Schedule

2. [M 3/6] Review of Markov Localization, Exercise 5, Particle filtering (for localization), the physics of SONAR.

3. [R 3/2] Review of covariance matrices, Jacobians, etc.

4. [M 2/27] Quiz 3. Conditional probabilities and Markov localization???

5. [R 2/23] Kalman filter, EKF???

6. [T 2/21] Maximum likelihood estimation??? Merging and compounding measurements???

7. [R 2/16] Review of probability and statistics, Exercise 3, estimating a constant with different measurement variances, recursive versus batch estimation, merging and compounding (scalar and vector cases), covariance of a linearly transformed random variable, Kalman filter.

8. [M 2/13] Quiz 2. Crash course in probability and statistics: independence, adding independent random variables covariance, covariance matrices, confidence bounds, confidence bounds of the 2D Gaussian PDF, least squares as maximum likelihood estimation, averaging as the maximum likelihood estimator for a constant.

9. [R 2/9] Crash course in probability and statistics: random variables, expected value, variance and standard deviation, Gaussian PDF.

10. [M 2/6] Obstacle avoidance and local navigation: steer angle field approach, curvature-velocity method and dynamic window approach, cross-coupled controller, pure pursuit.

11. [R 2/2] Exercise 1 solutions, Obstacle avoidance and local navigation: potential fields and the VFH/VFH+ methods.

12. [M 1/30] Quiz 1, Bug algorithms, A* search, Exercise 1.

13. [R 1/26] PID control, effect of parameters on system response, steady state error, implementation issues (actuator saturation, integrator windup, and noise accentuation in differentiation), Ziegler-Nichols tuning.

14. [M 1/23] Dynamics of second order systems, open loop and closed loop (feedback) control, harmonic oscillator, damped harmonic oscillator, response of second order systems.

15. [R 1/19] Introduction; Mobile robot hardware: locomotion configurations, centers of rotation (CORs), characterizing locomotion configurations with sets of CORs, sensors.