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{
"cells": [
{
"cell_type": "markdown",
"source": [
"# A suggestion of curse changes for a robot sailboat\n",
"\n",
"## Motivation\n",
"\n",
"The goal of this project is to suggest good points to change the curse of a sailboat while going from point $A$ to point $B$.\n",
"\n",
"This project is done as part of the curse \"Maschienen Learning\" at the University of Applied Sciences South Westphalia. The code labeling the was writen by the team of the [Sailing Team Darmstadt e.V.](https://www.st-darmstadt.de/). A society of stundens whose goal it is to build the [\"roBOOTer\"](https://www.st-darmstadt.de/ueber-uns/boote/prototyp-ii/) a fully autonomous sailboat able to cross the atlantic ocean. A technical challenge that was mastered the first time only a few years ago by [a Norwegian team](http://sailbuoy.no/). I myself am part of the Sailing Team Darmstadt e.V. for nearly 10 years.\n",
"\n",
"One of the challenges to solve is a highly efficient way to find a path over the Ocean. The boot is only 2 meters long and powered by solar energy. That makes power a relatively spares commodity.\n",
"\n",
"## Situation as is\n",
"At the moment the pathfinding algorithm generates a set of more or less random routes to the goal. Each route than gets optimized by a gradient decent moving the curse change points over the ocean to find a path with the lowest cost that can be found by following the highest gradient. This is relatively inefficient since only local minima can be found for each of the randomly generated route. The route with the lowest cost for the so optimized route will be chosen as the final route.\n",
"The idea of this project is to ascertain if it is possible to generate a better initial route through a neural network to give the system a kind of good instinct for the initial routes to reduce optimization steps and the number fo routes that need to be calculated to find a good route. Even tough the initial calculation effort could be high the parallel calculation of 40 routes and lots of optimization steps make it possible that some calculation time and therefore energy can be saved this way.\n",
"The idea of this project is to ascertain if it is possible to generate a better initial route through a neural network to give the system a kind of good instinct for the initial routes to reduce optimization steps and the number fo routes that need to be calculated to find a good route. Even tough the initial calculation effort could be high the parallel calculation of 40 routes and lots of optimization steps make it possible that some calculation time and energy can be saved this way.\n",
"\n",
"## The Project\n",
"\n",
"The goal of this project is to calculate a good first route. That allows for some simplifications of this problem.\n",
"\n",
"Some solutions and assumptions can be made.\n",
"1. The route proposed by this network will not be the final route. This make a somewhat accurate solution good enough.\n",
"2. Since the neural network should not learn how to interpret a specific map but the concept of a map the map can be rotated.\n",
"This allows the wind to come always from north.\n",
"3. Since curse speed is only somewhat proportional to the wind speed a final course may change depending on wind speed not only direction.\n",
"These changes are however somewhat small compared to other influences and can hopefully be ignored since later processing of a proposed route should strait these details out.\n",
"4. When the wind comes always from the same direction (After map orientation by wind) map and route can be mirrored allowing to use all data twice for each route.\n",
"5. Scale does only matter when the curvature of the earth has a significant influence. Allowing for different scaling of the problem for additional training data.\n",
"\n",
"Since there is a solution for this project that only needs some optimisation we can used labeled data to train the network.\n",
"\n",
"### The generell structure\n",
"\n",
"Since"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": 451,
"metadata": {
"collapsed": true,
"pycharm": {
"name": "#%%\n"
}
},
"outputs": [],
"source": [
"from typing import Optional, Union, Any\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"from PIL import ImageDraw, Image\n",
"from shapely.geometry import Polygon, Point\n",
"from shapely.ops import unary_union"
]
},
{
"cell_type": "code",
"execution_count": 452,
"outputs": [],
"source": [
"SIZE_INNER = 75\n",
"SIZE_ROUTE = 100\n",
"MIN_DESTINATION_DISTANCE = 25"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 453,
"outputs": [],
"source": [
"# https://stackoverflow.com/questions/16444719/python-numpy-complex-numbers-is-there-a-function-for-polar-to-rectangular-co\n",
"def polar_to_cartesian(\n",
" radii: np.ndarray,\n",
" angles: np.ndarray,\n",
"):\n",
" \"\"\"Transforms polar coordinates into cartesian coordinates.\n",
"\n",
" Args:\n",
" radii: A array of radii.\n",
" angles: A array of angles.\n",
"\n",
" Returns:\n",
" An array of cartesian coordinates.\n",
" \"\"\"\n",
" return radii * np.exp(2j * angles * np.pi)\n",
"\n",
"\n",
"def cartesian_to_polar(\n",
" x: np.ndarray,\n",
"):\n",
" \"\"\"Transforms cartesian coordinates into polar coordinates.\n",
"\n",
" Args:\n",
" x: A set of complex number to be separated into polar coordinates.\n",
"\n",
" Returns:\n",
" An distance array and an angle array.\n",
" \"\"\"\n",
" return abs(x), np.angle(x)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 454,
"outputs": [
{
"data": {
"text/plain": "(array('d', [26.73842492254689, 24.737172393549677, 26.02664010787553, 26.73842492254689]),\n array('d', [37.247658792208725, 35.001670575973314, 32.95721707278007, 37.247658792208725]))"
},
"execution_count": 454,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def random_polygon(\n",
" radius_mean: float = 2,\n",
" radius_sigma: float = 1.5,\n",
"):\n",
" \"\"\"Generates the simplest of polygons, a triangle with a size described by a random polygon.\n",
"\n",
" Args:\n",
" radius_mean: The average radius defining a circumcircle of a triangle.\n",
" radius_sigma: The variance of a radius defining a circumcircle of a triangle.\n",
"\n",
" Returns:\n",
" A single triangle.\n",
" \"\"\"\n",
" array = polar_to_cartesian(\n",
" np.random.lognormal(radius_mean, radius_sigma), np.random.rand(3)\n",
" )\n",
" offset = np.random.randint(low=-SIZE_ROUTE, high=SIZE_ROUTE, size=(2,))\n",
" return_values = np.zeros((3, 2), dtype=float)\n",
" # return_values[1, :] = np.real(offset)\n",
" return_values[:] = offset\n",
" return_values[:, :] += np.array((np.real(array), np.imag(array))).T\n",
" return Polygon(return_values)\n",
" # return np.array( + offset[0], np.imag(array) + offset[1])\n",
"\n",
"\n",
"random_polygon().exterior.xy"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 455,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"POLYGON ((-86.43301358227995 93.04181448099787, -74.21505182510836 90.98362331906574, -82.7595147462848 95.20011372852156, -86.43301358227995 93.04181448099787))\n"
]
}
],
"source": [
"print(random_polygon())"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 456,
"outputs": [],
"source": [
"def generate_obstacles(\n",
" seed=None,\n",
" number_of_polygons: int = 50,\n",
" radius_mean: float = 2,\n",
" radius_sigma: float = 1,\n",
"):\n",
" \"\"\"Generates a set of obstacles from a union of triangles.\n",
"\n",
" The union of triangles meas that if polygons overlap o polygon containing the union of those polygons is returned.\n",
" Args:\n",
" seed: A seed to generate a set of obstacles from.\n",
" number_of_polygons: The number of polygons that should be drawn.\n",
" radius_mean: The average radius defining a circumcircle of an obstacle triangle.\n",
" radius_sigma: The variance of a radius defining a circumcircle of an obstacle triangle.\n",
"\n",
" Returns:\n",
" A list of unified obstacles.\n",
" \"\"\"\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
" polygons = []\n",
" for _ in range(number_of_polygons):\n",
" poly = random_polygon(radius_mean, radius_sigma)\n",
" if poly.contains(Point(0, 0)):\n",
" continue\n",
" polygons.append(poly)\n",
" polygon_list = list(unary_union(polygons).geoms)\n",
" return polygon_list"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 457,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"POINT (-61 31)\n"
]
}
],
"source": [
"def generate_destination(obstacles: list[Polygon], seed: Optional[int] = None) -> Point:\n",
" \"\"\"Generates for a map.\n",
"\n",
" Can be used to generate a valid destination for list of obstacles.\n",
" Args:\n",
" obstacles: A list of obstacles.\n",
" seed: The seed determining the point.\n",
"\n",
" Returns:\n",
" A goal that should be reached by the ship.\n",
" \"\"\"\n",
" # sets the seed\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
"\n",
" # generates the point\n",
" point: Optional[Point] = None\n",
" while (\n",
" point is None\n",
" or abs(point.x) < MIN_DESTINATION_DISTANCE\n",
" or abs(point.y) < MIN_DESTINATION_DISTANCE\n",
" or any(obstacle.contains(point) for obstacle in obstacles)\n",
" ):\n",
" point = Point(np.random.randint(-SIZE_INNER, SIZE_INNER, size=(2,), dtype=int))\n",
" return point\n",
"\n",
"\n",
"print(generate_destination(generate_obstacles(42), 42))"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 469,
"outputs": [
{
"data": {
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{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": 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truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": 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truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": 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truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def plot_situation(\n",
" obstacles: list[Polygon],\n",
" destination: Point,\n",
" obstacle_color: Optional[str] = None,\n",
" legend: bool = True,\n",
") -> None:\n",
" \"\"\"PLots the obstacles into a matplotlib plot.\n",
"\n",
" Args:\n",
" obstacles: A list of obstacles.\n",
" destination: The destination that should be reached by the boat.\n",
" obstacle_color: The color the obstacles should have. Can be None.\n",
" If none all obstacles will have different colors.\n",
" legend: If true plots a legend.\n",
" Returns:\n",
" None\n",
" \"\"\"\n",
" plt.figure(figsize=(8, 8))\n",
" plt.axis([-SIZE_ROUTE, SIZE_ROUTE, -SIZE_ROUTE, SIZE_ROUTE])\n",
" plt.title(\"Test\")\n",
" for polygon in obstacles:\n",
" if obstacle_color is not None:\n",
" plt.fill(*polygon.exterior.xy, color=obstacle_color, label=\"Obstacle\")\n",
" else:\n",
" plt.fill(*polygon.exterior.xy)\n",
" plt.scatter(*destination.xy, marker=\"X\", color=\"green\", label=\"Destination\")\n",
" plt.scatter(0, 0, marker=\"o\", color=\"green\", label=\"Start\")\n",
" # https://www.geeksforgeeks.org/matplotlib-pyplot-arrow-in-python/\n",
" plt.arrow(\n",
" 0,\n",
" int(SIZE_ROUTE * 0.9),\n",
" 0,\n",
" -int(SIZE_ROUTE * 0.1),\n",
" head_width=10,\n",
" width=4,\n",
" label=\"wind (3Bft)\",\n",
" )\n",
" if legend:\n",
" # https://stackoverflow.com/questions/13588920/stop-matplotlib-repeating-labels-in-legend\n",
" handles, labels = plt.gca().get_legend_handles_labels()\n",
" by_label = dict(zip(labels, handles))\n",
" plt.legend(by_label.values(), by_label.keys())\n",
" plt.show()\n",
"\n",
"\n",
"for s in range(20):\n",
" o = generate_obstacles(s)\n",
" plot_situation(o, generate_destination(o, s), \"RED\")"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 459,
"outputs": [
{
"data": {
"text/plain": "<PIL.Image.Image image mode=RGB size=200x200>",
"image/png": "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\n"
},
"execution_count": 459,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def generate_image_from_map(\n",
" obstacles: list[Polygon], destination: Point, solution\n",
") -> Image:\n",
" \"\"\"Generate an image from the map.\n",
"\n",
" Can be used to feed an ANN.\n",
" - Obstacles are marked as reed.\n",
" - The destination is marked as green.\n",
" - The points where the route will likely change are blue.\n",
"\n",
" Args:\n",
" obstacles: A list of obstacles as shapely Polygons.\n",
" destination: A destination that should be navigated to.\n",
" \"\"\"\n",
" img = Image.new(\n",
" \"RGB\",\n",
" (SIZE_ROUTE * 2, SIZE_ROUTE * 2),\n",
" \"#ffffff\",\n",
" )\n",
" draw = ImageDraw.Draw(img)\n",
" for polygon in obstacles:\n",
" draw.polygon(\n",
" list(np.dstack(polygon.exterior.xy).reshape((-1)) + SIZE_ROUTE),\n",
" fill=\"#FF0000\",\n",
" outline=\"#FF0000\",\n",
" )\n",
" img.putpixel((int(destination.x) + 100, int(destination.y) + 100), (0, 0xFF, 0))\n",
" return img\n",
"\n",
"\n",
"o = generate_obstacles(42)\n",
"g = generate_destination(o, 42)\n",
"og_img = generate_image_from_map(o, g, None)\n",
"og_img"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 460,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.97 ms ± 24.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"def generate_all_to_series(\n",
" seed: Optional[int] = None, image: bool = False\n",
") -> pd.Series:\n",
" \"\"\"Generates everything and aggregates all data into a `pd:Series`.\n",
"\n",
" Args:\n",
" seed:The seed that should be used to generate map and destination.\n",
" image: If an image should be generated or if that should be postponed to save memory.\n",
" Returns:\n",
" Contains a `pd.Series`containing the following.\n",
" - The seed tha generated the map.\n",
" - The destination in x\n",
" - The destination in y\n",
" - A list of Obstacle polygons.\n",
" - The route generated for this map by the roBOOTer navigation system.\n",
" - Optionally the image containing all the information. Can be generated at a later date without the fear for a loss of accuracy.\n",
" \"\"\"\n",
" obstacles = generate_obstacles(seed)\n",
" destination = generate_destination(obstacles, seed)\n",
"\n",
" return pd.Series(\n",
" data={\n",
" \"seed\": str(seed),\n",
" \"obstacles\": obstacles,\n",
" \"destination_x\": destination.x,\n",
" \"destination_y\": destination.y,\n",
" \"image\": generate_image_from_map(obstacles, destination, None)\n",
" if image\n",
" else pd.NA,\n",
" }\n",
" )\n",
"\n",
"\n",
"%timeit generate_all_to_series()"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 461,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7.09 s ± 161 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
},
{
"data": {
"text/plain": " obstacles destination_x \\\nseed \n0 [POLYGON ((-93.1050012242828 -66.3114919371247... -28.0 \n1 [POLYGON ((-81.48957922766184 -77.835510803987... -38.0 \n2 [POLYGON ((-85.70808367996737 -96.461421605594... -53.0 \n3 [POLYGON ((-72.36680200571271 -83.042653386020... -75.0 \n4 [POLYGON ((-49.01542037068429 -91.156604355820... 47.0 \n... ... ... \n995 [POLYGON ((-50.76852976356937 -111.76285175833... 53.0 \n996 [POLYGON ((-72.9562194541358 -97.5919820487205... -66.0 \n997 [POLYGON ((-40.30122912398245 -93.905261647772... 41.0 \n998 [POLYGON ((-107.12872081817716 -106.2700055400... -45.0 \n999 [POLYGON ((-65.09067978612013 -95.812774257958... -59.0 \n\n destination_y image \nseed \n0 42.0 <NA> \n1 65.0 <NA> \n2 -32.0 <NA> \n3 -54.0 <NA> \n4 54.0 <NA> \n... ... ... \n995 52.0 <NA> \n996 73.0 <NA> \n997 51.0 <NA> \n998 64.0 <NA> \n999 42.0 <NA> \n\n[1000 rows x 4 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>obstacles</th>\n <th>destination_x</th>\n <th>destination_y</th>\n <th>image</th>\n </tr>\n <tr>\n <th>seed</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>[POLYGON ((-93.1050012242828 -66.3114919371247...</td>\n <td>-28.0</td>\n <td>42.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>1</th>\n <td>[POLYGON ((-81.48957922766184 -77.835510803987...</td>\n <td>-38.0</td>\n <td>65.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>2</th>\n <td>[POLYGON ((-85.70808367996737 -96.461421605594...</td>\n <td>-53.0</td>\n <td>-32.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>3</th>\n <td>[POLYGON ((-72.36680200571271 -83.042653386020...</td>\n <td>-75.0</td>\n <td>-54.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>4</th>\n <td>[POLYGON ((-49.01542037068429 -91.156604355820...</td>\n <td>47.0</td>\n <td>54.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>995</th>\n <td>[POLYGON ((-50.76852976356937 -111.76285175833...</td>\n <td>53.0</td>\n <td>52.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>996</th>\n <td>[POLYGON ((-72.9562194541358 -97.5919820487205...</td>\n <td>-66.0</td>\n <td>73.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>997</th>\n <td>[POLYGON ((-40.30122912398245 -93.905261647772...</td>\n <td>41.0</td>\n <td>51.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>998</th>\n <td>[POLYGON ((-107.12872081817716 -106.2700055400...</td>\n <td>-45.0</td>\n <td>64.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>999</th>\n <td>[POLYGON ((-65.09067978612013 -95.812774257958...</td>\n <td>-59.0</td>\n <td>42.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n </tbody>\n</table>\n<p>1000 rows × 4 columns</p>\n</div>"
},
"execution_count": 461,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%timeit pd.DataFrame([generate_all_to_series(i) for i in range(1000)]).set_index(\"seed\")\n",
"df = pd.DataFrame([generate_all_to_series(i) for i in range(1000)]).set_index(\"seed\")\n",
"df.to_pickle(\"test.pickle\")\n",
"df"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 462,
"outputs": [
{
"data": {
"text/plain": " obstacles destination_x \\\nseed \n0 [POLYGON ((-93.1050012242828 -66.3114919371247... -28.0 \n1 [POLYGON ((-81.48957922766184 -77.835510803987... -38.0 \n2 [POLYGON ((-85.70808367996737 -96.461421605594... -53.0 \n3 [POLYGON ((-72.36680200571271 -83.042653386020... -75.0 \n4 [POLYGON ((-49.01542037068429 -91.156604355820... 47.0 \n... ... ... \n995 [POLYGON ((-50.76852976356937 -111.76285175833... 53.0 \n996 [POLYGON ((-72.9562194541358 -97.5919820487205... -66.0 \n997 [POLYGON ((-40.30122912398245 -93.905261647772... 41.0 \n998 [POLYGON ((-107.12872081817716 -106.2700055400... -45.0 \n999 [POLYGON ((-65.09067978612013 -95.812774257958... -59.0 \n\n destination_y image \nseed \n0 42.0 <NA> \n1 65.0 <NA> \n2 -32.0 <NA> \n3 -54.0 <NA> \n4 54.0 <NA> \n... ... ... \n995 52.0 <NA> \n996 73.0 <NA> \n997 51.0 <NA> \n998 64.0 <NA> \n999 42.0 <NA> \n\n[1000 rows x 4 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>obstacles</th>\n <th>destination_x</th>\n <th>destination_y</th>\n <th>image</th>\n </tr>\n <tr>\n <th>seed</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>[POLYGON ((-93.1050012242828 -66.3114919371247...</td>\n <td>-28.0</td>\n <td>42.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>1</th>\n <td>[POLYGON ((-81.48957922766184 -77.835510803987...</td>\n <td>-38.0</td>\n <td>65.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>2</th>\n <td>[POLYGON ((-85.70808367996737 -96.461421605594...</td>\n <td>-53.0</td>\n <td>-32.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>3</th>\n <td>[POLYGON ((-72.36680200571271 -83.042653386020...</td>\n <td>-75.0</td>\n <td>-54.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>4</th>\n <td>[POLYGON ((-49.01542037068429 -91.156604355820...</td>\n <td>47.0</td>\n <td>54.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>995</th>\n <td>[POLYGON ((-50.76852976356937 -111.76285175833...</td>\n <td>53.0</td>\n <td>52.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>996</th>\n <td>[POLYGON ((-72.9562194541358 -97.5919820487205...</td>\n <td>-66.0</td>\n <td>73.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>997</th>\n <td>[POLYGON ((-40.30122912398245 -93.905261647772...</td>\n <td>41.0</td>\n <td>51.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>998</th>\n <td>[POLYGON ((-107.12872081817716 -106.2700055400...</td>\n <td>-45.0</td>\n <td>64.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n <tr>\n <th>999</th>\n <td>[POLYGON ((-65.09067978612013 -95.812774257958...</td>\n <td>-59.0</td>\n <td>42.0</td>\n <td>&lt;NA&gt;</td>\n </tr>\n </tbody>\n</table>\n<p>1000 rows × 4 columns</p>\n</div>"
},
"execution_count": 462,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = pd.read_pickle(\"test.pickle\")\n",
"df2"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"https://programtalk.com/python-examples/PIL.ImageDraw.Draw.polygon/)\n",
"https://stackoverflow.com/questions/3654289/scipy-create-2d-polygon-mask"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
experiments/ExReadMe.md
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Whitespace-only changes.
experiments/Experiments.ipynb
+632
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@@ -0,0 +1,632 @@
{
"cells": [
{
"cell_type": "markdown",
"source": [
"# A suggestion of curse changes for a robot sailboat\n",
"\n",
"## Motivation\n",
"\n",
"The goal of this project is to suggest good points to change the curse of a sailboat while going from point $A$ to point $B$.\n",
"\n",
"This project is done as part of the curse \"Maschienen Learning\" at the University of Applied Sciences South Westphalia. The code labeling the was writen by the team of the [Sailing Team Darmstadt e.V.](https://www.st-darmstadt.de/). A society of stundens whose goal it is to build the [\"roBOOTer\"](https://www.st-darmstadt.de/ueber-uns/boote/prototyp-ii/) a fully autonomous sailboat able to cross the atlantic ocean. A technical challenge that was mastered the first time only a few years ago by [a Norwegian team](http://sailbuoy.no/). I myself am part of the Sailing Team Darmstadt e.V. for nearly 10 years.\n",
"\n",
"One of the challenges to solve is a highly efficient way to find a path over the Ocean. The boot is only 2 meters long and powered by solar energy. That makes power a relatively spares commodity.\n",
"\n",
"## Situation as is\n",
"At the moment the pathfinding algorithm generates a set of more or less random routes to the goal. Each route than gets optimized by a gradient decent moving the curse change points over the ocean to find a path with the lowest cost that can be found by following the highest gradient. This is relatively inefficient since only local minima can be found for each of the randomly generated route. The route with the lowest cost for the so optimized route will be chosen as the final route.\n",
"The idea of this project is to ascertain if it is possible to generate a better initial route through a neural network to give the system a kind of good instinct for the initial routes to reduce optimization steps and the number fo routes that need to be calculated to find a good route. Even tough the initial calculation effort could be high the parallel calculation of 40 routes and lots of optimization steps make it possible that some calculation time and therefore energy can be saved this way.\n",
"The idea of this project is to ascertain if it is possible to generate a better initial route through a neural network to give the system a kind of good instinct for the initial routes to reduce optimization steps and the number fo routes that need to be calculated to find a good route. Even tough the initial calculation effort could be high the parallel calculation of 40 routes and lots of optimization steps make it possible that some calculation time and energy can be saved this way.\n",
"\n",
"## The Project\n",
"\n",
"The goal of this project is to calculate a good first route. That allows for some simplifications of this problem.\n",
"\n",
"Some solutions and assumptions can be made.\n",
"1. The route proposed by this network will not be the final route. This make a somewhat accurate solution good enough.\n",
"2. Since the neural network should not learn how to interpret a specific map but the concept of a map the map can be rotated.\n",
"This allows the wind to come always from north.\n",
"3. Since curse speed is only somewhat proportional to the wind speed a final course may change depending on wind speed not only direction.\n",
"These changes are however somewhat small compared to other influences and can hopefully be ignored since later processing of a proposed route should strait these details out.\n",
"4. When the wind comes always from the same direction (After map orientation by wind) map and route can be mirrored allowing to use all data twice for each route.\n",
"5. Scale does only matter when the curvature of the earth has a significant influence. Allowing for different scaling of the problem for additional training data.\n",
"\n",
"Since there is a solution for this project that only needs some optimisation we can used labeled data to train the network.\n",
"\n",
"### The generell structure\n",
"\n",
"Since"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"pycharm": {
"name": "#%%\n",
"is_executing": true
}
},
"outputs": [],
"source": [
"from typing import Optional\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"from PIL import ImageDraw, Image\n",
"from shapely.geometry import Polygon, Point\n",
"from shapely.ops import unary_union"
]
},
{
"cell_type": "code",
"execution_count": 76,
"outputs": [],
"source": [
"SIZE_INNER = 75\n",
"SIZE_ROUTE = 100\n",
"MIN_DESTINATION_DISTANCE = 25"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 77,
"outputs": [],
"source": [
"# https://stackoverflow.com/questions/16444719/python-numpy-complex-numbers-is-there-a-function-for-polar-to-rectangular-co\n",
"def polar_to_cartesian(\n",
" radii: np.ndarray,\n",
" angles: np.ndarray,\n",
"):\n",
" \"\"\"Transforms polar coordinates into cartesian coordinates.\n",
"\n",
" Args:\n",
" radii: A array of radii.\n",
" angles: A array of angles.\n",
"\n",
" Returns:\n",
" An array of cartesian coordinates.\n",
" \"\"\"\n",
" return radii * np.exp(2j * angles * np.pi)\n",
"\n",
"\n",
"def cartesian_to_polar(\n",
" x: np.ndarray,\n",
"):\n",
" \"\"\"Transforms cartesian coordinates into polar coordinates.\n",
"\n",
" Args:\n",
" x: A set of complex number to be separated into polar coordinates.\n",
"\n",
" Returns:\n",
" An distance array and an angle array.\n",
" \"\"\"\n",
" return abs(x), np.angle(x)"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 78,
"outputs": [
{
"data": {
"text/plain": "(array('d', [58.14925495607776, 57.991505776535874, 57.76398376244, 56.384785221067744, 55.81452229141463, 56.285256940049436, 56.785512495130604, 58.14925495607776]),\n array('d', [-62.57469140842694, -62.279867618259566, -62.04187518408968, -61.94019571244569, -62.6896502705296, -63.99539776490992, -64.20651127735718, -62.57469140842694]))"
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def random_polygon(\n",
" radius_mean: float = 2,\n",
" radius_sigma: float = 1.5,\n",
"):\n",
" \"\"\"Generates the simplest of polygons, a triangle with a size described by a random polygon.\n",
"\n",
" Args:\n",
" radius_mean: The average radius defining a circumcircle of a triangle.\n",
" radius_sigma: The variance of a radius defining a circumcircle of a triangle.\n",
"\n",
" Returns:\n",
" A single triangle.\n",
" \"\"\"\n",
" number_of_corners = np.random.randint(3, 10)\n",
" array = polar_to_cartesian(\n",
" np.random.lognormal(radius_mean, radius_sigma),\n",
" np.sort(np.random.rand(number_of_corners)),\n",
" )\n",
" offset = np.random.randint(low=-SIZE_ROUTE, high=SIZE_ROUTE, size=(2,))\n",
" return_values = np.zeros((number_of_corners, 2), dtype=float)\n",
" # return_values[1, :] = np.real(offset)\n",
" return_values[:] = offset\n",
" return_values[:, :] += np.array((np.real(array), np.imag(array))).T\n",
" return Polygon(return_values)\n",
" # return np.array( + offset[0], np.imag(array) + offset[1])\n",
"\n",
"\n",
"random_polygon().exterior.xy"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 79,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"POLYGON ((64.24234834022612 114.96629329424587, 62.58155390362512 115.71472980681563, -73.35243626054171 120.99282453832782, -193.33402301375247 -80.4360840218972, 64.24234834022612 114.96629329424587))\n"
]
}
],
"source": [
"print(random_polygon())"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 80,
"outputs": [],
"source": [
"def generate_obstacles(\n",
" seed=None,\n",
" number_of_polygons: int = 40,\n",
" radius_mean: float = 2,\n",
" radius_sigma: float = 1,\n",
"):\n",
" \"\"\"Generates a set of obstacles from a union of triangles.\n",
"\n",
" The union of triangles meas that if polygons overlap o polygon containing the union of those polygons is returned.\n",
" Args:\n",
" seed: A seed to generate a set of obstacles from.\n",
" number_of_polygons: The number of polygons that should be drawn.\n",
" radius_mean: The average radius defining a circumcircle of an obstacle triangle.\n",
" radius_sigma: The variance of a radius defining a circumcircle of an obstacle triangle.\n",
"\n",
" Returns:\n",
" A list of unified obstacles.\n",
" \"\"\"\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
" polygons = []\n",
" for _ in range(number_of_polygons):\n",
" poly = random_polygon(radius_mean, radius_sigma)\n",
" if poly.contains(Point(0, 0)):\n",
" continue\n",
" polygons.append(poly)\n",
" polygon_list = list(unary_union(polygons).geoms)\n",
" return polygon_list"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 81,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"POINT (-61 31)\n"
]
}
],
"source": [
"def generate_destination(obstacles: list[Polygon], seed: Optional[int] = None) -> Point:\n",
" \"\"\"Generates for a map.\n",
"\n",
" Can be used to generate a valid destination for list of obstacles.\n",
" Args:\n",
" obstacles: A list of obstacles.\n",
" seed: The seed determining the point.\n",
"\n",
" Returns:\n",
" A goal that should be reached by the ship.\n",
" \"\"\"\n",
" # sets the seed\n",
" if seed is not None:\n",
" np.random.seed(seed)\n",
"\n",
" # generates the point\n",
" point: Optional[Point] = None\n",
" while (\n",
" point is None\n",
" or abs(point.x) < MIN_DESTINATION_DISTANCE\n",
" or abs(point.y) < MIN_DESTINATION_DISTANCE\n",
" or any(obstacle.contains(point) for obstacle in obstacles)\n",
" ):\n",
" point = Point(np.random.randint(-SIZE_INNER, SIZE_INNER, size=(2,), dtype=int))\n",
" return point\n",
"\n",
"\n",
"print(generate_destination(generate_obstacles(42), 42))"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 82,
"outputs": [
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": 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truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"text/plain": "<Figure size 576x576 with 1 Axes>",
"image/png": "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 truncated
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def plot_situation(\n",
" obstacles: list[Polygon],\n",
" destination: Point,\n",
" obstacle_color: Optional[str] = None,\n",
" legend: bool = True,\n",
") -> None:\n",
" \"\"\"PLots the obstacles into a matplotlib plot.\n",
"\n",
" Args:\n",
" obstacles: A list of obstacles.\n",
" destination: The destination that should be reached by the boat.\n",
" obstacle_color: The color the obstacles should have. Can be None.\n",
" If none all obstacles will have different colors.\n",
" legend: If true plots a legend.\n",
" Returns:\n",
" None\n",
" \"\"\"\n",
" plt.figure(figsize=(8, 8))\n",
" plt.axis([-SIZE_ROUTE, SIZE_ROUTE, -SIZE_ROUTE, SIZE_ROUTE])\n",
" plt.title(\"Test\")\n",
" for polygon in obstacles:\n",
" if obstacle_color is not None:\n",
" plt.fill(*polygon.exterior.xy, color=obstacle_color, label=\"Obstacle\")\n",
" else:\n",
" plt.fill(*polygon.exterior.xy)\n",
" plt.scatter(*destination.xy, marker=\"X\", color=\"green\", label=\"Destination\")\n",
" plt.scatter(0, 0, marker=\"o\", color=\"green\", label=\"Start\")\n",
" # https://www.geeksforgeeks.org/matplotlib-pyplot-arrow-in-python/\n",
" plt.arrow(\n",
" 0,\n",
" int(SIZE_ROUTE * 0.9),\n",
" 0,\n",
" -int(SIZE_ROUTE * 0.1),\n",
" head_width=10,\n",
" width=4,\n",
" label=\"Wind (3Bft)\",\n",
" )\n",
" if legend:\n",
" # https://stackoverflow.com/questions/13588920/stop-matplotlib-repeating-labels-in-legend\n",
" handles, labels = plt.gca().get_legend_handles_labels()\n",
" by_label = dict(zip(labels, handles))\n",
" plt.legend(by_label.values(), by_label.keys())\n",
" plt.show()\n",
"\n",
"\n",
"for s in range(5):\n",
" o = generate_obstacles(s)\n",
" plot_situation(o, generate_destination(o, s), \"RED\")"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 83,
"outputs": [
{
"data": {
"text/plain": "<PIL.Image.Image image mode=RGB size=200x200>",
"image/png": "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\n"
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def generate_image_from_map(\n",
" obstacles: list[Polygon], destination: Point, solution: list[Point]\n",
") -> Image:\n",
" \"\"\"Generate an image from the map.\n",
"\n",
" Can be used to feed an ANN.\n",
" - Obstacles are marked as reed.\n",
" - The destination is marked as green.\n",
" - The points where the route will likely change are blue.\n",
"\n",
" Args:\n",
" obstacles: A list of obstacles as shapely Polygons.\n",
" destination: A destination that should be navigated to.\n",
" \"\"\"\n",
" img = Image.new(\n",
" \"RGB\",\n",
" (SIZE_ROUTE * 2, SIZE_ROUTE * 2),\n",
" \"#ffffff\",\n",
" )\n",
" draw = ImageDraw.Draw(img)\n",
" for polygon in obstacles:\n",
" draw.polygon(\n",
" list(np.dstack(polygon.exterior.xy).reshape((-1)) + SIZE_ROUTE),\n",
" fill=\"#FF0000\",\n",
" outline=\"#FF0000\",\n",
" )\n",
" img.putpixel((int(destination.x) + 100, int(destination.y) + 100), (0, 0xFF, 0))\n",
" return img\n",
"\n",
"\n",
"o = generate_obstacles(42)\n",
"g = generate_destination(o, 42)\n",
"og_img = generate_image_from_map(o, g, None)\n",
"og_img"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 84,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.39 ms ± 209 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
]
}
],
"source": [
"def generate_all_to_series(\n",
" seed: Optional[int] = None, image: bool = False\n",
") -> pd.Series:\n",
" \"\"\"Generates everything and aggregates all data into a `pd:Series`.\n",
"\n",
" Args:\n",
" seed:The seed that should be used to generate map and destination.\n",
" image: If an image should be generated or if that should be postponed to save memory.\n",
" Returns:\n",
" Contains a `pd.Series`containing the following.\n",
" - The seed tha generated the map.\n",
" - The destination in x\n",
" - The destination in y\n",
" - A list of Obstacle polygons.\n",
" - The route generated for this map by the roBOOTer navigation system.\n",
" - Optionally the image containing all the information. Can be generated at a later date without the fear for a loss of accuracy.\n",
" \"\"\"\n",
" obstacles = generate_obstacles(seed)\n",
" destination = generate_destination(obstacles, seed)\n",
"\n",
" return pd.Series(\n",
" data={\n",
" \"seed\": str(seed),\n",
" \"obstacles\": obstacles,\n",
" \"destination_x\": destination.x,\n",
" \"destination_y\": destination.y,\n",
" \"image\": generate_image_from_map(obstacles, destination, None)\n",
" if image\n",
" else pd.NA,\n",
" \"route\": None,\n",
" }\n",
" )\n",
"\n",
"\n",
"%timeit generate_all_to_series()"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 85,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"6.22 s ± 197 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
},
{
"data": {
"text/plain": " obstacles destination_x \\\nseed \n0 [POLYGON ((-17.62168766659423 -98.369266220295... -66.0 \n1 [POLYGON ((-97.82715137072381 -82.221167700698... -38.0 \n2 [POLYGON ((-46.23706006792075 -76.756994820638... 73.0 \n3 [POLYGON ((-7.4210414351932155 -83.11109606339... 31.0 \n4 [POLYGON ((-77.97638439917915 -70.239097296919... 47.0 \n... ... ... \n995 [POLYGON ((-58.363834699771374 -78.10250502922... 53.0 \n996 [POLYGON ((-45.38105849246964 -86.568664678920... -66.0 \n997 [POLYGON ((-82.90222667221566 -92.864813148784... 41.0 \n998 [POLYGON ((-37.1039416590347 -88.1790371955828... -45.0 \n999 [POLYGON ((-119.59353826736813 -30.54685916387... 74.0 \n\n destination_y image route \nseed \n0 -54.0 <NA> None \n1 65.0 <NA> None \n2 49.0 <NA> None \n3 56.0 <NA> None \n4 54.0 <NA> None \n... ... ... ... \n995 52.0 <NA> None \n996 73.0 <NA> None \n997 51.0 <NA> None \n998 64.0 <NA> None \n999 -25.0 <NA> None \n\n[1000 rows x 5 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>obstacles</th>\n <th>destination_x</th>\n <th>destination_y</th>\n <th>image</th>\n <th>route</th>\n </tr>\n <tr>\n <th>seed</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>[POLYGON ((-17.62168766659423 -98.369266220295...</td>\n <td>-66.0</td>\n <td>-54.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>1</th>\n <td>[POLYGON ((-97.82715137072381 -82.221167700698...</td>\n <td>-38.0</td>\n <td>65.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>2</th>\n <td>[POLYGON ((-46.23706006792075 -76.756994820638...</td>\n <td>73.0</td>\n <td>49.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>3</th>\n <td>[POLYGON ((-7.4210414351932155 -83.11109606339...</td>\n <td>31.0</td>\n <td>56.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>4</th>\n <td>[POLYGON ((-77.97638439917915 -70.239097296919...</td>\n <td>47.0</td>\n <td>54.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>995</th>\n <td>[POLYGON ((-58.363834699771374 -78.10250502922...</td>\n <td>53.0</td>\n <td>52.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>996</th>\n <td>[POLYGON ((-45.38105849246964 -86.568664678920...</td>\n <td>-66.0</td>\n <td>73.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>997</th>\n <td>[POLYGON ((-82.90222667221566 -92.864813148784...</td>\n <td>41.0</td>\n <td>51.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>998</th>\n <td>[POLYGON ((-37.1039416590347 -88.1790371955828...</td>\n <td>-45.0</td>\n <td>64.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>999</th>\n <td>[POLYGON ((-119.59353826736813 -30.54685916387...</td>\n <td>74.0</td>\n <td>-25.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n </tbody>\n</table>\n<p>1000 rows × 5 columns</p>\n</div>"
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%timeit pd.DataFrame([generate_all_to_series(i) for i in range(1000)]).set_index(\"seed\")\n",
"df = pd.DataFrame([generate_all_to_series(i) for i in range(1000)]).set_index(\"seed\")\n",
"df.to_pickle(\"test.pickle\")\n",
"df"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "code",
"execution_count": 86,
"outputs": [
{
"data": {
"text/plain": " obstacles destination_x \\\nseed \n0 [POLYGON ((-17.62168766659423 -98.369266220295... -66.0 \n1 [POLYGON ((-97.82715137072381 -82.221167700698... -38.0 \n2 [POLYGON ((-46.23706006792075 -76.756994820638... 73.0 \n3 [POLYGON ((-7.4210414351932155 -83.11109606339... 31.0 \n4 [POLYGON ((-77.97638439917915 -70.239097296919... 47.0 \n... ... ... \n995 [POLYGON ((-58.363834699771374 -78.10250502922... 53.0 \n996 [POLYGON ((-45.38105849246964 -86.568664678920... -66.0 \n997 [POLYGON ((-82.90222667221566 -92.864813148784... 41.0 \n998 [POLYGON ((-37.1039416590347 -88.1790371955828... -45.0 \n999 [POLYGON ((-119.59353826736813 -30.54685916387... 74.0 \n\n destination_y image route \nseed \n0 -54.0 <NA> None \n1 65.0 <NA> None \n2 49.0 <NA> None \n3 56.0 <NA> None \n4 54.0 <NA> None \n... ... ... ... \n995 52.0 <NA> None \n996 73.0 <NA> None \n997 51.0 <NA> None \n998 64.0 <NA> None \n999 -25.0 <NA> None \n\n[1000 rows x 5 columns]",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>obstacles</th>\n <th>destination_x</th>\n <th>destination_y</th>\n <th>image</th>\n <th>route</th>\n </tr>\n <tr>\n <th>seed</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>[POLYGON ((-17.62168766659423 -98.369266220295...</td>\n <td>-66.0</td>\n <td>-54.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>1</th>\n <td>[POLYGON ((-97.82715137072381 -82.221167700698...</td>\n <td>-38.0</td>\n <td>65.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>2</th>\n <td>[POLYGON ((-46.23706006792075 -76.756994820638...</td>\n <td>73.0</td>\n <td>49.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>3</th>\n <td>[POLYGON ((-7.4210414351932155 -83.11109606339...</td>\n <td>31.0</td>\n <td>56.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>4</th>\n <td>[POLYGON ((-77.97638439917915 -70.239097296919...</td>\n <td>47.0</td>\n <td>54.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>995</th>\n <td>[POLYGON ((-58.363834699771374 -78.10250502922...</td>\n <td>53.0</td>\n <td>52.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>996</th>\n <td>[POLYGON ((-45.38105849246964 -86.568664678920...</td>\n <td>-66.0</td>\n <td>73.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>997</th>\n <td>[POLYGON ((-82.90222667221566 -92.864813148784...</td>\n <td>41.0</td>\n <td>51.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>998</th>\n <td>[POLYGON ((-37.1039416590347 -88.1790371955828...</td>\n <td>-45.0</td>\n <td>64.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n <tr>\n <th>999</th>\n <td>[POLYGON ((-119.59353826736813 -30.54685916387...</td>\n <td>74.0</td>\n <td>-25.0</td>\n <td>&lt;NA&gt;</td>\n <td>None</td>\n </tr>\n </tbody>\n</table>\n<p>1000 rows × 5 columns</p>\n</div>"
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = pd.read_pickle(\"test.pickle\")\n",
"df2"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"https://programtalk.com/python-examples/PIL.ImageDraw.Draw.polygon/)\n",
"https://stackoverflow.com/questions/3654289/scipy-create-2d-polygon-mask"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "markdown",
"source": [
"Ich würde auch zu 1. tendieren, stimme Ihnen aber zu, dass das Thema sehr umfangreich ist. Könnte man sich nicht einen Teilbereich herauspicken? Ich verstehe nicht viel vom Segeln, daher lassen Sie mich kurz zusammenfassen, was Sie vorhaben: - Sie generieren Trainingsdaten mit dem existierenden aber langsamen GD Algorithmus. Ich nehme an, es handelt sich um lokale Routen in einem relativ kleinen Kartenausschnitt. Lässt es die Laufzeit zu, dass Sie eine große Menge an Routen berechnen. - Sie haben dann eine Karte und als Ausgabe eine Liste der Wendepunkte - Warum wollen Sie daraus eine Heatmap berechnen? Diesen Schritt habe ich noch nicht verstanden - Wenn Sie aus einer Karte eine Heatmap trainieren wollen und dafür genügend Beispiele haben, könnnten GANs hilfreich sein: https://arxiv.org/abs/1611.07004 Ich würde Ihnen raten, das Problem möglichst so zu reduzieren, dass es im Rahmen des Moduls noch handhabbar bleibt. Alles Weitere kann man sich auch für spätere Arbeiten aufbewahren. Das 2. Thema ist auch ok. Aber vielleicht nicht ganz so spannend. Ich überlasse Ihnen die Entscheidung. Freundliche Grüße Heiner Giefers"
],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%% md\n"
}
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [],
"metadata": {
"collapsed": false,
"pycharm": {
"name": "#%%\n"
}
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
pyrate
+1 -1
Submodule pyrate updated: 1415645163...d148e65134.
ros.dockerfile
-26
View File
@@ -1,26 +0,0 @@
FROM ros:humble-ros-base-jammy
# Resolve APT dependencies
RUN apt-get update -qq
RUN apt-get install apt-utils software-properties-common -yqq
RUN add-apt-repository ppa:antiprism/ppa -y
RUN apt-get install apt-utils software-properties-common python3-pip g++ python3-dev python3-gdal libgdal-dev libsqlite3-mod-spatialite antiprism git libqt5gui5 libqt5core5a qtbase5-dev qtchooser qt5-qmake qtbase5-dev-tools libxtst6 libasound2 -yqq
# Clone and install Pyrate
COPY pyrate/ pyrate
RUN pip3 install ./pyrate
# Clone and install Model
COPY model/ model
RUN pip3 install ./model
# Clone ROS nodes and install requirements
COPY ros-nodes/ ros-nodes
WORKDIR ros-nodes
RUN pip3 install -r requirements.txt
RUN rosdep update -q
RUN rosdep install -q -y -i --from-path src --as-root pip:false
RUN pip install jupyter
SHELL ["/bin/bash", "-c", "source /opt/ros/humble/setup.bash && colcon build --symlink-install"]
RUN source install/setup.bash