├── img ├── expand.png ├── extend.png └── voronoi.png ├── .gitignore ├── pyproject.toml ├── main.py ├── LICENSE ├── README.md ├── utils.py ├── track_generator.py └── uv.lock /img/expand.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mvanlobensels/random-track-generator/HEAD/img/expand.png -------------------------------------------------------------------------------- /img/extend.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mvanlobensels/random-track-generator/HEAD/img/extend.png -------------------------------------------------------------------------------- /img/voronoi.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/mvanlobensels/random-track-generator/HEAD/img/voronoi.png -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | .vscode/ 3 | *.yaml 4 | *.csv 5 | *.gpx 6 | 7 | # uv 8 | .venv/ 9 | .python-version -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "random-track-generator" 3 | version = "0.1.0" 4 | description = "Add your description here" 5 | readme = "README.md" 6 | requires-python = ">=3.11" 7 | dependencies = [ 8 | "gpxpy>=1.5.0", 9 | "matplotlib>=3.5.0", 10 | "numpy>=1.21.0,<2.0", 11 | "pyyaml>=6.0", 12 | "scipy>=1.8.1", 13 | "shapely>=2.0.1", 14 | ] 15 | -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | from track_generator import TrackGenerator 2 | from utils import Mode, SimType 3 | 4 | # Input parameters 5 | n_points = 60 6 | n_regions = 20 7 | min_bound = 0. 8 | max_bound = 150. 9 | mode = Mode.EXTEND 10 | sim_type = SimType.FSDS 11 | 12 | # Output options 13 | plot_track = True 14 | visualise_voronoi = True 15 | create_output_file = True 16 | output_location = '/' 17 | 18 | # Generate track 19 | track_gen = TrackGenerator(n_points, n_regions, min_bound, max_bound, mode, plot_track, visualise_voronoi, create_output_file, output_location, lat_offset=51.197682, lon_offset=5.323411, sim_type=SimType.GPX) 20 | track_gen.create_track() 21 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 Mees van Löben Sels 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Random Track Generator 2 | 3 | ![Python](https://img.shields.io/badge/python-%3E%3D3.11-blue) 4 | ![License](https://img.shields.io/badge/license-MIT-green) 5 | 6 |

7 | 8 | 9 |

10 | 11 | Generate random, rules-compliant tracks for Formula Student Driverless competitions. Creates realistic tracks with proper track width, cone spacing, and corner radii using Voronoi diagram-based generation. 12 | 13 | Developed by [Formula Student Team Delft](https://fsteamdelft.nl) for use with [FSSIM](https://github.com/AMZ-Driverless/fssim) and [FSDS](https://github.com/FS-Driverless/Formula-Student-Driverless-Simulator). 14 | 15 | ## How It Works 16 | 17 | Tracks are generated using bounded Voronoi diagrams from uniformly sampled points. Regions are selected using one of three modes (**Expand**, **Extend**, or **Random**), then interpolated with cubic splines to create smooth, realistic racing lines. 18 | 19 |

20 | 21 |

22 | 23 | ## Installation 24 | 25 | ```bash 26 | uv sync 27 | ``` 28 | 29 | ## Usage 30 | 31 | ### Basic Example 32 | 33 | ```python 34 | from track_generator import TrackGenerator 35 | from utils import Mode, SimType 36 | 37 | # Configure track generation 38 | track_gen = TrackGenerator( 39 | n_points=60, # Voronoi points 40 | n_regions=20, # Regions to select 41 | min_bound=0., # Minimum x/y bound 42 | max_bound=150., # Maximum x/y bound 43 | mode=Mode.EXTEND, # Selection mode 44 | plot_track=True, 45 | visualise_voronoi=True, 46 | create_output_file=True, 47 | output_location='/', 48 | sim_type=SimType.FSSIM 49 | ) 50 | 51 | track_gen.create_track() 52 | ``` 53 | 54 | ### Generation Modes 55 | 56 | - **`Mode.EXPAND`** - Selects nearest neighbors for roundish tracks 57 | - **`Mode.EXTEND`** - Selects regions along a random line for elongated tracks 58 | - **`Mode.RANDOM`** - Randomly selects regions for large, irregular tracks 59 | 60 | ### Simulator Output 61 | 62 | - **`SimType.FSSIM`** - Exports YAML format for FSSIM 63 | - **`SimType.FSDS`** - Exports CSV format for FSDS 64 | - **`SimType.GPX`** - Exports GPX format with lat/lon coordinates 65 | 66 | ### Quick Start 67 | 68 | ```bash 69 | uv run python main.py 70 | ``` 71 | 72 | Edit parameters directly in `main.py` or use the `TrackGenerator` class in your own scripts. 73 | 74 | ## Configuration 75 | 76 | All generation parameters can be configured through the `TrackGenerator` constructor. Key parameters include Voronoi diagram bounds, region selection count, track constraints (width, cone spacing, curvature), and output options. 77 | 78 | > **Note:** Not all parameter combinations produce stable results. Experiment with settings if generation fails. 79 | 80 | ## Credits 81 | 82 | Based on Ian Hudson's [Race-Track-Generator](https://github.com/I-Hudson/Race-Track-Generator). 83 | -------------------------------------------------------------------------------- /utils.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from enum import Enum 3 | 4 | class Mode(Enum): 5 | """ 6 | Possible modes for how Voronoi regions are selected. 7 | 8 | 1. Expand: 9 | Find closest nodes around starting node. 10 | Results in roundish track shapes. 11 | 12 | 2. Extend: 13 | Find nodes closest to line extending from starting node. 14 | Results in elongated track shapes. 15 | 16 | 3. Random: 17 | Select all regions randomly. 18 | Results in large track shapes. 19 | """ 20 | EXPAND = 1 21 | EXTEND = 2 22 | RANDOM = 3 23 | 24 | class SimType(Enum): 25 | """ Selection between output format for different simulators. 26 | 27 | 1. FSSIM: 28 | Output FSSIM compatible .yaml file. 29 | 2. FSDS: 30 | Output FSDS compatible .csv file 31 | """ 32 | FSSIM = 1 33 | FSDS = 2 34 | GPX = 3 35 | 36 | def closest_node(node, nodes, k): 37 | """ 38 | Returns the index of the k-th closest node. 39 | 40 | Args: 41 | node (numpy.ndarray): Node to find k-th closest node to. 42 | nodes (numpy.ndarray): Available nodes. 43 | k (int): Number which determines which closest node to return. 44 | 45 | Returns: 46 | int: Index of k-th closest node. 47 | """ 48 | deltas = nodes - node 49 | distance = np.einsum('ij,ij->i', deltas, deltas) 50 | return np.argpartition(distance, k)[k] 51 | 52 | def clockwise_sort(p): 53 | """ 54 | Sorts nodes in clockwise order. 55 | 56 | Args: 57 | p (numpy.ndarray): Points to sort. 58 | 59 | Returns: 60 | numpy.ndarray: Clockwise sorted points. 61 | """ 62 | d = p - np.mean(p, axis=0) 63 | s = np.arctan2(d[:,0], d[:,1]) 64 | return p[np.argsort(s),:] 65 | 66 | def curvature(dx_dt, d2x_dt2, dy_dt, d2y_dt2): 67 | """ 68 | Calculates the curvature along a line. 69 | 70 | Args: 71 | dx_dt (numpy.ndarray): First derivative of x. 72 | d2x_dt2 (numpy.ndarray): Second derivative of x. 73 | dy_dt (numpy.ndarray): First derivative of y. 74 | d2y_dt2 (numpy.ndarray): Second derivative of y. 75 | 76 | Returns: 77 | np.ndarray: Curvature along line. 78 | """ 79 | return (dx_dt**2 + dy_dt**2)**-1.5 * (dx_dt * d2y_dt2 - dy_dt * d2x_dt2) 80 | 81 | def arc_length(x, y, R): 82 | """ 83 | Calculates the arc length between to points based on the radius of curvature of the path segment. 84 | 85 | Args: 86 | x (numpy.ndarray): X-coordinates. 87 | y (numpy.ndarray): Y-coordinates. 88 | R (numpy.ndarray): Radius of curvature of track segment in meters. 89 | Returns: 90 | (float): Arc length in meters. 91 | """ 92 | x0, x1 = x[:-1], x[1:] 93 | y0, y1 = y[:-1], y[1:] 94 | R = R[:-1] 95 | 96 | distance = np.sqrt((x1 - x0)**2 + (y1 - y0)**2) 97 | theta = 2 * np.arcsin(0.5 * distance / R) 98 | arc_length = R * theta 99 | return arc_length 100 | 101 | def transformation_matrix(displacement, angle): 102 | """ 103 | Translate, then rotate around origin. 104 | 105 | Args: 106 | displacement (tuple): Distance to translate along both axes. 107 | angle (float): Angle in radians to rotate. 108 | 109 | Returns: 110 | numpy.ndarray: 3x3 transformation matrix. 111 | """ 112 | h, k = displacement 113 | c, s = np.cos(angle), np.sin(angle) 114 | 115 | M = np.array([ 116 | [c, -s, h * c - k * s], 117 | [s, c, h * s + k * c], 118 | [0, 0, 1 ] 119 | ]) 120 | return M -------------------------------------------------------------------------------- /track_generator.py: -------------------------------------------------------------------------------- 1 | import os, yaml 2 | import numpy as np 3 | import matplotlib.pyplot as plt 4 | from scipy import signal, spatial, interpolate 5 | from shapely.geometry.polygon import Point, LineString, Polygon 6 | from utils import * 7 | import math 8 | import gpxpy 9 | import gpxpy.gpx 10 | 11 | class TrackGenerator: 12 | """ 13 | Generates a random track based on a bounded Voronoi diagram. 14 | Ensures that the tracks curvature is within limits and that the car starts at a straight section. 15 | """ 16 | 17 | def __init__(self, 18 | n_points: int, 19 | n_regions: int, 20 | min_bound: float, 21 | max_bound: float, 22 | mode: Mode, 23 | plot_track: bool, 24 | visualise_voronoi: bool, 25 | create_output_file: bool, 26 | output_location: str, 27 | z_offset: float = 0, 28 | lat_offset: float = 0, 29 | lon_offset: float = 0, 30 | sim_type: SimType = SimType.FSSIM): 31 | 32 | # Input parameters 33 | self._n_points = n_points # [-] 34 | self._n_regions = n_regions # [-] 35 | self._min_bound = min_bound # [m] 36 | self._max_bound = max_bound # [m] 37 | self._bounding_box = np.array([self._min_bound, self._max_bound] * 2) # [x_min, x_max, y_min, y_max] 38 | self._mode = mode 39 | self._sim_type = sim_type 40 | 41 | # Track parameters 42 | self._track_width = 3. # [m] 43 | self._cone_spacing = 5. # [m] 44 | self._length_start_area = 6. # [m] 45 | self._curvature_threshold = 1. / 3.75 # [m^-1] 46 | self._straight_threshold = 1. / 100. # [m^-1] 47 | 48 | # Output options 49 | self._plot_track = plot_track 50 | self._visualise_voronoi = visualise_voronoi 51 | self._create_output_file = create_output_file 52 | self._output_location = output_location 53 | self._z_offset = z_offset 54 | self._lat_offset = lat_offset 55 | self._lon_offset = lon_offset 56 | 57 | def bounded_voronoi(self, input_points, bounding_box): 58 | """ 59 | Creates a Voronoi diagram bounded by the bounding box. 60 | Mirror input points at edges of the bounding box. 61 | Then create Voronoi diagram using all five sets of points. 62 | This prevents having a Voronoi diagram with edges going off to infinity. 63 | 64 | Args: 65 | input_points (numpy.ndarray): Coordinates of input points for Voronoi diagram. 66 | bounding_box (numpy.ndarray): Specifies the boundaries of the Voronoi diagram, [x_min, x_max, y_min, y_max]. 67 | 68 | Returns: 69 | scipy.spatial.qhull.Voronoi: Voronoi diagram object. 70 | """ 71 | 72 | def _mirror(boundary, axis): 73 | mirrored = np.copy(points_center) 74 | mirrored[:, axis] = 2 * boundary - mirrored[:, axis] 75 | return mirrored 76 | 77 | x_min, x_max, y_min, y_max = bounding_box 78 | 79 | # Mirror points around each boundary 80 | points_center = input_points 81 | points_left = _mirror(x_min, axis=0) 82 | points_right = _mirror(x_max, axis=0) 83 | points_down = _mirror(y_min, axis=1) 84 | points_up = _mirror(y_max, axis=1) 85 | points = np.concatenate([points_center, points_left, points_right, points_down, points_up]) 86 | 87 | # Compute Voronoi 88 | vor = spatial.Voronoi(points) 89 | 90 | # We only need the section of the Voronoi diagram that is inside the bounding box 91 | vor.filtered_points = points_center 92 | vor.filtered_regions = np.array(vor.regions, dtype=object)[vor.point_region[:vor.npoints//5]] 93 | return vor 94 | 95 | def create_track(self): 96 | """ 97 | Creates a track from the vertices of a Voronoi diagram. 98 | 1. Create bounded Voronoi diagram. 99 | 2. Select regions of Voronoi diagram based on selection mode. 100 | 3. Get the vertices belonging to the regions and sort them clockwise. 101 | 4. Interpolate between vertices. 102 | 5. Calculate curvature of track to check wether the curvature threshold is exceeded. 103 | 6. If curvature threshold is exceeded, remove vertice where the curvature is the highest from its set. 104 | Repeat steps 4-6 until curvature is within limimts. 105 | 7. Check if track does not cross itself. If so, go to step 2 and reiterate. 106 | 8. Find long enough straight section to place start line and start position. 107 | 9. Translate and rotate track to origin. 108 | 10. Create track yaml file. 109 | """ 110 | # Create bounded Voronoi diagram 111 | input_points = np.random.uniform(self._min_bound, self._max_bound, (self._n_points, 2)) 112 | vor = self.bounded_voronoi(input_points, self._bounding_box) 113 | 114 | while True: 115 | 116 | if self._mode.value == 1: 117 | # Pick a random point and find its n closest neighbours 118 | random_index = np.random.randint(0, self._n_points) 119 | random_point_indices = [random_index] 120 | random_point = input_points[random_index] 121 | 122 | for i in range(self._n_regions - 1): 123 | closest_point_index = closest_node(random_point, input_points, k=i+1) 124 | random_point_indices.append(closest_point_index) 125 | 126 | elif self._mode.value == 2: 127 | # Pick a random point, create a line extending from this point and find other points close to this line 128 | random_index = np.random.randint(0, self._n_points) 129 | random_heading = np.random.uniform(0, np.pi/2) 130 | random_point = input_points[random_index] 131 | 132 | start = (random_point[0] - 1./2. * self._max_bound * np.cos(random_heading), random_point[1] - 1./2. * self._max_bound * np.sin(random_heading)) 133 | end = (random_point[0] + 1./2. * self._max_bound * np.cos(random_heading), random_point[1] + 1./2. * self._max_bound * np.sin(random_heading)) 134 | line = LineString([start, end]) 135 | distances = [Point(p).distance(line) for p in input_points] 136 | random_point_indices = np.argpartition(distances, self._n_regions)[:self._n_regions] 137 | 138 | elif self._mode.value == 3: 139 | # Select regions randomly 140 | random_point_indices = np.random.randint(0, self._n_points, self._n_regions) 141 | 142 | # From the Voronoi regions, get the regions belonging to the randomly selected points 143 | regions = np.array([np.array(region) for region in vor.regions], dtype=object) 144 | random_region_indices = vor.point_region[random_point_indices] 145 | random_regions = np.concatenate(regions[random_region_indices]) 146 | 147 | # Get the vertices belonging to the random regions 148 | random_vertices = np.unique(vor.vertices[random_regions], axis=0) 149 | 150 | # Sort vertices 151 | sorted_vertices = clockwise_sort(random_vertices) 152 | sorted_vertices = np.vstack([sorted_vertices, sorted_vertices[0]]) 153 | 154 | while True: 155 | 156 | # Interpolate 157 | tck, _ = interpolate.splprep([sorted_vertices[:,0], sorted_vertices[:,1]], s=0, per=True) 158 | t = np.linspace(0, 1, 1000) 159 | x, y = interpolate.splev(t, tck, der=0) 160 | dx_dt, dy_dt = interpolate.splev(t, tck, der=1) 161 | d2x_dt2, d2y_dt2 = interpolate.splev(t, tck, der=2) 162 | 163 | # Calculate curvature 164 | k = curvature(dx_dt, d2x_dt2, dy_dt, d2y_dt2) 165 | abs_curvature = np.abs(k) 166 | 167 | # Check if curvature exceeds threshold 168 | peaks, _ = signal.find_peaks(abs_curvature) 169 | exceeded_peaks = abs_curvature[peaks] > self._curvature_threshold 170 | max_peak_index = abs_curvature[peaks].argmax() 171 | is_curvature_exceeded = exceeded_peaks[max_peak_index] 172 | 173 | if is_curvature_exceeded: 174 | # Find vertice where curvature is exceeded and delete vertice from sorted vertices. Reiterate 175 | max_peak = peaks[max_peak_index] 176 | peak_coordinate = (x[max_peak], y[max_peak]) 177 | vertice = closest_node(peak_coordinate, sorted_vertices, k=0) 178 | sorted_vertices = np.delete(sorted_vertices, vertice, axis=0) 179 | 180 | # Make sure that first and last coordinate are the same for periodic interpolation 181 | if not np.array_equal(sorted_vertices[0], sorted_vertices[-1]): 182 | sorted_vertices = np.vstack([sorted_vertices, sorted_vertices[0]]) 183 | else: 184 | break 185 | 186 | # Create track boundaries 187 | track = Polygon(zip(x, y)) 188 | track_left = track.buffer(self._track_width / 2) 189 | track_right = track.buffer(-self._track_width / 2) 190 | 191 | # Check if track does not cross itself 192 | if track.is_valid and track_left.is_valid and track_right.is_valid: 193 | if track.geom_type == track_left.geom_type == track_right.geom_type == 'Polygon': 194 | break 195 | 196 | # Calculate cone spacing 197 | cone_spacing_left = np.linspace(0, track_left.length, np.ceil(track_left.length / self._track_width).astype(int) + 1)[:-1] 198 | cone_spacing_right= np.linspace(0, track_right.length, np.ceil(track_right.length / self._track_width).astype(int) + 1)[:-1] 199 | 200 | # Determine coordinates of cones 201 | cones_left = np.asarray([np.asarray(track_left.exterior.interpolate(sp).xy).flatten() for sp in cone_spacing_left]) 202 | cones_right = np.asarray([np.asarray(track_right.exterior.interpolate(sp).xy).flatten() for sp in cone_spacing_right]) 203 | 204 | # Find straight section in track that is at least the length of the start area 205 | # If such a section cannot be found, adjust the straight_threshold and length_start_area variables 206 | # There is only a chance of this happening if n_regions == 1 207 | straight_threshold = self._straight_threshold if abs_curvature.min() < self._straight_threshold else abs_curvature.min() + 0.1 208 | straight_sections = abs_curvature[:-1] <= straight_threshold 209 | distances = arc_length(x, y, 1 / abs_curvature) 210 | length_straights = distances * straight_sections 211 | 212 | # Find cumulative length of straight sections 213 | for i in range(1, len(length_straights)): 214 | if length_straights[i]: 215 | length_straights[i] += length_straights[i-1] 216 | 217 | # Find start line and start pose 218 | length_start_area = self._length_start_area if length_straights.max() > self._length_start_area else length_straights.max() 219 | try: 220 | start_line_index = np.where(length_straights > length_start_area)[0][0] 221 | except IndexError: 222 | raise Exception("Unable to find suitable starting position. Try to decrease the length of the starting area or different input parameters.") 223 | start_line = np.array([x[start_line_index], y[start_line_index]]) 224 | start_position = np.asarray(track.exterior.interpolate(np.sum(distances[:start_line_index]) - length_start_area)).flatten() 225 | start_position = np.array([start_position[0].x, start_position[0].y]) 226 | start_heading = float(np.arctan2(*(start_line - start_position))) 227 | 228 | # Translate and rotate track to origin 229 | M = transformation_matrix(-start_position, start_heading - np.pi/2) 230 | cones_left = M.dot(np.c_[cones_left, np.ones(len(cones_left))].T)[:-1].T 231 | cones_right = M.dot(np.c_[cones_right, np.ones(len(cones_right))].T)[:-1].T 232 | 233 | # Create track file 234 | if self._visualise_voronoi: self.visualise_voronoi(vor, sorted_vertices, random_point_indices, input_points, x, y) 235 | if self._plot_track: self.plot_track(cones_left, cones_right) 236 | if self._create_output_file: self.output_yaml(cones_left.tolist(), cones_right.tolist()) 237 | 238 | def visualise_voronoi(self, vor, sorted_vertices, random_point_indices, input_points, x, y): 239 | """ 240 | Visualises the voronoi diagram and the resulting track. 241 | 242 | Args: 243 | vor (scipy.spatial.qhull.Voronoi): Voronoi diagram object. 244 | sorted_vertices (numpy.ndarray): Selected vertices sorted clockwise. 245 | random_point_indices (numpy.ndarray): Selected points. 246 | input_points (numpy.ndarray): All Voronoi points. 247 | """ 248 | # Plot initial points 249 | plt.figure() 250 | plt.plot(vor.filtered_points[:, 0], vor.filtered_points[:, 1], 'b.') 251 | 252 | # Plot vertices points 253 | for region in vor.filtered_regions: 254 | vertices = vor.vertices[region, :] 255 | plt.plot(vertices[:, 0], vertices[:, 1], 'go') 256 | 257 | # Plot edges 258 | for region in vor.filtered_regions: 259 | vertices = vor.vertices[region + [region[0]], :] 260 | plt.plot(vertices[:, 0], vertices[:, 1], 'k-') 261 | 262 | # Plot selected vertices 263 | plt.scatter(sorted_vertices[:,0], sorted_vertices[:,1], color='y', s=200, label='Selected vertices') 264 | 265 | # Plot selected points 266 | plt.scatter(*input_points[random_point_indices].T, s=100, marker='x', color='b', label='Selected points') 267 | 268 | # Plot track 269 | plt.scatter(x, y) 270 | plt.xlabel('x [m]') 271 | plt.ylabel('y [m]') 272 | plt.axis('equal') 273 | plt.legend() 274 | plt.show() 275 | 276 | def plot_track(self, cones_left, cones_right): 277 | """ 278 | Plots the resulting track. The car will start at the origin. 279 | 280 | Args: 281 | cones_left (numpy.ndarray): Nx2 numpy array of left cone coordinates. 282 | cones_right (numpy.ndarray): Nx2 numpy array of right cone coordinates. 283 | """ 284 | plt.figure() 285 | plt.scatter(*cones_left.T, color='b', s=1) 286 | plt.scatter(*cones_right.T, color='y', s=1) 287 | 288 | plt.xlabel('x [m]') 289 | plt.ylabel('y [m]') 290 | plt.axis('equal') 291 | plt.grid() 292 | plt.show() 293 | 294 | def output_yaml(self, cones_left, cones_right): 295 | """ 296 | Writes the track data to a yaml file. 297 | 298 | Args: 299 | cones_left (list): Nx2 list of left cone coordinates. 300 | cones_right (list): Nx2 list of right cone coordinates. 301 | """ 302 | abs_path_dir = os.path.realpath(os.path.dirname(__file__)) 303 | track_file_dir = abs_path_dir + self._output_location 304 | 305 | if(self._sim_type == SimType.FSSIM): 306 | track_file_name = track_file_dir + 'random_track.yaml' 307 | 308 | with open(track_file_name, 'w') as outfile: 309 | data = dict() 310 | data['cones_left'] = cones_left 311 | data['cones_right'] = cones_right 312 | data['cones_orange'] = [] 313 | data['cones_orange_big'] = [[4.7, 2.5], [4.7, -2.5], [7.3, 2.5], [7.3, -2.5]] 314 | data['starting_pose_cg'] = [0., 0., 0.] 315 | data['tk_device'] = [[6., 3.], [6., -3.]] 316 | yaml.dump(data, outfile) 317 | 318 | elif(self._sim_type == SimType.FSDS): 319 | track_file_name = track_file_dir + 'random_track.csv' 320 | 321 | print("Saving " + track_file_name) 322 | 323 | with open(track_file_name, 'w') as outfile: 324 | for cone in cones_left: 325 | outfile.write("blue," + str(cone[0]) + ',' + str(cone[1]) + ',0,0.01,0.01,0\n') 326 | 327 | for cone in cones_right: 328 | outfile.write("yellow," + str(cone[0]) + ',' + str(cone[1]) + ',0,0.01,0.01,0\n') 329 | 330 | outfile.write("big_orange,4.7,2.2,0,0.01,0.01,0\n") 331 | outfile.write("big_orange,4.7,-2.2,0,0.01,0.01,0\n") 332 | outfile.write("big_orange,7.3,2.2,0,0.01,0.01,0\n") 333 | outfile.write("big_orange,7.3,-2.2,0,0.01,0.01,0\n") 334 | elif(self._sim_type == SimType.GPX): 335 | track_file_name = track_file_dir + 'random_track.gpx' 336 | gpx = gpxpy.gpx.GPX() 337 | 338 | # Create first track in our GPX: 339 | gpx_track = gpxpy.gpx.GPXTrack() 340 | gpx.tracks.append(gpx_track) 341 | 342 | # Create points: 343 | for cone in cones_left: 344 | lat = self._lat_offset + (cone[1] / 6378100) * (180 / math.pi) 345 | lon = self._lon_offset + (cone[0] / 6378100) * (180 / math.pi) / math.cos(self._lat_offset * math.pi/180) 346 | gpx.waypoints.append(gpxpy.gpx.GPXWaypoint(latitude=lat, longitude=lon, elevation=0 + self._z_offset)) 347 | 348 | for cone in cones_right: 349 | lat = self._lat_offset + (cone[1] / 6378100) * (180 / math.pi) 350 | lon = self._lon_offset + (cone[0] / 6378100) * (180 / math.pi) / math.cos(self._lat_offset * math.pi/180) 351 | gpx.waypoints.append(gpxpy.gpx.GPXWaypoint(latitude=lat, longitude=lon, elevation=0 + self._z_offset)) 352 | 353 | with 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