Replace Sleipnir with Jormungandr

.pylintrc

@@ -423,6 +423,7 @@ disable=bad-inline-option,
         locally-disabled,
         no-else-return,
         no-member,
+        no-name-in-module,
         raw-checker-failed,
         redefined-outer-name,
         suppressed-message,

Makefile

@@ -91,27 +91,9 @@ build/venv.stamp:
 	@mkdir -p $(@D)
 	python3 setup_venv.py
 	$(VENV_PIP) install -e ./bookutil
-	$(VENV_PIP) install frccontrol==2023.28 pylint qrcode requests robotpy-wpimath==2024.0.0b3.post1
+	$(VENV_PIP) install frccontrol==2023.28 sleipnirgroup-jormungandr==0.0.1.dev23 pylint qrcode requests robotpy-wpimath==2024.0.0b3.post1
 	@touch $@
 
-ifeq ($(OS),Windows_NT)
-$(CPP_EXE): build/%.exe: %.cpp build/venv.stamp
-else
-$(CPP_EXE): build/%: %.cpp build/venv.stamp
-endif
-	@mkdir -p $(@D)
-	# Run CMake
-	cmake -B $(@D) -S $(dir $<)
-	# Build and run binary
-	cmake --build $(@D) --target $(notdir $(basename $@))
-ifeq ($(OS),Windows_NT)
-	cd $(@D)/Debug && ./$(notdir $@)
-else
-	cd $(@D) && ./$(notdir $@)
-endif
-	# Convert generated CSVs to PDFs
-	$(abspath $(VENV_PYTHON)) ./snippets/sleipnir_csv_to_pdf.py $(@D)/*.csv
-
 $(PY_STAMP): build/%.stamp: %.py build/venv.stamp
 	@mkdir -p $(@D)
 	cd $(@D) && $(abspath $(VENV_PYTHON)) $(abspath ./$<) --noninteractive
@@ -129,8 +111,6 @@ format:
 	# Format .py
 	find . -type f -name '*.py' -not -path './build/*' -print0 | xargs -0 python3 -m autoflake -i
 	find . -type f -name '*.py' -not -path './build/*' -print0 | xargs -0 python3 -m black -q
-	# Format .cmake
-	find . -type f \( -name CMakeLists.txt -o -name '*.cmake' \) -not -path './build/*' -print0 | xargs -0 python3 -m gersemi -i -q
 
 # Run formatters and all .tex linters. The commit metadata files and files
 # generated by Python scripts are dependencies because check_tex_includes.py

motion-planning/double_integrator_minimum_time.py

@@ -0,0 +1,110 @@
+#!/usr/bin/env python3
+
+"""Solves double integrator minimum-time trajectory optimization problem with Jormungandr."""
+
+from pathlib import Path
+import re
+import sys
+
+from jormungandr.optimization import OptimizationProblem
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+import numpy as np
+
+from bookutil import latex
+
+if "--noninteractive" in sys.argv:
+    mpl.use("svg")
+plt.rc("text", usetex=True)
+
+
+def main():
+    """Entry point."""
+    T = 3.5  # s
+    dt = 0.005  # 5 ms
+    N = int(T / dt)
+
+    r = 2.0
+
+    problem = OptimizationProblem()
+
+    # 2x1 state vector with N + 1 timesteps (includes last state)
+    X = problem.decision_variable(2, N + 1)
+
+    # 1x1 input vector with N timesteps (input at last state doesn't matter)
+    U = problem.decision_variable(1, N)
+
+    # Kinematics constraint assuming constant acceleration between timesteps
+    for k in range(N):
+        p_k1 = X[0, k + 1]
+        v_k1 = X[1, k + 1]
+        p_k = X[0, k]
+        v_k = X[1, k]
+        a_k = U[0, k]
+
+        problem.subject_to(p_k1 == p_k + v_k * dt)
+        problem.subject_to(v_k1 == v_k + a_k * dt)
+
+    # Start and end at rest
+    problem.subject_to(X[:, 0] == np.array([[0.0], [0.0]]))
+    problem.subject_to(X[:, N] == np.array([[r], [0.0]]))
+
+    # Limit velocity
+    problem.subject_to(-1 <= X[1, :])
+    problem.subject_to(X[1, :] <= 1)
+
+    # Limit acceleration
+    problem.subject_to(-1 <= U)
+    problem.subject_to(U <= 1)
+
+    # Cost function - minimize position error
+    J = 0.0
+    for k in range(N + 1):
+        J += (r - X[0, k]) ** 2
+    problem.minimize(J)
+
+    problem.solve()
+
+    ts = [k * dt for k in range(N + 1)]
+
+    # Plot states
+    labels = ["Position (m)", "Velocity (m/s)"]
+    for i in range(X.shape[0]):
+        plt.figure()
+        ax = plt.gca()
+
+        xs = X.value()[i, :]
+        ax.plot(ts, xs, label=labels[i])
+        ax.set_xlabel("Time (s)")
+        ax.set_ylabel(labels[i])
+
+        unit_name = re.search(r"(\w+)(\(.*?\))?", labels[i]).group(1).lower()
+        figname = f"{Path(__file__).stem}_{unit_name}"
+
+        if "--noninteractive" in sys.argv:
+            latex.savefig(figname)
+        else:
+            plt.show()
+
+    # Plot inputs
+    labels = ["Acceleration (m/s²)"]
+    for i in range(U.shape[0]):
+        plt.figure()
+        ax = plt.gca()
+
+        us = np.concatenate((U.value()[i, :], [0.0]))
+        ax.plot(ts, us, label=labels[i])
+        ax.set_xlabel("Time (s)")
+        ax.set_ylabel(labels[i])
+
+        unit_name = re.search(r"(\w+)(\(.*?\))?", labels[i]).group(1).lower()
+        figname = f"{Path(__file__).stem}_{unit_name}"
+
+        if "--noninteractive" in sys.argv:
+            latex.savefig(figname)
+        else:
+            plt.show()
+
+
+if __name__ == "__main__":
+    main()

motion-planning/trajectory-optimization.tex

@@ -48,18 +48,29 @@ \subsection{Double integrator minimum time}
 acceleration. The velocity constraints are $-1 \leq x(1) \leq 1$ and the
 acceleration constraints are $-1 \leq u \leq 1$.
 
+\subsubsection{Importing required libraries}
+
+Jormungandr is the Python version of Sleipnir and can be installed via
+\texttt{pip install sleipnirgroup-jormungandr}.
+\begin{code}
+  \begin{lstlisting}[style=customPython]
+    from jormungandr.optimization import OptimizationProblem
+    import numpy as np
+  \end{lstlisting}
+\end{code}
+
 \subsubsection{Initializing a problem instance}
 
 First, we need to make a problem instance.
-\begin{coderemotesubset}{cpp}
-  {motion-planning/DoubleIntegratorMinimumTime.cpp}{1}{17}
+\begin{coderemotesubset}{Python}
+  {motion-planning/double_integrator_minimum_time.py}{23}{29}
 \end{coderemotesubset}
 
 \subsubsection{Creating decision variables}
 
 Next, we need to make decision variables for our state and input.
-\begin{coderemotesubset}{cpp}
-  {motion-planning/DoubleIntegratorMinimumTime.cpp}{19}{23}
+\begin{coderemotesubset}{Python}
+  {motion-planning/double_integrator_minimum_time.py}{31}{35}
 \end{coderemotesubset}
 
 By convention, we use capital letters for the variables to designate
@@ -68,29 +79,29 @@ \subsubsection{Creating decision variables}
 \subsubsection{Applying constraints}
 
 Now, we need to apply dynamics constraints between timesteps.
-\begin{coderemotesubset}{cpp}
-  {motion-planning/DoubleIntegratorMinimumTime.cpp}{25}{36}
+\begin{coderemotesubset}{Python}
+  {motion-planning/double_integrator_minimum_time.py}{37}{46}
 \end{coderemotesubset}
 
 Next, we'll apply the state and input constraints.
-\begin{coderemotesubset}{cpp}
-  {motion-planning/DoubleIntegratorMinimumTime.cpp}{38}{48}
+\begin{coderemotesubset}{Python}
+  {motion-planning/double_integrator_minimum_time.py}{48}{58}
 \end{coderemotesubset}
 
 \subsubsection{Specifying a cost function}
 
 Next, we'll create a cost function for minimizing position error.
-\begin{coderemotesubset}{cpp}
-  {motion-planning/DoubleIntegratorMinimumTime.cpp}{50}{55}
+\begin{coderemotesubset}{Python}
+  {motion-planning/double_integrator_minimum_time.py}{60}{64}
 \end{coderemotesubset}
 
 The cost function passed to Minimize() should produce a scalar output.
 
 \subsubsection{Solving the problem}
 
 Now we can solve the problem.
-\begin{coderemotesubset}{cpp}
-  {motion-planning/DoubleIntegratorMinimumTime.cpp}{57}{57}
+\begin{coderemotesubset}{Python}
+  {motion-planning/double_integrator_minimum_time.py}{66}{66}
 \end{coderemotesubset}
 
 The solver will find the decision variable values that minimize the cost
@@ -100,22 +111,22 @@ \subsubsection{Accessing the solution}
 
 You can obtain the solution by querying the values of the variables like so.
 \begin{code}
-  \begin{lstlisting}[style=customCpp]
-double position = X.Value(0, 0);
-double velocity = X.Value(1, 0);
-double acceleration = U.Value(0);
+  \begin{lstlisting}[style=customPython]
+    position = X.value[0, 0]
+    velocity = X.value[1, 0]
+    acceleration = U.value(0)
   \end{lstlisting}
 \end{code}
 \begin{bookfigure}
-  \begin{minisvg}{2}{build/motion-planning/DoubleIntegratorMinimumTimeStatesPosition}
+  \begin{minisvg}{2}{build/motion-planning/double_integrator_minimum_time_position}
     \caption{Double integrator position}
   \end{minisvg}
   \hfill
-  \begin{minisvg}{2}{build/motion-planning/DoubleIntegratorMinimumTimeStatesVelocity}
+  \begin{minisvg}{2}{build/motion-planning/double_integrator_minimum_time_velocity}
     \caption{Double integrator velocity}
   \end{minisvg}
   \hfill
-  \begin{minisvg}{2}{build/motion-planning/DoubleIntegratorMinimumTimeInputsAcceleration}
+  \begin{minisvg}{2}{build/motion-planning/double_integrator_minimum_time_acceleration}
     \caption{Double integrator acceleration}
   \end{minisvg}
 \end{bookfigure}

setup_archlinux.sh

@@ -48,5 +48,4 @@ sudo pacman -Sy --needed --noconfirm \
   texlive-xetex
 
 # autoflake isn't in [extra] and we can't use an AUR helper
-# gersemi isn't in [extra] or the AUR
-pip3 install --user --break-system-packages autoflake gersemi
+pip3 install --user --break-system-packages autoflake

setup_macos.sh

@@ -64,6 +64,5 @@ sudo /Library/TeX/texbin/tlmgr install \
 
 # Python packages
 #  * black (to format Python source code)
-#  * gersemi (to format CMake files)
 #  * pylint (for Python linting)
-pip3 install --user autoflake black gersemi pylint qrcode requests wheel
+pip3 install --user autoflake black pylint qrcode requests wheel

setup_ubuntu.sh

@@ -45,6 +45,5 @@ sudo apt-get install -y \
 # The Ubuntu 22.04 packages are too old
 # Python packages
 #   * black (to format Python source code)
-#   * gersemi (to format CMake files)
 #   * pylint (for Python linting)
-pip3 install --user autoflake black gersemi pylint qrcode
+pip3 install --user autoflake black pylint qrcode