I'm a professional robotics research consultant, with 30 years of experience working for organizations like SRI International and JPL.
Like computers, robotics has quite a strong divide between the software and the hardware. Hardware is further subdivided into actuators and sensors.
If you'd said "I want to get into computers", I would explain that only a few hardware engineers actually design and build physical computers--most researchers assume that the hardware and firmware has been built already, and then they worry about the software--how to make the system actually work.
Similarly with robots, building the hardware is a job for the mechanical engineers (to design the structure and heat dissipation), with little bits and pieces for power electrical engineers (to spec the motors) and computer engineers (to design the firmware silicon). Next-generation robots also use industrial designers (to make the outsides look pretty, and the insides fit well together).
Research areas for actuator design include fingered hands; tentacles; hummingbird and other bird and insect wings; springy wheels; legs; non-electronic designs for high radiation areas; and surgical instruments.
With cameras in every cell phone, vision sensors are mostly a solved problem at this point. Research areas for sensor design include smart flexible tactile skin, brain wave sensors, and other biomedical sensors. There's still some room for good force sensors as well. These fall in the realms of materials engineering, computer engineering, mechanical engineering, and biomedical engineering.
In order to drive the actuators properly so they don't shake themselves apart, you need a control-theory engineer. Start with Fourier transforms so that you can then understand z-transforms. The learning curve on this mathematics is extremely steep, and careers are quite few, so either you have to be born to be a controls engineer or you should let someone else handle these lower-level details for you.
Signal processing, for the medium- and low-level sensor drivers, has been under the domain of the EEs historically. This works its way up to image processing, which falls under computer science, and then image understanding, which is in the A.I. branch of CS.
However, as I mentioned, the hardware, firmware, and drivers are all manufacturing details that you solve once and then sell forever. Anybody can buy a Lego or a Bioloids kit off the shelf now, and start working with motors. It's not like 2006, when the Fujitsu HOAP humanoid robot we were working with at JPL was a $50,000 custom-ordered special.
Most of what I consider the really interesting work starts by assuming the hardware and drivers have already been accomplished--and then, what do you do with the system? This is completely in the realm of software.
Robotic software control starts with 3D simulators, which in turn are based on forward kinematics; eventually inverse kinematics; dynamics, if you feel like it; and physics-engine simulations. Math here centers around locations [position + orientation], which are best represented by using [4x4] homogeneous coordinate transformation matrices. These are not very hard, and you can get a good background in them from any computer graphics textbook. Make sure you follow the religion of post-multiplying by matrices ending in a column vector on the right; this allows you to chain base-to-waist-to-shoulder-to-elbow-to-hand kinematics in a way that you'll be able to understand. Early textbooks proposed premultiplying using row vectors, because they thought it wouldn't make a difference. It does.
Of course the physics engines require a decent knowledge of physics.
Higher-level processing is accomplished using artificial intelligence, usually rules-based systems. Natural-language processing also can tie in linguistics and phonetics. Speech recognition and speech generation are again mostly signal processing, taught in EE and CS.
Recent advances work on Big Data, which uses statistics, Bayesian reasoning, and bases vector spaces (from mathematics).
Robotics has not yet broken out. It is still at the level cell phones were at when Gordon Gecko was walking on the beach talking into a "portable phone" the size of a shoe. I don't see robots becoming ubiquitous before 2020. Around 2025, being a robot programmer will be in demand as much as being an app programmer is today. Study lots of A.I. Start early.
I would highly recommend looking into Artificial Intelligence for Robotics on Udacity, it is very interesting course that covers the software and AI part. Also Coursera offers a free online robotics course, and other courses as well that are very relevant and useful to Robotics.
Industrial robotics is usually handeled by Mechanical Engineers, and sport/team robotics by electical engr, electronics engr, or computer science majors. It all depends on what you mean by "robotics". Also, in case nobody else mentions it, a Masters degree is strongly encouranged.
As an added bonus the math used in industrial robotics, is directly linked to math for game development. There isn't really a clear cut line of who is supposed to be doing what in robotics.
Mechtronics is the current field of study for those interested in robotics. It combines mechanical, electrical, controls, and software as relates to robotics.
In the past we came from many different backgrounds, mechanical engineers, electrical, electronics, and software. I am an Application Engineer for robot manufacturer. I started out in Avionics, moved to automated test equipment, then to automated material delivery systems, I became a robotics service technician and manager then moved over to application programing and training.
One final note, be prepared to keep learning. This is a field that is constantly changing and evolving.
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我是一名专业的机器人研究顾问，拥有 30 年在 SRI International 和 JPL 等组织工作的经验。
与计算机一样，机器人技术在软件和硬件。硬件进一步细分为执行器和传感器。
如果您说“我想进入计算机领域”，我会解释说，只有少数硬件工程师实际上设计和构建物理计算机 - 大多数研究人员认为硬件和固件已经构建完成，然后他们担心软件——如何使系统真正运行。
与机器人类似，构建硬件是机械工程师（设计结构和散热）的工作，而电力电气工程师（设计电机）和计算机工程师（设计固件芯片）则需要一些零碎的工作。下一代机器人还聘请工业设计师（使外部看起来漂亮，内部配合得很好）。
执行器设计的研究领域包括手指手；触手；蜂鸟和其他鸟类和昆虫的翅膀；弹性轮；腿；高辐射区域的非电子设计；和手术器械。
随着每部手机都有摄像头，视觉传感器目前基本上已经解决了问题。 传感器设计的研究领域包括智能柔性触觉皮肤、脑电波传感器和其他生物医学传感器。良好的力传感器也仍有一定的空间。这些属于材料工程、计算机工程、机械工程和生物医学工程领域。
为了正确驱动执行器，使其不会自行摇晃，您需要一名控制理论工程师。从傅里叶变换开始，这样您就可以理解 z 变换。这种数学的学习曲线非常陡峭，而且职业也很少，所以要么你必须天生成为一名控制工程师，要么你应该让其他人为你处理这些较低级别的细节。
用于中低级传感器驱动程序的信号处理历来属于EE的领域。这一直延伸到图像处理（属于计算机科学），然后是图像理解（属于计算机科学的人工智能分支）。
然而，正如我所提到的，硬件、固件和驱动程序都是制造细节，您只需解决一次即可永久出售。现在任何人都可以购买现成的乐高或 Bioloids 套件，并开始使用电机。这与 2006 年不同，当时我们在喷气推进实验室使用的富士通 HOAP 人形机器人是一款价值 50,000 美元的定制特价产品。
我认为真正有趣的工作大部分都是从假设硬件和驱动程序已经完成开始的——然后，您对系统做什么？这完全属于软件领域。
机器人软件控制从 3D 模拟器开始，而 3D 模拟器又基于正向运动学；最终实现逆运动学；动态，如果你愿意的话；和物理引擎模拟。这里的数学以位置 [位置 + 方向] 为中心，最好使用 [4x4] 齐次坐标变换矩阵来表示。这些并不是很难，您可以从任何计算机图形学教科书中获得良好的背景知识。确保您遵循以右侧列向量结尾的矩阵后乘的宗教；这使您能够以您能够理解的方式链接基础到腰部到肩膀到肘部到手的运动学。早期的教科书建议使用行向量进行预乘，因为他们认为这不会产生任何影响。确实如此。
当然，物理引擎需要良好的物理知识。
更高级别的处理是使用人工智能（通常是基于规则的系统）完成的。 自然语言处理也可以与语言学和语音学联系起来。语音识别和语音生成同样主要是信号处理，在 EE 和 CS 中教授。
大数据的最新进展使用了统计、贝叶斯推理和基础向量空间（来自数学）。
机器人技术尚未爆发。它仍然处于戈登·壁虎（Gordon Gecko）在海滩上行走时对着鞋子大小的“便携式电话”通话时手机的水平。我认为机器人在 2020 年之前不会变得无处不在。到 2025 年左右，对机器人程序员的需求将与今天对应用程序员的需求一样多。研究大量人工智能 尽早开始。
截至 2006 年最先进的人形机器人系统设计[短片]：
http://www.seqcon.com/caseJPL.html
非常高级的组件框图[形象的]：
http://www.seqcon.com/images/SystemSchematic640.gif
I'm a professional robotics research consultant, with 30 years of experience working for organizations like SRI International and JPL.
Like computers, robotics has quite a strong divide between the software and the hardware. Hardware is further subdivided into actuators and sensors.
If you'd said "I want to get into computers", I would explain that only a few hardware engineers actually design and build physical computers--most researchers assume that the hardware and firmware has been built already, and then they worry about the software--how to make the system actually work.
Similarly with robots, building the hardware is a job for the mechanical engineers (to design the structure and heat dissipation), with little bits and pieces for power electrical engineers (to spec the motors) and computer engineers (to design the firmware silicon). Next-generation robots also use industrial designers (to make the outsides look pretty, and the insides fit well together).
Research areas for actuator design include fingered hands; tentacles; hummingbird and other bird and insect wings; springy wheels; legs; non-electronic designs for high radiation areas; and surgical instruments.
With cameras in every cell phone, vision sensors are mostly a solved problem at this point. Research areas for sensor design include smart flexible tactile skin, brain wave sensors, and other biomedical sensors. There's still some room for good force sensors as well. These fall in the realms of materials engineering, computer engineering, mechanical engineering, and biomedical engineering.
In order to drive the actuators properly so they don't shake themselves apart, you need a control-theory engineer. Start with Fourier transforms so that you can then understand z-transforms. The learning curve on this mathematics is extremely steep, and careers are quite few, so either you have to be born to be a controls engineer or you should let someone else handle these lower-level details for you.
Signal processing, for the medium- and low-level sensor drivers, has been under the domain of the EEs historically. This works its way up to image processing, which falls under computer science, and then image understanding, which is in the A.I. branch of CS.
However, as I mentioned, the hardware, firmware, and drivers are all manufacturing details that you solve once and then sell forever. Anybody can buy a Lego or a Bioloids kit off the shelf now, and start working with motors. It's not like 2006, when the Fujitsu HOAP humanoid robot we were working with at JPL was a $50,000 custom-ordered special.
Most of what I consider the really interesting work starts by assuming the hardware and drivers have already been accomplished--and then, what do you do with the system? This is completely in the realm of software.
Robotic software control starts with 3D simulators, which in turn are based on forward kinematics; eventually inverse kinematics; dynamics, if you feel like it; and physics-engine simulations. Math here centers around locations [position + orientation], which are best represented by using [4x4] homogeneous coordinate transformation matrices. These are not very hard, and you can get a good background in them from any computer graphics textbook. Make sure you follow the religion of post-multiplying by matrices ending in a column vector on the right; this allows you to chain base-to-waist-to-shoulder-to-elbow-to-hand kinematics in a way that you'll be able to understand. Early textbooks proposed premultiplying using row vectors, because they thought it wouldn't make a difference. It does.
Of course the physics engines require a decent knowledge of physics.
Higher-level processing is accomplished using artificial intelligence, usually rules-based systems. Natural-language processing also can tie in linguistics and phonetics. Speech recognition and speech generation are again mostly signal processing, taught in EE and CS.
Recent advances work on Big Data, which uses statistics, Bayesian reasoning, and bases vector spaces (from mathematics).
Robotics has not yet broken out. It is still at the level cell phones were at when Gordon Gecko was walking on the beach talking into a "portable phone" the size of a shoe. I don't see robots becoming ubiquitous before 2020. Around 2025, being a robot programmer will be in demand as much as being an app programmer is today. Study lots of A.I. Start early.
State-of-the-art humanoid robot system design as of 2006 [short movie]:
http://www.seqcon.com/caseJPL.html
Very high level block diagram of components [graphic]:
http://www.seqcon.com/images/SystemSchematic640.gif
我强烈建议您在机器人人工智能 .udacity.com/" rel="nofollow">Udacity，这是一个非常有趣的课程，涵盖了软件和人工智能部分。此外，Coursera 还提供免费的在线机器人课程，以及与机器人技术非常相关且有用的其他课程。
I would highly recommend looking into Artificial Intelligence for Robotics on Udacity, it is very interesting course that covers the software and AI part. Also Coursera offers a free online robotics course, and other courses as well that are very relevant and useful to Robotics.
机电工程和计算机科学。
机械工程将为伺服系统、连杆机构、齿轮和所有其他机械部件的选择提供信息。
控制理论是机电工程的交叉学科。你会需要那个。
如今，很多控制都是数字化的，因此电子工程和计算机科学将成为其中的一部分。
这是一个很大的领域。祝你好运。
Mechanical and electrical engineering and computer science.
Mechanical engineering will inform choices about servos, linkages, gears, and all other mechanical components.
Control theory is the junction of mechanical and electrical engineering. You'll need that.
So much of control is digital these days, so EE and computer science will be a part of it.
It's a big field. Good luck.
工业机器人通常由机械工程师负责，运动/团队机器人通常由电气工程师、电子工程师或计算机科学专业的学生负责。这完全取决于你所说的“机器人”是什么意思。另外，如果没有其他人提到这一点，强烈鼓励硕士学位。
作为额外的好处，工业机器人中使用的数学与游戏开发的数学直接相关。在机器人技术中，谁应该做什么并没有真正明确的界限。
Industrial robotics is usually handeled by Mechanical Engineers, and sport/team robotics by electical engr, electronics engr, or computer science majors. It all depends on what you mean by "robotics". Also, in case nobody else mentions it, a Masters degree is strongly encouranged.
As an added bonus the math used in industrial robotics, is directly linked to math for game development. There isn't really a clear cut line of who is supposed to be doing what in robotics.
机电一体化是对机器人感兴趣的人当前的研究领域。它结合了与机器人技术相关的机械、电气、控制和软件。
过去我们来自许多不同的背景，机械工程师、电气、电子和软件工程师。我是机器人制造商的应用工程师。我从航空电子设备开始，转向自动化测试设备，然后转向自动化材料输送系统，我成为机器人服务技术人员和经理，然后转向应用程序编程和培训。
最后一点，准备好继续学习。这是一个不断变化和发展的领域。
Mechtronics is the current field of study for those interested in robotics. It combines mechanical, electrical, controls, and software as relates to robotics.
In the past we came from many different backgrounds, mechanical engineers, electrical, electronics, and software. I am an Application Engineer for robot manufacturer. I started out in Avionics, moved to automated test equipment, then to automated material delivery systems, I became a robotics service technician and manager then moved over to application programing and training.
One final note, be prepared to keep learning. This is a field that is constantly changing and evolving.