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Exercise Description:

In this practice the intention is to develop a color filter that allow us to segment some object in the image. You will have to get in contact with RGB and HSV color spaces, and OpenCV (python) library.

For the realization of the practice, you are provided of a framework written in python that collects the images and allows its visualization. These images will be collected through several specific videos for this practice (pelota_roja.avi and pelotas_roja_azul.avi), although you could use your own videos instead or even a camera..

Exercise Details:

• Link GitHub: Color Filter
• Repository: JdeRobot Academy

Exercise Description:

In this practice, the intention is to program the necessary logic to allow kobuki robot to generate a 3D reconstruction of the scene that it is receiving throughout its left and right cameras.

Exercise Details:

• Link GitHub: 3D Reconstruction
• Repository: JdeRobot Academy

Exercise Description:

In this practice, the intention is to use your knowledge in image processing to segment the faces of people and follow them through a camera connected by USB to your computer. For this, you must have the right hardware (Sony model EVI d100p), and then implement the logic that performs the segmentation of a face and an algorithm that collects that data and transforms it into orders for the camera's actuators, which must follow the movement of the person.

Exercise Details:

• Link GitHub: Follow Face
• Repository: JdeRobot Academy

Exercise Description:

The objective of this practice is to perform a PID reactive control capable of following the line painted on the racing circuit.

Exercise Details:

• Link GitHub: F1 Follow Line
• Repository: JdeRobot Academy

Exercise Description:

The objective of this practice is to implement the logic of the VFF navigation algorithm.

Navigation using VFF (Virtual Force Field), consists of:

• Each object in the environment generates a repulsive force towards the robot.
• Destiny generates an attractive force in the robot.

This makes it possible for the robot to go towards the target, distancing itself of the obstacles, so that their address is the vector sum of all the forces.

The solution can integrate one or more of the following levels of difficulty, as well as any other one that occurs to you:

• Go as quickly as possible
• Choose the safest way
• Obstacles in movement
• Robustness in situations of indecision (zero vector sum)

Exercise Details:

• Link GitHub: Obstacle Avoidance
• Repository: JdeRobot Academy

Exercise Description:

The objective of this practice is to implement the logic of a Gradient Path Planning (GPP) algorithm. Global navigation through GPP, consists of:

• Selected a destination, the GPP algorithm is responsible for finding the shortest path to it, avoiding, in the case of this practice, everything that is not road.
• Once the path has been selected, the logic necessary to follow this path and reach the objective must be implemented in the robot.

With this, it is possible for the robot to go to the marked destination autonomously and following the shortest path.

The solution can integrate one or more of the following levels of difficulty, as well as any other one that occurs to you:

• Reach the goal.
• Optimize the way to find the shortest path.
• Arrive as quickly as possible to the destination.

Exercise Details:

• Link GitHub: Global Navigation
• Repository: JdeRobot Academy

Exercise Description:

The objective of this practice is to implement the logic of a Gradient Path Planning (GPP) algorithm. Global navigation through GPP, consists of:

• Selected a destination, the GPP algorithm is responsible for finding the shortest path to it, avoiding, in the case of this practice, everything that is not road.
• Once the path has been selected, the logic necessary to follow this path and reach the objective must be implemented in the robot.

With this, it is possible for the robot to go to the marked destination autonomously and following the shortest path.

The solution can integrate one or more of the following levels of difficulty, as well as any other one that occurs to you:

• Reach the goal.
• Optimize the way to find the shortest path.
• Arrive as quickly as possible to the destination.

Exercise Details:

• Link GitHub: Global Navigation
• Repository: JdeRobot Academy

Exercise Description:

The goal of this practice is to implement the logic of a navigation algorithm for an automated vehicle. The vehicle must find a parking space and park properly.

Exercise Details:

• Link GitHub: AutoPark
• Repository: JdeRobot Academy

Exercise Description:

The goal of this practice is to implement the logic of a navigation algorithm for an automated vehicle. The vehicle must Stop at the T joint, where there is a stop sign, wait until there are no cars and pass once the road is clear.

Exercise Details:

• Link GitHub: Car Junction
• Repository: JdeRobot Academy

Exercise Description:

The intention of this excersise is to program a basic behaviour of bump-spin using a finite state machine. For that, we will use JdeRobot visualStates tool, that allows you to create your own states machine in an intuitive way.

There is a Kobuki robot inside a labyrinth or scenario. The robot will go front until it gets close to an obstacle. The it will go back, turn a random angle and go front again repeating the process. This exercise aims to show the power of automata when building robot behavior.

Using the JdeRobot tool VisualStates the solution works like this. The tool's detailed manual can be found here.

Exercise Details:

• Link GitHub: Bump & Go
• Repository: JdeRobot Academy

Exercise Description:

The objective of this practice is to implement the logic of a navigation algorithm for an autonomous vacuum. The main objective will be to cover the largest area of ​​a house using the programmed algorithm.

Program a robotic vacuum-cleaner like Roomba to clean your home. It does have a compass but not precise self-localization.

Exercise Details:

• Link GitHub: Vaccum Cleaner
• Repository: JdeRobot Academy

Exercise Description:

The objective of this practice is to implement the logic of a navigation algorithm for an autonomous vacuum with autolocation. The main objective will be to cover the largest area of ​​a house using the programmed algorithm.

Exercise Details:

• Link GitHub: Vaccum Cleaner SLAM
• Repository: JdeRobot Academy

Exercise Description:

In this practice we will learn the use of PID controllers to implement a local navigation algorithm in the quadricopters. For this practice a world has been designed for the Gazebo simulator.

This world has a 3D model of the AR.Drone and 5 beacons arranged in cross mode. The intention is to make the drone do the following route:the first beacon to visit is the one to the left of the drone, the next will be the one in front, then you will have to go to the one that was located to the left of the initial position, then the one in the back, and finally to the one in front, in the most distant position. Finally we will make it return to the initial position and land.

Exercise Details:

• Link GitHub: Drone Position Control
• Repository: JdeRobot Academy

Exercise Description:

The exercise consists of following a robot on the ground using a drone. The movements described by the ground robot will have to be followed by the air robot.

Exercise Details:

• Link GitHub: Follow Ground Robot
• Repository: JdeRobot Academy

Exercise Description:

The goal of this practice is to implement the logic that allows a quadricopter to follow a road. In order to do this, you will have to establish a color filter to segment road lines, and then develop an algorithm to follow them until the end of the road.

Exercise Details:

• Link GitHub: Follow Road
• Repository: JdeRobot Academy

Exercise Description:

The objective of this practice is to program an autonomous behavior for a drone that simulates the game of the cat and the mouse.

This is to make the black drone (cat), programmed by the student, follow the red drone (mouse, that is already preprogrammed and has a random path) as close as possible without crashing. The referee application will measure the distance between the two drones and assign a score based on it. The longer time you spend close to the mouse, more score will be obtained.

Exercise Details:

• Link GitHub: Drone Cat Mouse
• Repository: JdeRobot Academy

Exercise Description:

The objective of this practice is to develop an algorithm that visualize a beacon and land on it.

Exercise Details:

• Link GitHub: Landing On a Moving Car
• Repository: JdeRobot Academy

Exercise Description:

The objective of this practice is to implement the logic that allows a drone to escape from a labyrinth through visual signals placed on the ground.

Exercise Details:

• Link GitHub: Escaping from a labyrinth
• Repository: JdeRobot Academy

Exercise Description:

The intention of this practice is to implement the logic that allows a quadricopter to reognize recognize lost people (faces of people) and save their position on the map, in order to perform a subsequent rescue maneuver.

Exercise Details:

• Link GitHub: People rescue after an earthquake
• Repository: JdeRobot Academy

Project Description:

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Project Details:

• Client:Lorem Ipsum
• Services:Web Design, Photography
• Date:January 2015