A brief description of the method for finding the inverse kinematics equations of a robot
A brief description of the method for finding the inverse kinematics equations of a robot is to determine An, which is a chi-square coordinate transformation of the joint coordinates, based on the joint coordinate settings of the manipulator, as determined by the joint variables and parameters. As follows:
Inverse kinematics content: inverse kinematics is the process of determining the parameters of the joint movable objects to be set to achieve the desired pose. For example, given a 3D model of a human body, how do you set the angles of the wrist and elbow so that the hand goes from a relaxed position to a waving position?
This question is critical in robotics because manipulating a robotic arm is controlled through joint angles. Inverse kinematics is also important in game programming and 3D modeling, although its importance is diminished by the increasing use of large databases of motion capture data.
A jointed object consists of a set of rigid segments connected by joints. Varying the angles of the joints can produce an infinite number of shapes. The solution to the forward kinematics problem is the pose of the object given these angles. A more difficult solution is to find the angles of the joints given the pose of the object, e.g., given the position of the end-effector.
In general, inverse kinematics problems do not have analytic solutions. However, inverse kinematics can be solved by nonlinear programming techniques. Specific special kinematic chains – those with spherical wrists – allow motion decoupling. This allows us to treat the orientation and position of the end effector independently and leads to an efficient closed-form solution.
Geometric Foundations of Robotics:
Most textbooks dealing with this field cover a wide variety of topics on the kinematics, dynamics, control, perception, and planning of robot manipulator arms. What sets this book apart, however, is that it introduces the mathematical tools, especially geometric tools, used to solve problems in robotics. In particular, it is geared toward readers interested in robotics and introduces Lie groups and the algebraic and geometric concepts associated with them in an accessible way.
Geometric Foundations of Robotics is a fascinating introduction to geometric concepts that have important applications in robotics. The 2nd edition provides in-depth coverage of the relevant areas and remains unique: rather than focusing on computational results in kinematics and robotics, it contains extremely important and up-to-date material that reflects important advances in the field and connects robotics to the mathematical foundations in group theory and geometry.
Does the discipline of robotics involve chemical biology?
Robots may be exposed to a variety of environments, hot, cold, wet, corrosive, exploring, rescuing, etc., and in order for a robot to be able to adapt to a variety of environments, it is necessary to consider the variety of situations that may be encountered in order to be able to function as a robot.
How to learn robotics systematically
Author: Zheng Fan
Copyright © 2012 by the author, reproduced please contact the author for authorization.
1. Introduction to the basics
The textbooks are pretty much the same, and the two commonly used ones are recommended:
Craig: Introduction to Robotics (Douban)
Cai Zixin: Robotics (Douban)
In conjunction with the textbooks, you can look at Stanford’s Open Class: Stanford Open Class: Robotics
The above, help in create a general picture and basic concepts of robotics in your mind. Of course, you don’t have to watch all of them, but in fact a set of them is sufficient for a serious study. Usually the basic discussion of robotics are based on the robot arm, need to understand a few issues: the spatial description of the robot arm and coordinate transformation; robot arm kinematics; robot arm inverse kinematics; robot arm dynamics; trajectory planning; control of the robot arm; and others such as mechanical design, sensors, image processing etc.
Basic content, I personally believe that the most important must master a few concepts:
①Rigid body position of the coordinate description and transformation: the basis of the robot model, the importance of robotics in the English alphabet to the English language;
②D-H coordinate transformation: an important method of robotic arm modeling, a simple mathematical language to describe the robotic arm composed of a series of rigid bodies;
③Jacobi matrix: the core of the robotic arm kinematics, used for the conversion of joint speeds and terminal velocity conversion;
④ Lagrangian dynamics: a bridge for conversion between force and velocity acceleration.
Most important tool: mathematics, especially linear algebra.
2. Basic Hands-On Introduction
Engineering is not hands-on, nor is it learned. If you think the above basic content is boring (in fact, it is indeed very boring), do not put their own hands to increase the interest.
Software, you can use the almighty matlab. in fact, Clegg’s “Introduction to Robotics” has a lot of matlab exercises that you can refer to. Of course the roboticstoolboxformatlab written by PerterCorke has to be mentioned here:http://petercorke.com/Robotics_Toolbox.html.
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Hardware, personal DIY robots, then the cost is very high, positioning for the science and education function of the nao robot (this famous budding goods see the following picture, why I’m going to post this irrelevant picture because it is too cute), one on the sale of more than 100,000 it.
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BUT It’s also feasible for a student party to personally DIY a relatively rudimentary robotic arm. Buy a few tens of dollars of motors, although the precision is low, can turn up on the line. Buy a few control boards. If you circuit enough awesome, you can also design their own circuit drawing circuit diagrams sent to the processing and then welded, but always still buy ready-made boards convenient Well. For beginners, the control board can choose the student party most commonly used microcontroller, here I recommend their own messed up open source project arino: Arino-HomePage
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The good thing about arino is that the programming syntax is simple, as long as you can understand the basic C language can be, almost zero entry; editor comes with a lot of sample can be referred to; programming template versatility, a lot of times programming only need to change the template design to achieve the functionality of the statement can be; as an open source project, google to find a lot of foreign strong people to do the wildly cool DIY projects, such as: DIYRoboticHandControlledbyaGloveandArino Many DIY people are willing to make the program public, can be used for reference; there is, the price is not expensive.
Whether you buy a motor or a control board, you can turn to the almighty Taobao. A simple robotic arm to build up, a few hundred dollars enough.
Paste a I use arino board and simple motor blindly pouring mechanical arm:
<imgsrc=”https://pic3.mg.com/6ebe0575e365054c2743eadaefc9837e_b.jpg “data-rawwidth=”2000 ” data-rawheight=”3552 “class=”origin_imagezh-lightbox-thumb “width=”2000 “data-original=”https://pic3.mg.com/ 6ebe0575e365054c2743eadaefc9837e_r.jpg”> use it to draw lines on paper, because of the low precision, so the straight line shakes into that kind of frustration (shy >. <). Use it to draw lines on paper, because of the low precision, so the straight line shakes into that kind of frustration (shyness>. <).
Advanced can not be told in the knowledge. First of all, there are too many directions, after learning the basics, what you want to do (industrial robotic arms, motion humanoid robots, etc.), which part you want to do (mechanical design, circuitry, image processing, control algorithms, gait planning, etc.). Again, the cost is too high, you know. Finally, advanced robotics DIY is not necessary, high financial cost is one thing, even more so is the high cost of time and effort. If you don’t take robotics as your specialty, then up to stage 1 or 2, you can just play around by yourself. If one aspires to do robotics related research or work, then participating in a related research program in college is sufficient.
The concept of artificial intelligence was first introduced at the Dartmouth Conference in 1956
The concept of artificial intelligence was first introduced at the Dartmouth Conference in 1956. This sentence is correct.
In August 1956, in the quiet Dartmouth College in the small American town of Hanos, John McCarthy, Marvin Minsky (an expert in artificial intelligence and cognition), Claude Shannon ( ClaudeShannon, founder of information theory).
Scientists such as Allen Newell, a computer scientist, and Herbert Simon, a Nobel Prize-winning economist, are gathering to discuss a completely unearthly topic: using machines to mimic human learning and other aspects of intelligence.
The conference went on for two months, and while there was no general consensus, a name was given to what was being discussed: artificial intelligence. Thus, 1956 became the Year of Artificial Intelligence.
Basic content to be studied for artificial intelligence:
1. Cognition and neuroscience: specifically, courses in cognitive psychology, foundations of neuroscience, human memory and learning, language and thinking, and computational neural engineering.
2. Ethics of Artificial Intelligence: specifically including courses on Artificial Intelligence, Society and Humanities, Philosophical Foundations of Artificial Intelligence and Ethics.
3, Science and Engineering: requires the cooperation of brain science, neuroscience, cognitive psychology, information science and other related disciplines.
4. Advanced Robotics: specifically, courses in advanced robot control, cognitive robotics, robot planning and learning, and bionic robotics.
5, Artificial Intelligence Platforms and Tools: specifically including group intelligence and autonomous systems, unmanned technology and system implementation, game design and development, computer graphics, virtual reality and augmented reality and other courses.
6, Artificial Intelligence Core: Specifically, it includes courses such as Modern Approaches to Artificial Intelligence, Problem Representation and Solving, Modern Approaches to Artificial Intelligence, Machine Learning, Natural Language Processing, and Computer Vision.