Teaching

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

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