HOWTO create a Line Following Robot using Mindstorms. The easy way of learning how to built your own Line follower Robot using a PID Controller without any of the mathematical expressions that you will find on most tutorials. Code included The first thing you have thought about after unboxing your LEGO Mindstorms was building the first robot, and just after that you would love to make it follow lines, isnt it Line following is one of the most common problems on industrial robots, and it is one of the most useful applications because it allows the robot to move from one point to another to do tasks. Lego Nxt Balancing Robot Program' title='Lego Nxt Balancing Robot Program' />There are several ways of making a Line Follower, the one that I am going to explain you about is using the Light sensor. As you know, both Mindstorms and EV3 sets come with a little light sensor that it is able to get a reading of reflected light, apart of seeing colors. On this tutorial I will explain how to do line following with just one sensor. The more sensors you have the better and faster the robot will be able to follow it. Building the line follower robot. A PID Controller For Lego Mindstorms Robots. A PID Controller is a common technique used to control a wide variety of machinery including vehicles, robots and even. Opensource robotics OSR is where the physical artifacts of the subject are offered by the open design movement. This branch of robotics makes use of opensource. Now that you have either chosen or built a frame, the next step is to choose the right propulsion system. A complete propulsion system includes motors, propellers. BRQKCwd4/hqdefault.jpg' alt='Lego Nxt Balancing Robot Program' title='Lego Nxt Balancing Robot Program' />So first thing is build yourself a little robot much like Track. You can download the instructions provided by LEGO. It is a simple construction. Or base it on one of the LEGO Education modelsWhatever you build, just try to keep the distance between wheels to a minimum because the bigger the distance, the harder for the robot to follow the turns on the line. Ok, ready Time to code. In this article, you can explore fascinating LEGO projects like rovers, robot arms, drones, mobile caterpillar, and more. This tutorial will show you how to use and modify a standard NXTG program to create a working LEGO NXT Segway balancing robot that you can customize as you like. I. Theres nothing unique about loving Lego. Millions of people wax nostalgic when they see those colorful bricks. Millions more never stopped building. Ive always. The LEGO Movie, which was originally named LEGO The Piece of Resistance and then later was. Obstacle-Avoidance-3.bmp' alt='Lego Nxt Balancing Robot Program' title='Lego Nxt Balancing Robot Program' />References to the coming robot revolution, killer droids, malicious AIs and human extermination abounded, some more or less serious than others. It seems reasonable to expect that when you program a robot to drive straight, it should just work. And, of course, this is what the Move Steering block in the EV3. Let me explain how we get to the best solution with a serie of intermediate steps that will help you understand it better. Building your playground for line following. Ok, the robot is done. But before start coding, we need the line that the robot will follow. If you happen to have the Mindstorms NXT 2. But if you dont just do like me. I have used a black tape and with the finger I have sticked it to the floor creating a continuous path that the robot will follow. You dont need a closed loop although it is a good idea to do it in that way. BALANC3R_small-385x600.jpg' alt='Lego Nxt Balancing Robot Program' title='Lego Nxt Balancing Robot Program' />My floor is done of marble that it is white and brown at times and even with that it works. So it may work too on yours unless it has even less contrast than mine. Line Following Problem definition. It is quite important to understand the line following problem first. So lets describe the problem. We have a thick black line on a white surface and we want our robot to move along the line following it in the fastest possible way. Right Well, first thing that we need to understand is that we dont want to follow the line wtf No, serious, we dont follow the line but its border in what it is called left hand approach. We want to follow the line where there is a 5. So, next step is defining what it is black and what it is white. I hope you have a clear idea of what these two colors are, but unfortunatelly your robot dont. So the best thing you can do, before starting anything else is calibrate the robot. Light Sensor calibration. Ok, as you know Color sensor can also work as a Light sensor, so we choose the Measure reflected light mode and we are going to store in two variables the white and black colors. The reflected light value is just a number between 0 and 1. So the pseudocode would be. CALIBRATE. print WHITE. Wait for Touch Sensor to change. Read Light Sensor. BLACK. Wait for Touch Sensor to change. Read Light Sensor. Do you get the idea We add a Touch sensor to our Robot to record the light value, you can also do it using Brick buttons, as you prefer. Here is the EV3 code that I used for it. So the idea is that you place it on the white surface, press the touch sensor, place it now on the black surface and press the touch sensor again, now we have the white and black readings and can start working. I do it each time I start the robot but you can safely ignore it while light conditions keep stable. Line Following with OnOff Controller. Ok, we have the robot, we have the calibration data lets go code. Maaaaaaah. HH Bad Lets think first what we are going to do. Lets start with the simplest possible way and perhaps the worse of doing line following. We place the robot on the line, we get a reading if it below the middle black white we move to one side and if it is above we move to the other side. Simple Clever Lets see again the pseudo code. LINE FOLLOWING. white 0, black 0. Read Light Sensor. B set power 5. 0. C set power 2. 5. B set power 2. 5. C set power 5. 0. The idea is pretty simple just make one wheel turns faster than the other. Here it is how it works. Here is the EV3 program You can download all the source codes on the bottom of the page Does it works Well. If the corner is step enough the robot will miss it and as you can see it is missing the straight line and it starts oscillating around it. Why Because we have only two states, so the robot is either turning left or turning right. What can we do Exactly. So we have left, straight and right. So why not make the turn proportional to the error, the difference between the midpoint and the value read. Line Following with a P Controller. Do you like MathsI dont. I have a deep understanding of them but I really cant stand the complex way of explaining simple things. P Controller without any kind of mathematical notations. If you follow the reasoning of the OnOff controller with several states you may end up thinking of a controller with infinite states, each one of them with an value proportional to the error, the difference between where we want the robot to be and where it really is. If you want something to be proportional to a quantity you just multiply both factors so we have. Kp midpoint Light Sensor Reading. Where K is a constant that helps us tune the P controller. The bigger Kp is the higher will be the turning when there is an error, but if Kp is too big you may find that the robot overreacts and that it is uncontrollable. You can watch what happens when you change the value of Kp from 0 to 1. So start with 1. So our P Controller would be like this pseudo code. LINE FOLLOWING. white 0, black 0. Read Light Sensor. Turn BC Motors by correction. If you are not using the EV3 module just move one motor a value correction and the other motor to value correction. It is pretty much the same. Here it is the EV3 program You can download all the source codes on the bottom of the page Tunning the Kp parameter. Start with Kp1 and run your robot. If the robot cant follow the turns on the line then increase Kp, if on the other hand your robot turns violently on the line, decrease the Kp value. The bigger the effect the bigger the change need on Kp. Just keep playing. The P Controller has two problem that can be easily fixed with a complete PID controller one is that it will oscilate after corrections and the other is that there is a small error that it is a direct result of the P Controller. So lets see the complete PID controller. Line Following with a PID Controller. A PID controller is basically a P controller with two other components, the integral part and the derivative part. Dont be scared. Let me explain the overall idea and we move to the pseudo code. Proportional part of the PID Controller. This is exactly the part that have just seen above. Integral part of the PID Controller. The idea behind the integral part is that the error must tend to zero over a period of time. I will avoid you a very ugly mathematical expression explaining this. So over a number of interation we want this to be zero. No, Facebook Did Not Panic and Shut Down an AI Program That Was Getting Dangerously Smart. In recent weeks, a story about experimental Facebook machine learning research has been circulating with increasingly panicky, Skynet esque headlines. Facebook engineers panic, pull plug on AI after bots develop their own language, one site wrote. Facebook shuts down down AI after it invents its own creepy language, another added. Did we humans just create Frankenstein asked yet another. One British tabloid quoted a robotics professor saying the incident showed the dangers of deferring to artificial intelligence and could be lethal if similar tech was injected into military robots. References to the coming robot revolution, killer droids, malicious AIs and human extermination abounded, some more or less serious than others. Continually quoted was this passage, in which two Facebook chat bots had learned to talk to each other in what is admittedly a pretty creepy way. Bob I can i i everything else. Alice balls have zero to me to me to me to me to me to me to me to me to Bob you i everything else. Alice balls have a ball to me to me to me to me to me to me to me to me. The reality is somewhat more prosaic. A few weeks ago, Fast. Co Design did report on a Facebook effort to develop a generative adversarial network for the purpose of developing negotiation software. The two bots quoted in the above passage were designed, as explained in a Facebook Artificial Intelligence Research unit blog post in June, for the purpose of showing it is possible for dialog agents with differing goals implemented as end to end trained neural networks to engage in start to finish negotiations with other bots or people while arriving at common decisions or outcomes. The bots were never doing anything more nefarious than discussing with each other how to split an array of given items represented in the user interface as innocuous objects like books, hats, and balls into a mutually agreeable split. The intent was to develop a chatbot which could learn from human interaction to negotiate deals with an end user so fluently said user would not realize they are talking with a robot, which FAIR said was a success The performance of FAIRs best negotiation agent, which makes use of reinforcement learning and dialog rollouts, matched that of human negotiators. FAIRs bots not only can speak English but also think intelligently about what to say. When Facebook directed two of these semi intelligent bots to talk to each other, Fast. I Say A Little Prayer For You Midi File more. Co reported, the programmers realized they had made an error by not incentivizing the chatbots to communicate according to human comprehensible rules of the English language. In their attempts to learn from each other, the bots thus began chatting back and forth in a derived shorthandbut while it might look creepy, thats all it was. Agents will drift off understandable language and invent codewords for themselves, FAIR visiting researcher Dhruv Batra said. Like if I say the five times, you interpret that to mean I want five copies of this item. This isnt so different from the way communities of humans create shorthands. Facebook did indeed shut down the conversation, but not because they were panicked they had untethered a potential Skynet. FAIR researcher Mike Lewis told Fast. Co they had simply decided our interest was having bots who could talk to people, not efficiently to each other, and thus opted to require them to write to each other legibly. But in a game of content telephone not all that different from what the chat bots were doing, this story evolved from a measured look at the potential short term implications of machine learning technology to thinly veiled doomsaying. There are probably good reasons not to let intelligent machines develop their own language which humans would not be able to meaningfully understandbut again, this is a relatively mundane phenomena which arises when you take two machine learning devices and let them learn off each other. Its worth noting that when the bots shorthand is explained, the resulting conversation was both understandable and not nearly as creepy as it seemed before. As Fast. Co noted, its possible this kind of machine learning could allow smart devices or systems to communicate with each other more efficiently. Those gains might come with some problemsimagine how difficult it might be to debug such a system that goes wrongbut it is quite different from unleashing machine intelligence from human control. In this case, the only thing the chatbots were capable of doing was coming up with a more efficient way to trade each others balls. There are good uses of machine learning technology, like improved medical diagnostics, and potentially very bad ones, like riot prediction software police could use to justify cracking down on protests. All of them are essentially ways to compile and analyze large amounts of data, and so far the risks mainly have to do with how humans choose to distribute and wield that power. Hopefully humans will also be smart enough not to plug experimental machine learning programs into something very dangerous, like an army of laser toting androids or a nuclear reactor. But if someone does and a disaster ensues, it would be the result of human negligence and stupidity, not because the robots had a philosophical revelation about how bad humans are. At least not yet. Machine learning is nowhere close to true AI, just humanitys initial fumbling with the technology. If anyone should be panicking about this news in 2.