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1. Introduction
1.1. Models
1.2. Reasoning
1.3. The Mathematics of Robotics
2. A Trash Sorting Robot
2.1. Modeling the World State
2.2. Actions for Sorting Trash
2.3. Sensors for Sorting Trash
2.4. Perception
2.5. Decision Theory
2.6. Learning
2.7. Chapter Summary
3. A Robot Vacuum Cleaner
3.1. Modeling the State of the Vacuum Cleaning Robot
3.2. Actions over time
3.3. Dynamic Bayes Nets
3.4. Perception with Graphical Models
3.5. Markov Decision Processes
3.6. Learning to Act Optimally
3.7. Chapter Summary
4. Warehouse Robots in 2D
4.1. Continuous State
4.2. Moving in 2D
4.3. Sensor Models with Continuous State
4.4. Localization
4.5. Planning for Logistics
4.6. Some System Identification
4.7. Chapter Summary
5. A Mobile Robot With Simple Kinematics
5.1. State Space for a Differential Drive Robot
5.2. Motion Model for the Differential Drive Robot
5.3. Robot Vision
5.4. Computer Vision 101
5.5. Path Planning
5.6. Deep Learning
5.7. Chapter Summary
6. Autonomous Vehicles
6.1. Planar Geometry
6.2. Kinematics for Driving
6.3. Sensing for Autonomous Vehicles
6.4. SLAM
6.5. Planning for Autonomous Driving.
6.6. Deep Reinforcement Learning
6.7. Chapter Summary
7. Autonomous Drones in 3D
7.1. Moving in Three Dimensions
7.2. Multi-rotor Aircraft
7.3. Sensing for Drones
7.4. Visual SLAM
7.5. Trajectory Optimization
7.6. Neural Radiance Fields for Drones
7.7. Chapter Summary
8. Bibliography
Index
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