Ball Tracking with ROS2 Using NVIDIA JetBot in Isaac Sim

Omniverse Isaac Sim
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Ball Tracking with ROS2 Using NVIDIA JetBot in Isaac Sim

Overview

This project focuses on creating a ROS2 node that enables a robot to track and follow a green ball using image processing and control algorithms. The robot will use a camera to capture images, process these images to detect the green ball, and then publish movement commands to follow the ball.

Methodology

The project involves the following steps:

  1. Set up the ROS2 workspace and NVIDIA Isaac Sim: Configure your development environment to work with ROS2 and Isaac Sim.
  2. Capture images from the simulated camera: Use a camera in Isaac Sim to capture images.
  3. Process images to detect the ball: Implement an image processing node to detect a green ball.
  4. Control the JetBot to follow the ball: Develop a control node that uses the ball’s position to adjust the JetBot’s movement.

Key Components

  1. Image Capture: The robot’s camera continuously captures video frames and publishes them as ROS2 Image messages on a specific topic.
  2. Image Processing: The captured images are processed to identify the green ball. This involves converting the image to the HSV color space and using color thresholds to create a mask that highlights the green regions.
  3. Contour Detection: The processed image is analyzed to find contours, which represent the boundaries of objects in the image. The largest contour is assumed to be the green ball.
  4. Coordinate Calculation: The bounding box around the largest contour is used to calculate the center coordinates of the ball in the image frame.
  5. Control Logic: The robot uses a proportional control strategy to follow the ball. It calculates the error between the ball’s position and the center of the image and adjusts its linear and angular velocities accordingly. If the ball is centered, the robot moves forward; if the ball is off-center, the robot rotates to align with it.
  6. Publishing Commands: The calculated movement commands are published as velocity messages to control the robot’s motion.

Coordinate Calculation:

  • The bounding box around the largest contour provides the coordinates (x, y) of the top-left corner and the width (w) and height (h) of the box.
  • The center coordinates of the ball are calculated as:
  • ball_center_x=x+2w​
  • ball_center_y=y+2h​

calculate center coordinate

Control Logic:

  • A proportional control strategy is used to follow the ball.
  • The error between the ball’s x-coordinate and the center of the image frame is calculated:

To calculate coordinate of image

The angular speed to adjust the robot’s orientation is calculated using a proportional gain kpkp​:

calculated proportional gain

The robot’s movement commands are determined based on the error:

  • If the error is small (ball is nearly centered), the robot moves forward.
  • If the error is large, the robot adjusts its orientation.

Publishing Commands:

  • The calculated movement commands (linear and angular velocities) are published as velocity messages to control the robot’s motion.

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Real-Time Application

  • Warehouse Automation: Robots can use ball tracking to follow objects or move towards targets for tasks like picking and placing items.
  • Interactive Robots: Robots can engage in games or interactive activities, enhancing user experience in entertainment or educational settings.
  • Navigation and Guidance: Ball tracking can be adapted for navigation tasks where the robot follows markers or beacons to move through an environment.

Conclusion

In this tutorial, we’ve explored how to create a ball tracking system using ROS2, NVIDIA JetBot, and Isaac Sim. We’ve covered the setup process, image processing for ball detection, and control logic to follow the detected ball. This project demonstrates the integration of computer vision and robotics, showcasing the potential of ROS2 and NVIDIA Isaac Sim in developing advanced robotic applications.

By following this tutorial, you can extend the project further by improving the detection algorithm, tuning the control parameters, and applying the system to different robotic platforms. Happy experimenting!