This page will list the topics actually covered in each class.
- [M 3/6] Review of Markov Localization, Exercise 5, Particle
filtering (for localization), the physics of SONAR.
- [R 3/2] Review of covariance matrices, Jacobians, etc.
- [M 2/27] Quiz 3. Conditional probabilities and Markov localization???
- [R 2/23] Kalman filter, EKF???
- [T 2/21] Maximum likelihood estimation??? Merging and compounding
- [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
- [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.
- [R 2/9] Crash course in probability and statistics: random
variables, expected value, variance and standard deviation, Gaussian
- [M 2/6] Obstacle avoidance and local navigation: steer angle
field approach, curvature-velocity method and dynamic window approach,
cross-coupled controller, pure pursuit.
- [R 2/2] Exercise 1 solutions, Obstacle avoidance and local
navigation: potential fields and the VFH/VFH+ methods.
- [M 1/30] Quiz 1, Bug algorithms, A* search, Exercise 1.
- [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),
- [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.
- [R 1/19] Introduction; Mobile robot hardware: locomotion
configurations, centers of rotation (CORs), characterizing locomotion
configurations with sets of CORs, sensors.