Video tutorials

In this video tutorial we take a next step in programming reliable and consistent robots and this is to learn how to stop at a second intersection. We need this because most of the time when we want to reach a mission model on a robotics competition field, the model will be located away from us and we must use all kind of technique to reach it. In this tutorial - we stop at a second intersection.

On of the most precise ways to position on the FIRST LEGO League and other competition fields is to follow and align and in this tutorial we demonstrate exactly this - how consistent and reliable this method is to reach specific mission models. In this way you know that every time you will be at the right place, which is great.

With this video tutorial we demonstration the consistency and reliability of the using an intersection to position on the field. We move forward and we stop at the first intersection. We detect the intersection with a sensor. In the whole 10 runs there isn't a single mistake and you can see the precision of the robot on each run. Check it out and experiment with the same program for your robot.

In this video tutorial we demonstrate a really important concept for FIRST LEGO League competitions - you position the robot on the field with depending on timers and rotations. The issue with moving the robot for 10 seconds and then stopping is that every time it is in a different location. Same for rotations. The wheels will slip, the battery will change, something will happen and the robot will not be in the same place every time. This is not consistent and reliable.

In this video tutorial we show how you can build robots that position on the field consistently and reliably by following and line and then aligning to a line. We also demonstrate the concept of "double align" which is quite powerful and even if there were some mistakes they will be handled.

This video tutorial contains the final 2 programs for moving straight with a LEGO Mindstorms EV3 robot. The first program is for proportional compensation that just keeps the robot orientation straight, while the second program is for Integral compensation that returns the robot to the straight line when the robot makes a mistake. 

This is one of the last tutorials in the LEGO Education SPIKE Prime programming for Competitions course and it demonstrates what we've all been waiting for - how to reach a mission model from base in a reliable and consistent way when the mission model is on the other end of the field. Naturally what we use are all the different concepts that we've learned in the course and the tutorial is more advanced than the other so if anything is not clear take a look also at the different tutorials an concepts in the course.

This is the first video tutorial from the LEGO Education SPIKE Prime programming course in which we use a sensor, and we've selected to use the motion sensor. The Motion sensor allows us to detect how the robot turns in a 3-dimensional space-time continuum, or, to put it simply, we use it to move straight with the robot. That's it. We want to move straight - we use a sensor, and the Motion Sensor is a good choice.

In this video tutorial we follow a line 10 consecutive times. In this way we demonstrate whats the consistency that you could expect from the robot when following a line with a multi-states algorithm.

On the topic of following lines with a robot there is a way to make the robot follow the line a little more precisely. This method is called "Multi-states" program. Or at least we call it like this. In this tutorial you will see how we program 5 state for the robot line following program. This could be particularly important with FIRST LEGO League competitions where we must build consistent and reliable robots.

With this 10 runs we demonstrate the consistency and reliability that could be achieved with a LEGO Education SPIKE Prime robot and a 2-states simple line following algorithm. It is not bad. Still there are some things you should consider when using this program and one of the things is the use of the timers

In this video tutorial we demonstrate the classic algorithm for following a line with robots. It is widely used in FIRST LEGO League competition and could be applied to LEGO Education SPIKE Prime and to other robots also. The algorithm follows a line by moving to the left and to the right and trying to stay at the edge of a black line.

We use a sensor so the robot must be reliable and accurate right? Wrong. The robot is not reliable and accurate only because we use sensors and that's really important during FIRST LEGO League competition. There is a reason for this and in this tutorial we demonstrate and explain how the robot behaves with a basic use of the Motion Sensor to move straight. As a result you will learn why is it important not to stop at this stage and to go to the next level with more advance use of sensors.

In this video tutorial we use the LEGO Education SPIKE Prime Motion Sensor to reach a mission model on the field. It's a basic use of the motion sensor that we will extend in the next few tutorials and in the same time we take the opportunity to demonstrate what a MyBlock is and how to develop one. As a result of this video you will learn how to make the program easier to read and maintain by organizing the blocks in the program in special MyBlocks that couple them all together.

This video tutorial reviews the EV3 Gyro Sensor and the HiTechnic Gyro Sensor for the LEGO Mindstorms NXT robotics Kit. We compare the two sensors and demonstrate them in action.

This is where the confusion really comes. We are keeping the robot orientation straight while the robot moves, but at the end the, robot is not at the fiinal location that we would like it to be. The robot is still about 2-3 centimeters away after moving for about a meter.

The first part of making the robot move straight is to keep it oriented straight. While it moves it could make an error and turn slightly to the right and then the program should turn in back to the left to make its orientation straight. In this video tutorial, we would discuss how to implement a program to keep the robot orientation straight even when we are pushing or pulling it to either side and in the same time it has different wheels.

This robot has a color sensor and this sensor is used for following lines. Additional Mindstorms EV3 sensors could be place on the robot, like a Gyro sensor or a second Color sensor.

You are not using sensors?! You are positioning the mindstorms robot only by moving forward, backwards and rotating it. That`s one of the BIGGEST mistakes teams make on the FIRST LEGO League competitions. In this video we are showing a robot with chains and how imprecise are the results are when you are not using sensors. 

Would you like to resolve all the problems with the light/color sensors that you have? And to make all of them work in a predictable, stable way even when using more than one MINDSTORMS colour sensors. 

When using Color sensors it is important to calibrate them depending on the light conditions in your venue. In this way, the calibrated sensor will show values between 0 and 100 independent of the light conditions. But using the default EV3 colour calibration available in the colour sensor block could lead to unpredicted problems that are difficult to track and resolved especially when used with multiple Color sensors. So in this series of tutorial we implement the calibration ourselves discussing the principles of colour sensor calibration.

The integral part "remembers" the errors that the robot has made in the past and we can compensate for those errors. This will make the robot return back to the line that we would like to keep it aligned. 

It's inevitable. While following this gapped line we would reach a gap. The robot must somehow understand that there is a gap and must make a decision on what to do. For detecting the gap we use the Rotation Sensor. Not the most popular, but very convenient in many cases. Check out the video.

We should multiply the error by a certain number and then add it to the steering of the LEGO Mindstorms Steering block. In this way, by changing the coefficient we change how much/fast should the proportional part influence the steering of the robot. 

In this video tutorial, we would do a few experiments with the coefficients for the Integral compensation. There are actually two coefficients - "c" and "b"

In the previous part of the video lesson we showed how imprecise a chain LEGO Mindstorms robot could be if its positioning does not rely on sensors, but only on the use of move block. In this tutorial we will show the same thing, but using robot on tires.

We keep the robot orientation straight while moving, but when we stop the robot could be in a different orientation. This applies for both using the Mindstorms Gyro Sensor when moving straight or the Mindstorms Color sensor when following a line. In this video tutorial, we will do a few examples of when an how this could happen.

This is the final run for our World Robotics Olympiad (WRO) 2015 Elementary Challenge Robot. In "dives", detects the color of the pearl and then counts the number of Ping-Pong balls to release.