[[TableOfContents]]

== Information ==
I plan to study a motion planning algorithm.
I will refer to the famous course from USC.

This is course information.
Instructor: Professor Nora Ayanian
Course: Coordinated Mobile Robotics

== Reference ==
Book: [http://planning.cs.uiuc.edu Planning Algorithm]
Course: [https://web-app.usc.edu/soc/syllabus/20141/29990.pdf CSCI 599]

== Note ==
=== Week 1 ===
Read Chapter 2

==== Discrete Planning ====
 * All models are completely known and predictable
 * Problem Solving and Planning are used as synonym

===== Introduction to Discrete Feasible Planning =====
====== Problem Formulation ======
 * State Space Model
   * State = Distinct Situation for the world (x)
   * Set of all possible states = State space (X) -> Countable
 * State Transition Equation
   x' = f(x, u)
   * x : current state
   * x': new state
   * u : each action
 * Set U of all possible actions over all states
   U = set of U(x), x ∈ X
   * U(x): action space for each state x
   * For distinct x, x' ∈ X, U(x) and U(x') are not necessarily disjoint
 * Xg: a set of goal states
 * Formulation 2.1 = Discrete Feasible Planning
   1. A nonempty state space X, which is a finite or countably infinite set of states.
   2. For each state x ∈ X, a finite action space U(x).
   3. A state transition function f that produces a state f(x,u) ∈ X for every x ∈ X and u ∈ U(x). The state transition equation is derived from f as x′ =f(x,u).
   4. An initial state x1 ∈ X.
   5. A goal set Xg ⊂ X.
   => Express as a "Directed State Transition Graph"
     * set of vertices = state space X
     * directed edge from x ∈ X to x′ ∈ X exists <=> exists an action u ∈ U(x) such that x′ = f(x,u)
     * initial state and goal set are designated as special vertices in the graph

====== Examples of Discrete Planning ======

== Comments ==

== Back page ==
* [JunhyuckWoo]