ROAS 5700 Robot Motion Planning and Control
Descriptions: This course introduces the advanced methodologies in the context of motion planning and control for robotics and autonomous systems. Various methodologies are introduced, including search-based methods, grid-based methods, sampling-based methods, optimization-based methods, learning-based methods, etc. In general, this course covers modern approaches, deep theory, and good practice envisions. In addition to the fundamental knowledge in motion planning and control, the students will also have the opportunity to discover and learn cutting-edge methodologies in the related field, aligning with the substantial developments in robotics, autonomous driving, UAVs, etc.
| Outline | |
|---|---|
| Part I: Background | Week 1: Introduction of Motion Planning and Control |
| Part II: Path Planning | Week 2: Search-based Methods |
| Week 3: Grid-based Methods and Sampling-based Methods | |
| Part III: Trajectory Planning | Week 4: Trajectory Planning |
| Week 5 - 7: Optimization-based Methods | |
| Week 8: Seminar | |
| Part IV: Motion Control | Week 9: PID Control |
| Week 10: Linear Quadratic Regulator | |
| Week 11: Model Predictive Control | |
| Week 12: Special Topics in Robot Motion Planning and Control | |
| Week 13: Group Discussions for Project | |
ROAS 6000I Optimal Control Systems
Descriptions: Optimal control is an effective approach that has been widely used in robotics and autonomous systems. This course aims to provide students with a firm foundation in optimality principles in modern control system design. Fundamental key concepts in optimal control are introduced, including Hamiltonian, Pontryagin's minimum principle, Bellman equation, dynamic programming, etc., which bring the prospect of formal linkage to reinforcement learning techniques. Different optimal control methods are also covered in this course, such as LQR, Kalman filter, LQG, MPC, etc. Additionally, the students will have the opportunity to discover and learn cutting-edge methodologies in the related field and develop the expertise in optimal control system design.
| Outline | |
|---|---|
| Part I: Background | Week 1: Control Revisit |
| Week 2: Static Optimization | |
| Part II: Optimal Control | Week 3: Optimal Control of Discrete-time Systems |
| Week 4: Optimal Control of Continuous-time Systems | |
| Week 5: Linear Quadratic Regulator | |
| Week 6: Dynamic Programming | |
| Week 7: Seminar | |
| Week 8: Kalman Filter and Linear Quadratic Gaussian | |
| Week 9: Constrained Input Control | |
| Week 10: Model Predictive Control | |
| Part III: Reinforcement Learning | Week 11: Reinforcement Learning I |
| Week 12: Reinforcement Learning II | |
| Week 13: Group Discussions for Project | |