AI

Unlock the magic of LLMs: The AI game-changer transforming how computers converse!

Dive into Bayes' Rule: a tool that refines predictions using new data, from daily weather to vital health checks

Explore the world of robotics & SLAM algorithms! Learn how robots navigate unknown environments, build real-time maps, and overcome challenges. Dive in now!

Markov localization is a technique used in robotics to estimate a robot's position by dividing the environment into a grid of possible poses, updating the probability of the robot being in each cell based on sensor data and prior knowledge. The process involves action and perception updates, with the former updating the robot's belief about its position based on its previous belief and the action it has taken, while the latter uses new sensor measurements to refine the estimate. However, if the number of cells in the grid is too large, the computation can become too heavy for real-time operations, so it's important to balance grid granularity with available resources.

Learn how robots navigate through their environment using probability theory with the Bayes Filter Localisation Algorithm, which estimates a robot's position by combining its prior belief with sensor measurements and control inputs, improving accuracy by representing the robot's pose as a probability distribution.

This blog post covers the challenges of robot localisation, the use of dead reckoning and odometry for robot pose estimation, effector noise or motion uncertainty, and various methods for robot localisation.

The blog post provides an introduction to the Robot Operating System (ROS) and its features, including the ability to communicate between different systems and distribute processing load across multiple machines. It also explains important ROS terms such as publishing and subscribing, node types, and message types. The post highlights the modular and scalable architecture of ROS, and its use of pre-defined message types for standardisation. Finally, it touches upon the complex systems that can be created using ROS, and the ability to simulate components in place of real ones.