6 posts tagged with "robotics"

Robots generate massive amounts of data that must be managed effectively. Challenges like limited on-device storage, the need for real-time processing, and high cloud storage costs make it essential to find efficient solutions. Balancing edge and cloud storage while keeping data synchronized is a key part of effective management.

This article begins by outlining these challenges and offering practical strategies, such as using time-series databases and implementing retention policies. We will then introduce ReductStore, a specialized database designed to meet the unique needs of robotic systems. With features like real-time ingestion, efficient querying with batching, smart retention policies, and edge-to-cloud replication, ReductStore offers a cost-effective and high-performance solution for storing and managing robotic data.

We’ll also explore a hand-on example where we’ll show how you can set up ReductStore and use it for storing and managing data. Finally, we will compare ReductStore with MongoDB, explaining why ReductStore is the better choice for robotics. This comprehensive guide is designed to help engineers and developers overcome the challenges of robotic data management and optimize their systems.

Robotics applications generate and process a wide variety of data, such as sensor readings, video streams, logs, and AI model outputs. Managing this data efficiently is crucial because it affects the performance, scalability, and reliability of the entire system.

In this article, we'll compare ReductStore and MongoDB, two databases designed to handle different aspects of data management. ReductStore is a time-series blob storage solution optimized for managing large amounts of data coming from continuous streams. MongoDB, on the other hand, is a popular NoSQL database known for its flexibility, scalability, and support for unstructured and semi-structured data.

By understanding the strengths and limitations of each, you can make an informed decision to meet your project's specific data needs.

The Robot Operating System (ROS) stands as a versatile framework for developing sophisticated robotic applications with various sensors, including cameras. These cameras are relatively inexpensive and widely used as they can provide a wealth of information about the robot's environment.

Processing camera output with computer vision requires efficient solutions to handle massive amounts of data in real time. ROS 2 is designed with this in mind, but it is a communication middleware and does not provide a built-in solution for storing and managing large volumes of image data.

Addressing this challenge, this blog post will guide you through setting up ROS 2 with ReductStore—a time-series database for unstructured data optimized for edge computing, ensuring your robotic applications can process and store camera outputs effectively.

This blog post will focus specifically on image data, but if you are interested in a more general overview you can read How to Store and Manage Robotic Data.