The reason why you would want to watch this tutorial is because it demonstrates one of the most reliable ways to know where you are on the field and to accomplish missions successfully and this is aligning to a line. In the tutorial you will also see how a parallel program is developed and who parallel programs could be used in a meaningful way.
In this video tutorial we run the robot for 10 times and we demonstrate the consistency of the robot behavior with our advanced algorithm for smooth line following. What you will get from it is an understanding and demonstration of what can you expect from the robot.
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.
Use the color sensor to count the lines and stop on the third line. We do not use the wait block for this.
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.
Counting lines and stopping on the third is the subject of this video. It is important to know how to do this in order to conduct more than one experiment in STEM classes (if we consider that each line is an experiment)
The program from part 3 should be refactored and improved to make it easier to understand and support. We extract most of the repeatable behaviours in a loop and this reduces the size of the program three times in terms of the number of blocks used.
In this video we are showing how to use two light sensors to do basic aligning on a black line. This is always needed at the competitions. We also show an example for following a line with two sensors.
The color sensors supports different modes of working. In this video we are working with the Reflected light, which is not actually the detected color. Most robotics sensors actually work with reflected light and you should definitely learn how to use this mode.
In this video we are solving one of the most important challenges on the FIRST LEGO League competition, i.e how to cover your Mindstorms NXT light sensors so that they are not affected by outer light sources at the day of the competition. Once again you would find 3 solutions and more will come in the next videos.
We list the number of decisions that the robot is making while following the line. Then, we group them and decide on the number of sensors to be used.
In this video lesson I will show you how to follow a black line using just one sensor, but fast enough so that you could use it during any robotics competition without wasting any time.
We follow a line. We start from the Smooth Proportional Line Following program and modify it a little for this program. We follow the line with the middle sensor attached on port 2.
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.
How do you detect a cross-section and move from following the main line to following the crossing line. In this series of video tutorials we are starting with a very simple solution that could work in most of the cases. It is especially useful for the FIRST LEGO League Trash Trek competition where there is such a section.
Tasks on using the LEGO EV3 Mindstorms Color sensor. Quite fun and useful for different STEM classes or just to get to know the sensor.
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.
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.
In this video tutorial I give a step-by-step explanation of how to implement a block for aligning to a line. The block was first used in lesson 28. Aligning to lines is probably the most powerfull way to know the position of the robot on the FLL Competition field and to be able to execute the missions precisely at 100% of the time.
We've detected the gap. It's time to move over it. This is difficult because we have to detect where the line is after the 0.1 meters gap on the line following field.
"What is the light in the room?" - should it even matter. You can use one program for all lighting conditions by calibrating the color/light sensor of the LEGO Mindstorms EV3/NXT robots.
Next important state is Turn Right with our robot. This happens when we detect a line on the right.
This second part continues with importing two previously developed in Episode 53 blocks into our program. With them we can for align to lines. We program the robot to align to the cross line and start following it.
Next state in our state machine programming pattern is the "Turn Left" state and the corresponding behaviour.
We can Turn Right. We can Turn Left. How do we decide which way to go if there are lines both to the left and to the right. Check out the video.