6 posts tagged with "ros"

Working with real-world robot data depends on how ROS (Robot Operating System) messages are stored. In the article 3 Ways to Store ROS Topics, we explored several approaches — including storing compressed Rosbag files in time-series storage and storing topics as separate records.

In this tutorial, we'll focus on the most common format: .bag files recorded with Rosbag. These files contain valuable data on how a robot interacts with the world — such as odometry, camera frames, LiDAR, or IMU readings — and provide the foundation for analyzing the robot's behavior.

You’ll learn how to:

• Extract motion data from .bag files
• Create basic velocity features
• Train a classification model to recognize different types of robot movements

We'll use the bagpy library to process .bag files and apply basic machine learning techniques for classification.

Although the examples in this tutorial use data from a Boston Dynamics Spot robot (performing movements like moving forward, sideways, and rotating), you can adapt the code for your recordings.

Data management is a very important element in modern robotics systems. As robots become more advanced, they generate massive amounts of information from various sources, such as sensor readings, system logs, and video feeds, and being able to manage this data effectively can mean the difference between a robot that performs well and one that fails to meet expectations.

In this article, we will compare different data management tools for robotics by looking into their key features, strengths, and the types of use cases they are best suited for. Understanding these differences will help robotics engineers and developers choose the right tool for their specific needs.

The Robot Operating System (ROS) is a powerful framework for developing and managing robotic systems. It simplifies integration, communication, and development through various tools and libraries. ROS is built around a communication system that uses a publish-subscribe model to connect components, where some, like sensors or cameras, act as publishers, and others, like motors or processors, are subscribers. The data shared between these components is organized into topics.

To make the most of this data, especially when it's needed later for analysis, debugging, or sharing, it's crucial to store it efficiently. In this article, we'll dive into three methods for storing ROS topics, comparing their benefits and limitations to help you choose the best one for your needs. In case you need to gain a broader understanding of how to handle robotics data effectively, make sure to check out our article on storing and managing robotics data first.