1.2.1 移动机器人的结构
1.2.2 移动机器人的分类
1.3 移动机器人的研究领域及关键技术
1.4 移动机器人的应用及展望

第2章移动机器人的传感器
2.1 机器人内部传感器
2.1.1 位置传感器
2.1.2 姿态传感器
2.1.3 压力传感器
2.1.4 加速度传感器
2.2 机器人外部传感器
2.2.1 接近觉传感器
2.2.2 触觉传感器
2.2.3 力觉传感器
2.2.4 温度传感器
2.2.5 气体传感器
2.2.6 听觉传感器
2.2.7 视觉传感器
2.2.8 深度传感器
2.3 传感器的性能指标
2.4 多传感器信息融合技术
2.4.1 多传感器信息融合的关键问题
2.4.2 信息融合的具体方法
2.4.3 多传感器信息融合的结构和控制
2.5 小结
2.6 练习题

第3章移动机器人的通信技术
3.1 移动机器人通信系统的特点与设计
3.1.1 移动机器人通信的特点
3.1.2 移动机器人通信系统设计
3.2 常用无线通信技术及在移动机器人中的应用
3.2.1 移动通信系统简介
3.2.2 移动通信网络在机器人中的应用
3.2.3 红外技术
3.2.4 蓝牙通信技术
3.2.5 ZigBee通信技术
3.2.6 Wi-Fi通信息技术
3.2.7 Ad Hoc通信技术
3.2.8 UWB超宽带通信息技术
3.3 基于Internet/Web的远程移动机器人
3. 3.1 基于Internet的远程机器人
3.3.2 基于Web的远程机器人
3.4 物联网技术
3.4.1 物联网定义及本质
3.4.2 物联网的技术特点
3.5 小结
3.6 练习题

第4章移动机器人运动学与动力学
4.1 机器人位置运动学
4.1.1 空间点表示
4.1.2 齐次变换的表示
4.2 机器人的运动学
4.2.1 正运动学分析
4.2.2 机器人的工作空间研究
4.2.3 逆运动学求解
4.3 微分运动和速度
4.3.1 微分关系
4.3.2 刚体运动的速度
4.3.3 雅可比矩阵
4.4 移动机器人动力学
4.4.1 牛顿-欧拉方程
4.4.2 轮式机器人的动力学分析
4.5 小结
4.6 练习题

第5章移动机器人控制技术
5.1 经典控制技术
5.1.1 模拟PID控制器的数学模型
5.1.2 数字PID控制器的数学模型
5.2 现代控制技术
5. 2.1 机器人变结构控制
5.2.2 机器人自适应控制
5.3 智能控制技术
……
第6章移动机器人视觉技术
第7章移动机器人定位与路径规划
第8章移动机器人人工智能
第9章移动机器人人机接口
第10章智能轮椅
第11章智能声控机器人的制作
参考文献
名词索引

Book Abstract

3.Dempster-Shafer证据推理
该方法是贝叶斯方法的扩展，它将前提严格的条件从仅是可能成立的条件中分离出来，从而使任何涉及先验概率的信息缺乏得以显示化。它用信任区间描述的信息，不但表示信息的已知性和确定性，而且能够区分未知性。多传感器信息融合时，将传感器采集的信息作为证据，在决策目标集上建立相应的基本可信度，这样，证据推理能在同一决策框架下，用Dempster合并规则将不同的信息合并成一个统一的信息表示。证据决策理论允许直接将可信度赋予传感器信息的合取，既避免了对未知概率分布所做的简化假设，又保留了信息。证据推理的这些优点使其广泛应用于多传感器信息的定性融合。
4.模糊理论和神经网络
在多传感器系统中，各种信息源提供的环境信息都具有一定程度的不确定性，对这些不确定的信息的融合过程实质上是一个不确定性推理过程。模糊逻辑是一种多值型逻辑，指定一个从0到1之间的实数表示其真实度。模糊融合过程直接将不确定性表示在推理过程中。如果采用系统中的方法对信息融合中的不确定性建模，则可产生一致性模糊推理。
神经网络根据样本的相似性，通过网络权值表述在融合的结构中，首先通过神经网络特定的学习算法来获取知识，得到不确定性推理机制，然后根据这一机制进行融合和再学习。神经网络的结构本质上是并行的，这为神经网络在多传感器信息融合中的应用提供了良好的前景。基于神经网络的多信息融合具有以下特点：
（1）具有统一的内部知识表示形式，并建立基于规则和形式的知识库；
（2）利用外部信息，便于实现知识的自动获取和并行联想推理；
（3）能够将不确定的复杂环境通过学习转化为系统能够理解的形式；
（4）神经网络的大规模并行处理信息能力，使系统的处理速度变快。
5.卡尔曼滤波
卡尔曼滤波用于动态环境中冗余传感器信息的实时融合，该方法同测量型的统计特性系统递推给出统计意义上的最优融合信息估计。如果系统具有线性动力学模型，且系统和传感器噪声是高斯分布的白噪声，卡尔曼滤波为融合信息提供一种统计意义上的最优估计。
……

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