Autonomous Mobile Manipulation
( Robotics )

Lecture 1 : Introduction

Lecture 2: Transformations

Lecture 3: Rigid Body Motion

Lecture 4: Rigid Body Dynamics

Lecture 5: Actuation

Lecture 6: Wheeled Robot Kinematics

Lecture 7: Guidance: Open-Loop, Feedback-driven

Lecture 8: L1 Guidance, Pure Pursuit, Stanley Controller

Lecture 9: Manipulation: Forward Kinematics, Manipulability

Lecture 10: Manipulation: Inverse Kinematics, Trajectory Generation

Lecture 11: Manipulation: Dynamics

Lecture 12: Planning Fundamentals

Lecture 13: Planning: Graph-based, Dijkstra, A*

Lecture14: Planning: Sampling-based, PRM, sPRM, PRM*, RRT

Lecture15: Planning: Sampling-based, RRG, RRT*

Lecture 16: Sensors

Lecture 17: State Estimation: Bayesian Estimation - Kalman Filter

Lecture 18: State Estimation: Attitude & Heading - Extended Kalman Filter

Lecture 19: State Estimation: SLAM Extended Kalman Filter

Lecture 20: SLAM Unscented Kalman Filter, Particle Filter

Lecture 21: State Estimation: SLAM Graph Optimization

Lecture 22: SLAM Smoothing, Fixed-Lag, Keyframe-based