Mobile Robots and Autonomous Vehicles
Dear learners,
Get ready! The MOOC Mobile Robots and Autonomous Vehicles by Christian Laugier, Agostino Martinelli and Dizan Vasquez will begin next monday.
You will find below the detailed table of contents of the course.
See you next week!
The Mobile Robots MOOC Team
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TABLE OF CONTENTS
Week 1: State of the art, basic principles & grand challenges - from February, 08
1.0. Introduction
1.1. Socio-economic context
1.2. Technological evolution of Robotics & State of the Art
1.3. New challenges for Robotics in Human Environments
1.4. Decisional & Control Architecture for Autonomous Mobile Robots & IV
1.5. Sensing technologies: Object Detection
1.6. Sensing technologies: Robot Control & HRI
1.7. Basic technologies for Navigation in Dynamic Human Environments
1.8. Intelligent Vehicles: Context & State of the Art
1.9. Intelligent Vehicles: Technical Challenges & Driving Skills
Week 2: Bayes & Kalman filters - from February, 15
2.1. Basic concepts: robot configuration, localization and probabilistic framework
2.2. Characterization of proprioceptive and exteroceptive sensors
2.3. Wheel encoders for a differential drive vehicle
2.4. Sensor statistical models
2.5. Reminds on probability
2.6. The Bayes Filter
2.7. Grid Localization: an example in 1D
2.8. The Extended Kalman Filter (EKF)
Week 3: Extended Kalman filters - from February, 22
3.1. Examples for the Action in the EKF
3.2. Examples for the Perception in the EKF
3.3. The EKF is a weight mean
3.4. The use of the EKF in robotics
3.5. Simultaneous Localization and Mapping (SLAM)
3.6. Observability in robotics
3.7. Observability Rank Criterion
3.8. Applications of the Observability Rank Criterion
Week 4: Perception & Situation Awareness & Decision Making - from February,
29
4.1. Robot Perception for Dynamic environments - Outline & DP-Grids concept
4.2. Dynamic Probabilistic Grids ? Bayesian Occupancy Filter concept
4.3. Dynamic Probabilistic Grids ? Implementation approaches
4.4. Object level Perception functions (SLAM + DATMO)
4.5. Detection and Tracking of Mobile Objects ? Problem & Approaches
4.6. Detection and Tracking of Mobile Objects ? Model & Grid based approaches
4.7. Embedded Bayesian Perception & Short-term collision risk (DP-Grid level)
4.8. Situation Awareness ? Problem statement & Motion / Prediction Models
4.9. Situation Awareness ? Collision Risk Assessment & Decision (Object level)
Week 5: Behavior modeling and learning (with examples and exercises in Python) - from March, 07
5.1. Introduction
5.2. E-M
Clustering
5.3a. Learning typical trajectories 1/2
5.3b. Learning typical trajectories 2/2
5.4. Bayesian Filter inference: filtering, smoothing, prediction and recognition
5.5. Transforming typical trajectories in discrete time-state models
5.6. Recognizing, estimating and predicting human motion
5.7. Typical trajectories: drawbacks
5.8. Other approaches: Social Forces
5.9. Other approaches: Planning-based approaches
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