The following is a very tentative schedule.

Lecture Topic
1 Introduction, Project I discussion and assignment, Lie groups
2-3 Lie groups (cont.), geometry, 3-D kinematics
4 Simulation and experiment design, overview of resources, control of nonholonomic robots with examples
5 Simulation environments and engines, introduction to Stage/Gazebo.
6-7 Continuous feedback laws, navigation functions, geometric control
8 Geometric control (cont.) with examples using an aerial robot
9-13 Motion Planning
14 Motion planning in two and three dimensions with examples
15 Project I presentations, project II discussion and assignment
16 Introduction to filtering and estimation, the Kalman filter
17 Parametric filtering: Kalman filter, information form of Kalman filter, extended Kalman filter, unscented Kalman filter with examples
18-19 Case study: A tracking system for indoor localization. Sensor models and considerations.

Pragmatics of using odometry, laser, and camera data.

20 Non-parametric filtering and particle filters with examples
21-22 Simultaneous localization and mapping, EKF SLAM and FastSLAM with examples
23-24 Sampling based motion planning
25 A*-based time-constrained planning
26 planning in dynamic environments
27-28 Distributed robotics, multi-agent formation control and manipulation
29 planning under uncertainty
30 learning to plan and control
31-32 Project presentations