diff --git a/notebooks/demo.ipynb b/notebooks/demo.ipynb index aa0c5cee..b2265199 100644 --- a/notebooks/demo.ipynb +++ b/notebooks/demo.ipynb @@ -11,12 +11,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['c:\\\\Program Files\\\\Python311\\\\python313.zip', 'c:\\\\Program Files\\\\Python311\\\\DLLs', 'c:\\\\Program Files\\\\Python311\\\\Lib', 'c:\\\\Program Files\\\\Python311', '', 'C:\\\\Users\\\\altaz\\\\AppData\\\\Roaming\\\\Python\\\\Python313\\\\site-packages', 'C:\\\\Users\\\\altaz\\\\AppData\\\\Roaming\\\\Python\\\\Python313\\\\site-packages\\\\win32', 'C:\\\\Users\\\\altaz\\\\AppData\\\\Roaming\\\\Python\\\\Python313\\\\site-packages\\\\win32\\\\lib', 'C:\\\\Users\\\\altaz\\\\AppData\\\\Roaming\\\\Python\\\\Python313\\\\site-packages\\\\Pythonwin', 'c:\\\\Program Files\\\\Python311\\\\Lib\\\\site-packages', 'c:\\\\Users\\\\altaz\\\\session\\\\oxford\\\\engineering\\\\y3\\\\course\\\\1-engineering computation\\\\0-scientific coding\\\\practical\\\\b1-coding-practical-mt24']\n" + ] + } + ], "source": [ "# You can double-check your Python path like this...\n", "import sys \n", + "import os\n", + "sys.path.append(os.path.abspath('..'))\n", "print(sys.path)" ] }, @@ -30,16 +40,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Import relevant modules\n", + "from uuv_mission import Submarine, ClosedLoop, Mission, controller\n", "\n", "sub = Submarine()\n", "# Instantiate your controller (depending on your implementation)\n", "closed_loop = ClosedLoop(sub, controller)\n", - "mission = Mission.from_csv(\"path/to/file\") # You must implement this method in the Mission class\n", + "mission = Mission.from_csv(\"../data/mission.csv\") # You must implement this method in the Mission class\n", "\n", "trajectory = closed_loop.simulate_with_random_disturbances(mission)\n", "trajectory.plot_completed_mission(mission)" diff --git a/uuv_mission/__init__.py b/uuv_mission/__init__.py index e69de29b..db099414 100644 --- a/uuv_mission/__init__.py +++ b/uuv_mission/__init__.py @@ -0,0 +1,4 @@ +from .dynamic import Submarine +from .dynamic import ClosedLoop +from .dynamic import Mission +from .control import controller \ No newline at end of file diff --git a/uuv_mission/control.py b/uuv_mission/control.py new file mode 100644 index 00000000..cf92546b --- /dev/null +++ b/uuv_mission/control.py @@ -0,0 +1,12 @@ +# implementation of a PD controller +def controller(y0, y1, r0, r1): + # proportional gain + Kp = 0.18 + # derivative gain + Kd = 0.65 + # error + e0 = r0 - y0 + e1 = r1 - y1 + # action + u = Kp * e0 + Kd * (e0 - e1) + return u \ No newline at end of file diff --git a/uuv_mission/dynamic.py b/uuv_mission/dynamic.py index c7c7ad53..24eda699 100644 --- a/uuv_mission/dynamic.py +++ b/uuv_mission/dynamic.py @@ -3,105 +3,111 @@ import numpy as np import matplotlib.pyplot as plt from .terrain import generate_reference_and_limits +import pandas as pd class Submarine: - def __init__(self): - - self.mass = 1 - self.drag = 0.1 - self.actuator_gain = 1 - - self.dt = 1 # Time step for discrete time simulation - - self.pos_x = 0 - self.pos_y = 0 - self.vel_x = 1 # Constant velocity in x direction - self.vel_y = 0 - - - def transition(self, action: float, disturbance: float): - self.pos_x += self.vel_x * self.dt - self.pos_y += self.vel_y * self.dt - - force_y = -self.drag * self.vel_y + self.actuator_gain * (action + disturbance) - acc_y = force_y / self.mass - self.vel_y += acc_y * self.dt - - def get_depth(self) -> float: - return self.pos_y - - def get_position(self) -> tuple: - return self.pos_x, self.pos_y - - def reset_state(self): - self.pos_x = 0 - self.pos_y = 0 - self.vel_x = 1 - self.vel_y = 0 - + def __init__(self): + + self.mass = 1 + self.drag = 0.1 + self.actuator_gain = 1 + + self.dt = 1 # Time step for discrete time simulation + + self.pos_x = 0 + self.pos_y = 0 + self.vel_x = 1 # Constant velocity in x direction + self.vel_y = 0 + + + def transition(self, action: float, disturbance: float): + self.pos_x += self.vel_x * self.dt + self.pos_y += self.vel_y * self.dt + + force_y = -self.drag * self.vel_y + self.actuator_gain * (action + disturbance) + acc_y = force_y / self.mass + self.vel_y += acc_y * self.dt + + def get_depth(self) -> float: + return self.pos_y + + def get_position(self) -> tuple: + return self.pos_x, self.pos_y + + def reset_state(self): + self.pos_x = 0 + self.pos_y = 0 + self.vel_x = 1 + self.vel_y = 0 + class Trajectory: - def __init__(self, position: np.ndarray): - self.position = position - - def plot(self): - plt.plot(self.position[:, 0], self.position[:, 1]) - plt.show() - - def plot_completed_mission(self, mission: Mission): - x_values = np.arange(len(mission.reference)) - min_depth = np.min(mission.cave_depth) - max_height = np.max(mission.cave_height) - - plt.fill_between(x_values, mission.cave_height, mission.cave_depth, color='blue', alpha=0.3) - plt.fill_between(x_values, mission.cave_depth, min_depth*np.ones(len(x_values)), - color='saddlebrown', alpha=0.3) - plt.fill_between(x_values, max_height*np.ones(len(x_values)), mission.cave_height, - color='saddlebrown', alpha=0.3) - plt.plot(self.position[:, 0], self.position[:, 1], label='Trajectory') - plt.plot(mission.reference, 'r', linestyle='--', label='Reference') - plt.legend(loc='upper right') - plt.show() + def __init__(self, position: np.ndarray): + self.position = position + + def plot(self): + plt.plot(self.position[:, 0], self.position[:, 1]) + plt.show() + + def plot_completed_mission(self, mission: Mission): + x_values = np.arange(len(mission.reference)) + min_depth = np.min(mission.cave_depth) + max_height = np.max(mission.cave_height) + + plt.fill_between(x_values, mission.cave_height, mission.cave_depth, color='blue', alpha=0.3) + plt.fill_between(x_values, mission.cave_depth, min_depth*np.ones(len(x_values)), + color='saddlebrown', alpha=0.3) + plt.fill_between(x_values, max_height*np.ones(len(x_values)), mission.cave_height, + color='saddlebrown', alpha=0.3) + plt.plot(self.position[:, 0], self.position[:, 1], label='Trajectory') + plt.plot(mission.reference, 'r', linestyle='--', label='Reference') + plt.legend(loc='upper right') + plt.show() @dataclass class Mission: - reference: np.ndarray - cave_height: np.ndarray - cave_depth: np.ndarray + reference: np.ndarray + cave_height: np.ndarray + cave_depth: np.ndarray - @classmethod - def random_mission(cls, duration: int, scale: float): - (reference, cave_height, cave_depth) = generate_reference_and_limits(duration, scale) - return cls(reference, cave_height, cave_depth) + @classmethod + def random_mission(cls, duration: int, scale: float): + (reference, cave_height, cave_depth) = generate_reference_and_limits(duration, scale) + return cls(reference, cave_height, cave_depth) - @classmethod - def from_csv(cls, file_name: str): - # You are required to implement this method - pass + @classmethod + def from_csv(cls, file_name: str): + f = pd.read_csv(file_name) + reference = f['reference'].values + cave_height = f['cave_height'].values + cave_depth = f['cave_depth'].values + return cls(reference, cave_height, cave_depth) class ClosedLoop: - def __init__(self, plant: Submarine, controller): - self.plant = plant - self.controller = controller - - def simulate(self, mission: Mission, disturbances: np.ndarray) -> Trajectory: - - T = len(mission.reference) - if len(disturbances) < T: - raise ValueError("Disturbances must be at least as long as mission duration") - - positions = np.zeros((T, 2)) - actions = np.zeros(T) - self.plant.reset_state() - - for t in range(T): - positions[t] = self.plant.get_position() - observation_t = self.plant.get_depth() - # Call your controller here - self.plant.transition(actions[t], disturbances[t]) - - return Trajectory(positions) - - def simulate_with_random_disturbances(self, mission: Mission, variance: float = 0.5) -> Trajectory: - disturbances = np.random.normal(0, variance, len(mission.reference)) - return self.simulate(mission, disturbances) + def __init__(self, plant: Submarine, controller): + self.plant = plant + self.controller = controller + + def simulate(self, mission: Mission, disturbances: np.ndarray) -> Trajectory: + + T = len(mission.reference) + if len(disturbances) < T: + raise ValueError("Disturbances must be at least as long as mission duration") + + positions = np.zeros((T, 2)) + actions = np.zeros(T) + self.plant.reset_state() + + for t in range(T): + positions[t] = self.plant.get_position() + observation_t = self.plant.get_depth() + # Call your controller here + actions[t] = self.controller(positions[t][1], positions[t-1][1], + mission.reference[t], mission.reference[t-1]) + self.plant.transition(actions[t], disturbances[t]) + + return Trajectory(positions) + + def simulate_with_random_disturbances(self, mission: Mission, variance: float = 0.5) -> Trajectory: + disturbances = np.random.normal(0, variance, len(mission.reference)) + return self.simulate(mission, disturbances)