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AI Autonomous Systems in Simulation
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Module 1
Mathematical and Framework Foundations
1
Robot Pose: Homogeneous Transformation Matrices
Represent 3D robotic translation and rotation using homogeneous transformation matrices and perform matrix multiplications to compose them.
2
Modeling Uncertainty with Gaussian Distributions
Model sensor noise and state uncertainty using Gaussian distributions and covariance matrices.
3
Partial Derivatives & Jacobians for Kinematics & State Estimation
Compute partial derivatives and Jacobians for use in kinematic chains and non-linear state estimation.
4
Bayes' Rule for Robot State Estimation
Explain the application of Bayes' Rule for probabilistic state estimation in robotics.
1D Bayesian Tracker in Python
Implement a discrete Bayesian filter in Python to track an object's position in a 1D grid world.
ROS2 Workspace and Publisher/Subscriber Basics
Set up a ROS2 workspace, create a custom package, and implement a basic publisher/subscriber pair.
RViz2 Visualization Essentials
Visualize robot models, sensor data, and coordinate frames (TFs) using RViz2.
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