[AMR] Add feedback loops

electives/amr/parts/05_planning-control/00_feedback-control/03_mpc.tex

@@ -1,3 +1,9 @@
+
+Below feedback loops for PID and LQR
+
+\includegraphics[width=0.49\columnwidth]{assets/loop-pid.png}
+\includegraphics[width=0.5\columnwidth]{assets/loop-lqr.png}
+
 \subsection{MPC}
 \bi{Cost function} ($p(\vec{x}_N)$ \textit{terminal cost}, sum the \textit{stage cost})
 \[

electives/amr/parts/05_planning-control/01_motion-planning-exploration/03_rrt.tex

@@ -1,4 +1,3 @@
-\newpage
 \subsubsection{Rapidly-Exploring Random Tree (RRT)}
 \begin{algorithm}
     \small
@@ -27,6 +26,9 @@
         \EndProcedure
     \end{algorithmic}
 \end{algorithm}
+Notes: \texttt{nearestConfiguration} may be on edge, then edge is split;
+\texttt{stoppingConfiguration} returns furthest config $x_f$ on segment $x_n$ to $x$ that produces collision-free edge $(x_n, x_f)$
+
 Returns a collision-free path as graph. Need nearest neighbour search.
 Extension to RRT* to make path better:
 \begin{algorithm}

electives/amr/parts/05_planning-control/02_learning-to-act/02_reinforcement-learning.tex

@@ -26,3 +26,6 @@ $\varepsilon$ is prob. to act randomly, $1 - \varepsilon$ is prob. to act on pol
 Value-based (estimate val or $Q$-func and extract pol., e.g. Q-Learn),
 Actor-Critic (estim. val or $Q$ of curr. pol., improve pol., e.g. A3C, SAC),
 Policy-Gradient (diff. expect. reward w.r.t. params of policy network, e.g. REINFORCE)
+
+\includegraphics[width=0.5\columnwidth]{assets/loop-rl.png}
+\includegraphics[width=0.5\columnwidth]{assets/loop-drl.png}