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do () motion () times at () speed

do () motion () times at () speed

Description

The block performs the selected motion for the quadruped. The motion runs for the specified times and at the specified speed.

Available Motions:

Available Speeds;

If you want to perform the motion at a different speed, then you can use a variable to define the speed.

Example

Dance Motion With Humonoid

Introduction

Dance motion with humanoid refers to using a robot that has a human-like appearance to perform dance movements. These robots are pre-programmed with various dance sequences and can also be customized to create unique dance routines.

To make the robot move, we need to use code to control its motors and servos. The code can be created using a programming tools/language such Pictoblox, Python, or Arduino. The code tells the robot which movements to make, such as lifting its arms, bending its knees, or spinning around.

Different actions can be used to create different dance moves, and the dance can be accompanied by music or sound effects. The robot can also be programmed to display different colors or patterns on its body as it moves.

Humanoid robots is a fun and creative way to explore the intersection between technology and the arts.

Code

Logic

Here, we use the pre-defined dance and sequence of humanoid in our code.
To begin, we first initialize the humanoid extension and set up all the required pins by dragging and dropping the necessary blocks.
We use a forever loop to continuously play the dance sequence along with different sounds and display matrices.
To make the dance sequence more interesting, we use different actions with the ‘do() action()() times() speed’ block. It’s quite fascinating.
You can even try out your own dance movements by using different actions and adding your own creativity.

Output

Quadruped Predefined Motions

Learn how to use PictoBlox to control the predefined motions of the Quadruped robot.

Introduction

In this project, we will explain how to run predefined motions in PictoBlox for the Quadruped. The predefined motions allow users to make the robot move forward, backward, left, right, and much more.

Quadruped Motions

The are eight predefined motions for Quarky in PictoBlox which can be accessed through do () motion () times at () speed block. Using the do () motion () times at () speed block, we can control the number of times the motion has to be executed.

Testing Code

Click on the green flag to run the motion sequence.

Custome Speed Controls

We can also control the speed of the motion.

If you want to perform the motion at a different speed, then you can use a variable to define the speed.

Output

We can program a quadruped robot to move in predefined motions, such as forward, backward, left, and right

Code

Logic

To initialize the quadruped extension, we need to set the pins for the FR Hip, FL Hip, FR Leg, FL Leg, BR Hip, BL Hip, BR Leg, and BL Leg blocks.
To keep the program running infinitely, we can use the forever block.
To execute predefined motions with specific speeds, we can use the do () motion () times at () speed block.
To wait for a specific amount of time, we can use the wait () seconds block.

Output

Wirelessly Controlled Quadruped

In this tutorial, you will learn how to control a quadruped robot using the arrow key program.

Introduction

In this example, we will make the computer program that controls a “quadruped” (a four-legged robot). It’s like a remote control car, except with four legs instead of four wheels. You can press different keys on the keyboard to make the quadruped move forward, backward, turn left and turn right.

Logic

The Quadruped will move according to the following logic:

32Quadruped will move forward when the “UP” key is pressed.
Quadruped will move backward when the “DOWN” key is pressed.
Quadruped will turn left when the “LEFT” key is pressed.
When the “RIGHT” key is pressed – Quadruped will turn right.

Code

The program uses the up, down, left, and right arrows to control the robot and make it move forward, backward, left, and right. Every time you press one of the arrows, Quarky will move in the direction you choose for 1000 steps.

Output

Face Detection with Humanoid Robots

Incorporate a fun activity into your artificial intelligence learning journey by using Humanoid robots to learn about face detection.

Introduction

As we start learning artificial intelligence, let’s make it more engaging by incorporating a fun activity. One of the most popular topics in AI is face detection, and we can make it even more exciting by learning it with the help of Humanoid robots. Are you interested in learning it together?

Code

Logic

Simply drag and drop the RHip(), LHip(), RFoot(), LFoot(), RHand(), LHand() block from the Humanoid extension.
Start the program by initializing the sprite and face detection library parameters.
Use the forever loop block to create a continuous loop.
If the camera detects more than one face, the Humanoid will move forward with a specific time, speed, and dance move with do() motion() times at () speed() block.
If no face is detected, the Humanoid will move backward at a specific time and speed using do() motion() times at () speed() block.

Output

Face Tracking Controlled Quadruped

Learn about face-tracking, and how to code a face-tracking Quadruped robot using sensors and computer vision techniques.

Introduction

A face-tracking robot is a type of robot that uses sensors and algorithms to detect and track human faces in real-time. The robot’s sensors, such as cameras or infrared sensors, capture images or videos of the surrounding environment and use computer vision techniques to analyze the data and identify human faces.
Face-tracking robots have many potential applications, including in security systems, entertainment, and personal robotics. For example, a face-tracking robot could be used in a museum or amusement park to interact with visitors, or in a home as a companion robot that can recognize and follow the faces of family members.

One of the most fascinating activities is face tracking, in which the Quadruped can detect a face and move its head in the same direction as yours. How intriguing it sounds, so let’s get started with the coding for a face-tracking Quadruped robot.

Logic

If the face is tracked at the center of the stage, the Quadruped should be straight.
As the face moves to the left side, the Quadruped will also move to the left side.
As the face moves to the right side, the Quadruped will also move to the right side.

Code Explain

Drag and drop the when green flag clicked block from the Events palette.
Then, add a turn () video on stage with () % transparency block from the Face Detection extension and select one from the drop-down. This will turn on the camera.
Add the set pins FR Hip () FL Hip () FR Leg () FL Leg() BR Hip () BL Hip () BR Leg () BL Leg () block from the Humanoid extension.
Click on the green flag and your camera should start. Make sure this part is working before moving further.
Add the forever block below turn () video on stage with () % transparency from the Control palette.
Inside the forever block, add an analyzed image from the () block. This block will analyze the face the camera detects. Select the camera from the dropdown.
Create a variable called Angle that will track the angle of the face. Based on the angle, the robot will move to adjust its position.
Here comes the logical part as in this, the position of the face on the stage matters a lot. Keeping that in mind, we will add the division () / () block from the Operator palette into the scripting area.
Place get () of the face () at the first place of addition () + (), and 3 at the second place. From the dropdown select X position.
If the angle value is greater than 90, the Humanoid will move left at a specific speed. If the angle is less than 90, the Humanoid will move right at a specific speed. If the angle is exactly 90, the Humanoid will return to its home position.

Block Explained

Create a variable called Angle and assign it the value of the face’s position.
At the center of the stage, we will get the X position value which is zero.
As we move to the left side the X position value will give you the negative value and as we move to the right side the X position value will give you the positive value.
The x position value is divided by 3 which gives precise positioning.
To set the angle at 90 when the face is at the center of the stage we have added 90 to the X position value.
As we move to the left side the angle value will get decreased as the X position value is going in negative.
As we move to the right side the angle value will get increased as the X position value is going in positive.

Code

Output

Our next step is to check whether it is working right or not. Whenever your face will come in front of the camera, it should detect it and as you move to the right or left, the head of your Quadruped robot should also move accordingly.

Hand Gestures Controlled Quadruped with Machine Learning

Explore the power of machine learning in recognizing hand gestures and controlling the movements of a Quadruped robot.

Introduction

This project demonstrates how to use Machine Learning Environment to make a machine–learning model that identifies the hand gestures and makes the Quadruped move accordingly. learning model that identifies the hand gestures and makes the qudruped move accordingly.

We are going to use the Hand Classifier of the Machine Learning Environment. The model works by analyzing your hand position with the help of 21 data points.

Hand Gesture Classifier Workflow

Follow the steps below:

Open PictoBlox and create a new file.
You can click on “Machine Learning Environment” to open it.
Click on “Create New Project“.
A window will open. Type in a project name of your choice and select the “Hand Gesture Classifier” extension. Click the “Create Project” button to open the Hand Pose Classifier window.
You shall see the Classifier workflow with two classes already made for you. Your environment is all set. Now it’s time to upload the data.

Class in Hand Gesture Classifier

There are 2 things that you have to provide in a class:

Class Name: It’s the name to which the class will be referred as.
Hand Pose Data: This data can either be taken from the webcam or by uploading from local storage.

Note: You can add more classes to the projects using the Add Class button.

Adding Data to Class

You can perform the following operations to manipulate the data into a class.

Naming the Class: You can rename the class by clicking on the edit button.
Adding Data to the Class: You can add the data using the Webcam or by Uploading the files from the local folder.
1. Webcam:

Note: You must add at least 20 samples to each of your classes for your model to train. More samples will lead to better results.

We are going to use the Hand Classifier of the Machine Learning Environment.

Training the Model

After data is added, it’s fit to be used in model training. In order to do this, we have to train the model. By training the model, we extract meaningful information from the hand pose, and that in turn updates the weights. Once these weights are saved, we can use our model to make predictions on data previously unseen.

The accuracy of the model should increase over time. The x-axis of the graph shows the epochs, and the y-axis represents the accuracy at the corresponding epoch. Remember, the higher the reading in the accuracy graph, the better the model. The range of the accuracy is 0 to 1.

Testing the Model

Hand Pose Classifier

The model will return the probability of the input belonging to the classes.You will have the following output coming from the model.

Logic

The Quadruped will move according to the following logic:

When the forward gesture is detected – Quadruped will move forward.
When the backward gesture is detected – Quadruped will move backward.
When the left gesture is detected – Quadruped will turn left.
When the right gesture is detected – Quadruped will turn right.

Code

Output

Sign Detection with Quadruped

Learn how to set the bounding box threshold, and detect signals such as 'Go', 'TurnRight', 'TurnLeft', and 'Stop' to control quadruped movements.

Introduction

A sign detector Quadruped robot is a robot that can recognize and interpret certain signs or signals, such as hand gestures or verbal commands, given by a human. The robot uses sensors, cameras, and machine learning algorithms to detect and understand the sign, and then performs a corresponding action based on the signal detected.

These robots are often used in manufacturing, healthcare, and customer service industries to assist with tasks that require human-like interaction and decision-making.

Code

Logic

Then, it sets up the quadruped robot’s camera to look for hand signs and tells it how to recognize different signs.
Next, the code starts a loop where the robot looks for hand signs. If it sees a sign, it says the name of the sign out loud.
Finally, if the robot sees certain signs (like ‘Go’, ‘Turn Left’, ‘Turn Right’, or ‘U Turn’), it moves in a certain direction (forward, backward, left, or backward) based on the sign it sees.
So, this code helps a robot understand hand signs and move in response to them!

Output

Quadruped Obstacle Avoidance Project with Ultrasonic Sensor

Explore the functionality of our obstacle avoidance robot equipped with an ultrasonic sensor. Discover how it intelligently detects obstacles.

Intorduction

This project of obstacle avoidance is for a robot that will move around and look for obstacles. It uses an ultrasonic sensor to measure the distance. If the distance is less than 20 cm, it will stop and look in both directions to see if it can move forward. If it can, it will turn left or right. If not, it will make a U-turn. The robot will also light up an LED display to show where it is going.

Logic

This code is making a robot move around and explore its surroundings. It has an ultrasonic sensor that can measure the distance between objects.

First, it checks if the distance measured by the sensor is less than 20 cm.
If it is, it draws a stop sign pattern on the LED display and makes the robot stop and look straight. Then it looks left and checks if the distance is greater than 40 cm. If it is, it draws a left arrow pattern on the LED display and makes the robot turn left.
If not, it looks right and checks if the distance is greater than 40 cm. If it is, it draws a right arrow pattern on the LED display and makes the robot turn right.
If not, it draws a U arrow pattern on the LED display and makes the robot make a U-turn.
If the distance measured by the ultrasonic sensor is not less than 20 cm, the code will make the robot move forward.

Code

Upload the code to Quarky and test it.

Output

Sound Based Quadruped with Machine Learning

Learn how to create custom sounds to control Quadruped with the Audio Classifier of the Machine Learning Environment in PictoBlox.

Introduction

A Sound-Based Quadruped with Machine Learning refers to a Quadruped robot that can perceive and interact with its environment through sound-based sensing and uses machine-learning techniques to process and analyze the auditory data it receives.
Quadruped robots with machine learning have the potential to greatly enhance the way we interact with machines and each other, making communication more natural and intuitive while also enabling new applications in fields such as healthcare, education, and entertainment.
In this activity, we will use the Machine Learning Environment of the Pictoblox Software. We will use the Audio Classifier of the Machine Learning Environment and create our custom sounds to control the Quadruped.

Audio Classifier Workflow

Follow the steps below to create your own Audio Classifier Model:

Open PictoBlox and create a new file.
Select the Block coding environment as the appropriate Coding Environment.
Select the “Open ML Environment” option under the “Files” tab to access the ML Environment.
A new window will open. Type in an appropriate project name of your choice and select the “Audio Classifier” extension. Click the “Create Project” button to open the Audio Classifier Window.
You shall see the Classifier workflow with two classes already made for you. Your environment is all set. Now it’s time to upload the data.
As you can observe in the above image, we will add two classes for audio. We will be able to add audio samples with the help of the microphone. Rename class 1 as “Clap” and class 2 as “Snap”.

Note: You can add more classes to the projects using the Add Class button.

Adding Data to Class

You can perform the following operations to manipulate the data into a class.

Naming the Class: You can rename the class by clicking on the edit button.
Adding Data to the Class: You can add the data using the Microphone.
You will be able to add the audio sample in each class and make sure you add at least 20 samples for the model to run with good accuracy.
Add the first class as “clap” and record the audio for clap noises through the microphone.
Add the second class as “snap” and record the audio for snap noises through the microphone.

Note: You will only be able to change the class name in the starting before adding any audio samples. You will not be able to change the class name after adding the audio samples in the respective class.

Training the Model

After data is added, it’s fit to be used in model training. To do this, we have to train the model. By training the model, we extract meaningful information from the hand pose, and that in turn updates the weights. Once these weights are saved, we can use our model to make predictions on data previously unseen.

Testing the Model

To test the model simply, use the microphone directly and check the classes as shown in the below image:

You will be able to test the difference in audio samples recorded from the microphone as shown below:

Export in Block Coding

Click on the “Export Model” button on the top right of the Testing box, and PictoBlox will load your model into the Block Coding Environment if you have opened the ML Environment in the Block Coding.

The Quadruped will move according to the following logic:

When the audio is identified as “clap” sound– Quadruped will move forward.
When the “snap” sound is detected –Quadruped will move backward.

Note: You can add even more classes with different types of differentiating sounds to customize your control. This is just a small example from which you can build your own Sound Based Controlled Quadruped in a very easy stepwise procedure.

Code

Logic

First, initialize the Quadruped extension.
Then, initialize a forever loop to continuously loop and analyze the camera from the stage.
If the program detects a clap sound, the Quadruped will move forward at a specific speed.
Similarly, if it identifies a snap sound, the Quadruped will move backward at a specific speed.
Otherwise, the Quadruped will remain in its initial position (home position).

Output

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Quadruped (Quarky)

do () motion () times at () speed

Description

Example

Introduction

Code

Logic

Output

Introduction

Quadruped Motions

Testing Code

Custome Speed Controls

Output

Code

Logic

Output

Introduction

Logic

Code

Output

Introduction

Code

Logic

Output

Introduction

Logic

Code Explain

Block Explained

Code

Output

Introduction

Hand Gesture Classifier Workflow

Class in Hand Gesture Classifier

Adding Data to Class

Training the Model

Testing the Model

Hand Pose Classifier

Logic

Code

Output

Introduction

Code

Logic

Output

Intorduction

Logic

Code

Output

Introduction

Audio Classifier Workflow

Adding Data to Class

Training the Model

Testing the Model

Export in Block Coding

Code

Logic

Output

Company

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