## Introductory Material for Robotics Students

Under construction...

### Filtering and state estimation:

1. Wikipedia article
on recursive
bayesian estimation.

2. Wikipedia article
on particle filtering.

3. Wikipedia article on Kalman filtering

4. Thrun, Fox, Burgard, "Probabilistic Robotics", MIT press. This book
should be available in the UB library. I also have *one* copy to
lend. I have seen preprints of this book available online. Consider
looking at the following chapters. Chapter 2 covers some
basics. Chapter 3 covers Kalman filters. Chapter 4 covers histogram
filters and particle filters. Chapters 7 and 8 cover robot
localization (chapters 5 and 6 only as needed to understand chapters 7
and 8).

### Robot motion planning:

1. Wikipedia article
on rapidly exploring random trees or Steve LaValle's RRT page.

2. A recent and comprehensive source is Steve LaValle's book,
"Planning Algorithms", Cambridge University Press, is
available here.

### MDPs and Control:

1. Sutton and Barto's book "Reinforcement learning, an introduction", MIT press, contains a nice introduction to Markov Decision Processes (MDPs), value iteration, and reinforcement learning. A preprint is available here.

2. For an excellent, but rather mathematical, introduction to MDPs, value
iteration, and control, see Bertsekas, "Dynamic Programming and
Optimal Control", Athena. I found Chapters 3 and 4 to be particularly
useful.

### Trajectory optimization:

1. For an overview, see Section 14.7 of LaValle's book,
"Planning Algorithms", Cambridge University Press.

1. Betts has a very nice book on the subject: "Practical methods for optimal control using nonlinear programming", published by SIAM.

2. Todorov and Li's paper on iLQG is a useful touchpoint.

### Entropy (Shannon Entropy)

1. Wikipedia article here.