About This Book
Design, simulate, build and program an interactive autonomous mobile robotProgram Robot Operating System using PythonGet a grip on the hands-on guide to robotics for learning various robotics concepts and build an advanced robot from scratch
Who This Book Is For
If you are an engineer, a researcher, or a hobbyist, and you are interested in robotics and want to build your own robot, this book is for you. Readers are assumed to be new to robotics but should have experience with Python.
What You Will Learn
Understand the core concepts and terminologies of robotics Create 2D and 3D drawings of robots using freeware such as LibreCAD and Blender Simulate your robot using ROS and Gazebo Build robot hardware from the requirements Explore a diverse range of actuators and its interfacing Interface various robotic sensors to robots Set up and program OpenCV, OpenNI, and PCL to process 2D/3D visual data Learn speech processing and synthesis using Python Apply artificial intelligence to robots using Python Build a robot control GUI using Qt and Python Calibration and testing of robot
In Detail
Learning about robotics will become an increasingly essential skill as it becomes a ubiquitous part of life. Even though robotics is a complex subject, several other tools along with Python can help you design a project to create an easy-to-use interface.
Learning Robotics Using Python is an essential guide for creating an autonomous mobile robot using popular robotic software frameworks such as ROS using Python. It also discusses various robot software frameworks and how to go about coding the robot using Python and its framework. It concludes with creating a GUI-based application to control the robot using buttons and slides.
By the end of this tutorial, you'll have a clear idea of how to integrate and assemble all things into a robot and how to bundle the software package.
About the Author
Lentin Joseph
Lentin Joseph is an electronics engineer, robotics enthusiast, machine vision expert, embedded programmer, and the founder and CEO of Qbotics Labs (http://www.qboticslabs.com) in India. He got his bachelor's degree in electronics and communication engineering at the Federal Institute of Science and Technology (FISAT), Kerala. In his final year engineering project, he created a social robot, which can interact with people. The project was a huge success and got mentioned in visual and print media. The main feature of this robot was that it could communicate with people and reply intelligently. It also has some image-processing capabilities, such as face, motion, and color detection. The entire project was implemented using the Python programming language. His interest in robotics, image processing, and Python began this project. After graduation, he worked at a start-up company based on robotics and image processing for 3 years. In the meantime, he learned famous robotic software platforms―such as Robot Operating system (ROS), V-REP, and Actin (a robotic simulation tool)―and image processing libraries, such as OpenCV, OpenNI, and PCL. He also knows about robot 3D designing, embedded programming on Arduino, and Stellaris Launchpad. After 3 years of work experience, he started a new company called Qbotics Labs, which is mainly focused on research to build great products in domains such as wearable technology, robotics, machine vision, green technology, and online education. He maintains a personal website (http://www.lentinjoseph.com) and a technology blog called technolabsz (http://www.technolabsz.com). He publishes his works on his tech blog. He was a speaker at PyCon2013 India, and he spoke on the topic of learning robotics using Python.
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**第二段评价:** 说实话,我抱着极大的期待入手这本大部头的。作为一名资深软件工程师,我对理论的严谨性要求比较高,而这本书在理论深度上确实没有让人失望。它没有停留在那些肤浅的“即插即用”的代码层面,而是深入探讨了底层传感器数据融合背后的概率论基础和卡尔曼滤波的各种变体。我特别欣赏作者对不确定性处理的视角,这在很多同类书籍中常常被一笔带过。书中的章节结构安排得像一部精心编排的交响乐,从基础的刚体变换,到复杂的路径规划算法,例如RRT*和PRM,每一步都建立在前一步的知识之上,逻辑链条非常完整。虽然某些涉及高维空间操作的部分需要我反复阅读并结合外部资源进行辅助理解,但这恰恰说明了它内容的密度和价值。对于希望从“会用”提升到“精通”的读者来说,这本书提供的知识广度和深度是无可替代的。它更像是一本可以放在案头,随时查阅和深思的参考手册,而非一次性读完就束之高阁的书籍。
评分**第五段评价:** 从一个刚开始接触机构学的学生的角度来看,这本书的配套资源简直是太给力了。配套的在线代码仓库维护得非常及时,而且作者还上传了一些教学视频,用来演示书中复杂的仿真环境的搭建过程。这一点尤其重要,因为对于初学者,光靠文字描述去想象一个三维的仿真场景确实有些吃力。书中的每一个关键算法,无论是Dijkstra还是A*搜索,都有一个配套的简单可视化脚本,让我们能够亲眼看到搜索树是如何一步步生成的。此外,书中对特定硬件的选型建议也非常中肯,没有一味推荐昂贵或小众的设备,而是侧重于性价比高、社区支持好的主流平台。这使得我们学校的实验室能够以较低的成本复现书中的所有实验。这本书真正做到了“手把手”教学,让理论学习和动手实践无缝衔接,极大地降低了入门机器人学的门槛。
评分**第一段评价:** 这本书的装帧设计简直是业界良心,封面那种磨砂质感,拿在手里沉甸甸的,一看就知道是下足了功夫的。我第一次翻开它的时候,就被它精美的插图和清晰的排版吸引住了。那些关于机械臂运动学和逆运动学的示意图,画得极其生动形象,即便是像我这种非科班出身的初学者,也能立刻领悟到那些复杂的几何关系。尤其是章节之间的过渡,处理得非常平滑,完全没有那种生硬的知识点堆砌感。作者在讲解每一个算法时,都会先给出一个高层次的直观理解,然后再逐步深入到数学推导,这种循序渐进的方式,让我在学习过程中始终保持着一种“我能理解”的信心。而且,书中的代码示例都是可以直接运行的,并且配有详尽的注释,这对于动手实践至关重要。我花了一个周末的时间,严格按照书中的步骤搭建了一个简单的SLAM(同步定位与地图构建)演示环境,运行结果完美复现了书中的效果,那种成就感,真是难以言喻。这本书与其说是一本教材,不如说是一位经验丰富的导师,耐心地在你身边引导。
评分**第四段评价:** 我必须承认,这本书的语言风格非常独特,它不像教科书那样板着脸孔,反而带有一种近乎哲学的思辨色彩。在讨论决策制定和规划范式时,作者似乎总是在引导读者思考“机器人应该如何‘思考’”,而不是简单地告诉我们“应该如何编程”。我尤其喜欢其中对模糊逻辑和有限状态机在行为决策中的应用分析,那种将工程问题与认知科学交叉结合的论述,极大地拓宽了我的视野。阅读体验上,虽然篇幅较大,但作者非常善于使用比喻来解释抽象概念。例如,他将迭代优化过程比喻成在崎岖地形中寻找最高点的登山者,这个形象的比喻,让我在理解梯度下降法收敛性的那一刻豁然开朗。这本书的价值不在于提供快速的答案,而在于教会你提出更深刻的问题。它挑战了我的固有思维模式,迫使我从更宏观的角度去审视机器人的智能构建过程。
评分**第三段评价:** 这本书的实战性可以说是超乎我的预期。我之前尝试过几本号称“实战”的机器人书籍,结果发现它们要么代码过于陈旧,要么环境配置复杂到让人抓狂。但这本则完全不同,它紧密围绕着当下主流的开源库和硬件平台进行讲解。比如,在介绍视觉伺服控制时,书中详细描述了如何集成ROS(机器人操作系统)的消息机制来实时获取摄像头数据,并同步到控制循环中。我印象最深的是关于力控的部分,作者不仅解释了牛顿-欧拉公式,还展示了如何利用现代控制理论中的滑模控制器来抑制外部干扰,并且给出了详细的参数调优指南。对于我们这些需要在实验室环境中快速搭建原型系统的工程师来说,这种“理论指导实践,实践反哺理论”的模式极其高效。书末的附录中对几种常见调试工具的使用技巧也做了简要介绍,这些“边角料”信息,恰恰是日常工作中大大提高效率的秘诀所在。
评分http://pdf.th7.cn/down/files/1602/Learning%20Robotics%20using%20Python.pdf
评分挺不错的书
评分http://pdf.th7.cn/down/files/1602/Learning%20Robotics%20using%20Python.pdf
评分http://pdf.th7.cn/down/files/1602/Learning%20Robotics%20using%20Python.pdf
评分http://pdf.th7.cn/down/files/1602/Learning%20Robotics%20using%20Python.pdf
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