Robotics — Guide d’apprentissage
Connect observations, state, maps and behavior through existing explanations and experiments.
Un parcours structuré en robotique, commande, agriculture en environnement contrôlé, énergie et électronique de puissance.
Before you start Start with sensor, position and velocity terminology. Frames use vectors and matrices; estimation uses probability. Each topic links to its background.
1. System and runtime
Trace sensing through commands, then inspect communication and logs.
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The autonomous robot system
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ROS 2 communication
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Recording and replaying logs
Background: ROS 2 communication
- Lab · Run in browserReproduire les pannes du robot avec rosbag2 : entrées, TF et temps
2. Working with frames
Express the same point in different frames.
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Coordinate transforms
- Primer · Run in browserTransformations de coordonnées du robot : matrices, quaternions et TF
3. Estimating motion and state
Distinguish integrated motion from corrections using multiple sensors.
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Wheel odometry
Background: Coordinate transforms
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IMU bias and noise
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GNSS positioning and error
- Primer · Run in browserFonctionnement du GNSS et principaux produits — u-blox, Trimble, Fixposition
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Sensor fusion
Background: Frames and time · Kalman filtering
4. Registration and mapping
Explore registration failures before studying the full SLAM system.
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Point-cloud registration with ICP
Background: Coordinate transforms
- Lab · Run in browserPourquoi ICP échoue : initialisation, valeurs aberrantes et géométrie symétrique
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LiDAR SLAM
Background: Point-cloud registration with ICP · Pose-graph update
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Evaluating SLAM accuracy
Background: LiDAR SLAM
- Lab · Run in browserComment évaluer le SLAM — ATE, RPE, temps d’exécution et défaillances
5. From maps to motion
Separate map representation, paths and behavior, then connect them with Nav2.
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Occupancy grids
Background: Coordinate transforms
More questions on this topic
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Path search on a map
Background: Occupancy grids
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Behavior selection
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