From 104299e6edf362e6b8ecb27716c8d02acb33744e Mon Sep 17 00:00:00 2001 From: ivan-shargin Date: Wed, 27 Apr 2022 18:42:15 +0300 Subject: [PATCH 1/4] added(copied) notebook for hw8 --- notebooks/Seminar-Localisation.ipynb | 48 +++---- notebooks/localization_hw8.ipynb | 179 +++++++++++++++++++++++++++ 2 files changed, 205 insertions(+), 22 deletions(-) create mode 100644 notebooks/localization_hw8.ipynb diff --git a/notebooks/Seminar-Localisation.ipynb b/notebooks/Seminar-Localisation.ipynb index 733d31d..95e6b1b 100644 --- a/notebooks/Seminar-Localisation.ipynb +++ b/notebooks/Seminar-Localisation.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -43,9 +43,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['particle_1']\n", + "['particle_1', 'particle_1']\n", + "['particle_1', 'particle_1', 'particle_6']\n", + "['particle_1', 'particle_1', 'particle_6', 'particle_6']\n", + "['particle_1', 'particle_1', 'particle_6', 'particle_6', 'particle_1']\n", + "['particle_1', 'particle_1', 'particle_6', 'particle_6', 'particle_1', 'particle_4']\n" + ] + } + ], "source": [ "resampling_wheel(weights, 6, particleList)" ] @@ -59,16 +72,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip3 install matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -89,7 +93,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -108,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -126,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -135,12 +139,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -179,7 +183,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.8" } }, "nbformat": 4, diff --git a/notebooks/localization_hw8.ipynb b/notebooks/localization_hw8.ipynb new file mode 100644 index 0000000..d12954d --- /dev/null +++ b/notebooks/localization_hw8.ipynb @@ -0,0 +1,179 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "acquired-accused", + "metadata": {}, + "source": [ + "# Локализация. Упражнения" + ] + }, + { + "cell_type": "markdown", + "id": "electric-shannon", + "metadata": {}, + "source": [ + "В этих упражнениях вам предлагается реализовать простые алгоритмы локализации роботов. На лекции, помимо фильтра частиц, были упомянуты методы триангуляции и альфа-бета фильтр. Давайте их реализуем: обе реализации должны быть сделаны в виде класса, у которого должен быть метод update. Метод update принимает на вход измеренения с камеры о положении ориентиров в сосбтвенной системе координат робота." + ] + }, + { + "cell_type": "markdown", + "id": "trained-funds", + "metadata": {}, + "source": [ + "В качестве ориентиров будем использовать координаты стоек ворот. Для удобства будем пользоваться упрощенным вариантом, при котором ворота разных цветов. В словаре храняться положения стоек в глобальной системе координат (точка (0, 0) нахождится в центре поля)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "artistic-holocaust", + "metadata": {}, + "outputs": [], + "source": [ + "landmarks = {\n", + " \"blue_posts\": [\n", + " [ -4.5, -1.0], [ -4.5, 1.0]\n", + " ],\n", + " \"yellow_posts\":[\n", + " [ 4.5, -1.0], [ 4.5, 1.0]\n", + " ]\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "gross-browse", + "metadata": {}, + "source": [ + "Пример данных, которые могут поступать в модуль на одном шаге всей системы. Данные во втором словаре – это данные одометрии с собвственной информацией робота о перемещении за один шаг системы. Для проверки работы придумайте и запишите набор данных с такой же структурой. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "freelance-clothing", + "metadata": {}, + "outputs": [], + "source": [ + "observations = {\"yellow_posts\":[[4.45, -0.99],[4.49, 1.09]], \"blue_posts\":[]}\n", + "shift = {'shift_x':0.0, 'shift_y':0.01, 'shift_angle':-0.4}" + ] + }, + { + "cell_type": "markdown", + "id": "behind-mouth", + "metadata": {}, + "source": [ + "### 1) Реализовать локализацию триангуляцией (3 балл)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "funded-external", + "metadata": {}, + "outputs": [], + "source": [ + "class localization_triangulation:\n", + " \n", + " #put your code here\n", + " \n", + " pass\n", + " \n", + " def update():\n", + " \n", + " #put your code here\n", + " \n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "freelance-messaging", + "metadata": {}, + "source": [ + "### 2) Дополнить предыдущий пункт до альфа-бета фильтра (5 балла)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cordless-messenger", + "metadata": {}, + "outputs": [], + "source": [ + "class localization_alphabeta:\n", + " \n", + " #put your code here\n", + " \n", + " pass\n", + " \n", + " def update():\n", + " \n", + " #put your code here\n", + " \n", + " pass" + ] + }, + { + "cell_type": "markdown", + "id": "vanilla-documentary", + "metadata": {}, + "source": [ + "### 3) *Дополнительно* Добавьте визуализацию вида сверху (2 балла)" + ] + }, + { + "cell_type": "markdown", + "id": "polyphonic-extension", + "metadata": {}, + "source": [ + "Например на OpenCV или Matplotlib, например так как было в семинаре, чтобы можно было удобно посмотреть на работу модуля.\n" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "id": "olive-honey", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "systematic-prairie", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 4eaa2d891d65d47b066d0c0ed0f2a76f1138fa2f Mon Sep 17 00:00:00 2001 From: ivan-shargin Date: Thu, 28 Apr 2022 11:18:02 +0300 Subject: [PATCH 2/4] find_coord and find_jaw --- notebooks/localization_hw8.ipynb | 190 ++++++++++++++++++++++++++++++- 1 file changed, 185 insertions(+), 5 deletions(-) diff --git a/notebooks/localization_hw8.ipynb b/notebooks/localization_hw8.ipynb index d12954d..f0e3a3d 100644 --- a/notebooks/localization_hw8.ipynb +++ b/notebooks/localization_hw8.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "artistic-holocaust", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "freelance-clothing", "metadata": {}, "outputs": [], @@ -60,6 +60,41 @@ "shift = {'shift_x':0.0, 'shift_y':0.01, 'shift_angle':-0.4}" ] }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f923d813", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[4.45, -0.99], [4.49, 1.09]]\n", + "2.0\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'list' object has no attribute 'real'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1j\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m8j\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'real'" + ] + } + ], + "source": [ + "print(observations[\"yellow_posts\"])\n", + "d = 3\n", + "d /= 1.5\n", + "print(d)\n", + "c = [1 + 1j, 1 - 8j]\n", + "\n" + ] + }, { "cell_type": "markdown", "id": "behind-mouth", @@ -69,6 +104,98 @@ " " ] }, + { + "cell_type": "code", + "execution_count": 17, + "id": "f7bd8767", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import cmath" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c6d2b08b", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import json\n", + "import math\n", + "\n", + "sys.path.append(\"../localization/\")\n", + "\n", + "from robot import Robot\n", + "from field import Field\n", + "\n", + "with open(\"../localization/landmarks.json\", \"r\") as f:\n", + " landmarks = json.loads(f.read())\n", + "from pf_visualization import visualization" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "241ed55d", + "metadata": {}, + "outputs": [], + "source": [ + "def find_coord(observations, landmarks):\n", + " lm1 = np.array(observations[\"yellow posts\"][0])\n", + " lm2 = np.array(observations[\"yellow posts\"][1])\n", + " r1 = np.array([observations[\"yellow_posts\"][0, 0],\n", + " observations[\"yellow_posts\"][0, 1]])\n", + " r2 = np.array([observations[\"yellow_posts\"][0, 0],\n", + " observations[\"yellow_posts\"][0, 1]])\n", + " dist_1 = np.linalg.norm(r1, ord=2)\n", + " dist_2 = np.linalg.norm(r2, ord=2)\n", + " alpha = np.square(dist_1) - np.square(dist_2) \n", + " alpha += np.square(lm2[0]) - np.square(lm1[0])\n", + " alpha += np.square(lm2[1] - lm1[1])\n", + " alpha /= 2 * (lm2[1] - lm1[1])\n", + " beta = (lm1[0] - lm2[0]) / (lm2[1] - lm1[1])\n", + " a = 1 + np.square(beta)\n", + " b = -2*lm1[0] + 2 * alpha * beta \n", + " c = np.square(lm1[0]) + np.square(alpha) - np.square(dist_1)\n", + " Hypoths_x = np.roots(a, b, c)\n", + " Hypoths_x = np.array([x.real for x in Hypoths_x])\n", + " Hypoths_y = lm1[1] + alpha + beta * Hypoths_x\n", + " if Hypoths_y[0] < lm_1[1]:\n", + " return(np.array[Hypoths_x[0], Hypoths_y[0]])\n", + " else:\n", + " return(np.array[Hypoths_x[1], Hypoths_y[1]])\n", + "\n", + "def find_yaw(observations, coord):\n", + " lm1 = np.array(observations[\"yellow posts\"][0]) \n", + " r1 = np.array([observations[\"yellow_posts\"][0, 0],\n", + " observations[\"yellow_posts\"][0, 1]])\n", + " \n", + " cos = (lm1[0] - coord[0]) * r1[0] + (lm1[1]- coord[1]) * r1[1]\n", + " cos /= np.square(r1[0]) + np.square(r1[1])\n", + " sin = (r1[0] * cos + coord[0] - lm1[0]) / r1[1]\n", + " hypoth = np.arccos(cos)\n", + " if sin >= 0:\n", + " return hypoth\n", + " else:\n", + " return -hypoth + 2*np.pi\n", + " \n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d07cc219", + "metadata": {}, + "outputs": [], + "source": [ + "?np.roots" + ] + }, { "cell_type": "code", "execution_count": 6, @@ -78,11 +205,64 @@ "source": [ "class localization_triangulation:\n", " \n", - " #put your code here\n", + " def __init__(self, myrobot, field, landmarks, n=100, sense_noise = 0.4, apr = True):\n", + " self.n = n\n", + " self.myrobot = myrobot\n", + " self.landmarks = landmarks\n", + " self.count = 0\n", + " self.p = []\n", + " \n", + " with open('../localization/pf_constants.json', 'r') as constants:\n", + " constants = json.load(constants)\n", + "\n", + " self.forward_noise = constants['noise']['forward_noise']\n", + " self.turn_noise = constants['noise']['turn_noise']\n", + " self.sense_noise = constants['noise']['sense_noise']\n", + " self.gauss_noise = constants['noise']['gauss_noise']\n", + " self.yaw_noise = constants['noise']['yaw_noise']\n", + "\n", + " self.number_of_res = constants['consistency']['number_of_res']\n", + " self.consistency = constants['consistency']['consistency']\n", + " self.goodObsGain = constants['consistency']['goodObsGain']\n", + " self.badObsCost = constants['consistency']['badObsCost']\n", + " self.stepCost = constants['consistency']['stepCost']\n", + " self.dist_threshold = constants['consistency']['dist_threshold']\n", + " self.con_threshold = constants['consistency']['con_threshold']\n", + " self.spec_threshold = constants['consistency']['spec_threshold']\n", " \n", - " pass\n", + " def return_coord(self):\n", + " return self.myrobot.x, self.myrobot.y, self.myrobot.yaw\n", " \n", - " def update():\n", + " def particles_move(self, coord):\n", + " self.myrobot.move(coord['shift_x'],\n", + " coord['shift_y'], coord['shift_yaw'])\n", + " # now we simulate a robot motion for each of\n", + " # these particles\n", + " for partic in self.p:\n", + " partic[0].move(coord['shift_x'], coord['shift_y'],\n", + " coord['shift_yaw'])\n", + " self.count += 1\n", + " \n", + " def observation_to_predict(self, observations):\n", + " predicts = []\n", + " for color_landmarks in observations:\n", + " if (color_landmarks not in self.landmarks):\n", + " continue\n", + "\n", + " for landmark in self.landmarks[color_landmarks]:\n", + " if len(observations[color_landmarks]) != 0:\n", + " for obs in observations[color_landmarks]:\n", + " y_posts = self.myrobot.x + \\\n", + " obs[0]*math.sin(self.myrobot.yaw) + \\\n", + " obs[1]*math.cos(self.myrobot.yaw)\n", + " x_posts = self.myrobot.y + \\\n", + " obs[0]*math.cos(self.myrobot.yaw) - \\\n", + " obs[1]*math.sin(self.myrobot.yaw)\n", + " predicts.append([x_posts, y_posts])\n", + " return predicts\n", + " def \n", + " \n", + " def update(self):\n", " \n", " #put your code here\n", " \n", From 2562d446d69a25082a5e6a525d0d141a4195fb20 Mon Sep 17 00:00:00 2001 From: ivan-shargin Date: Thu, 28 Apr 2022 12:52:55 +0300 Subject: [PATCH 3/4] triangulation works for some examples --- notebooks/localization_hw8.ipynb | 208 +++++++++++++++---------------- 1 file changed, 103 insertions(+), 105 deletions(-) diff --git a/notebooks/localization_hw8.ipynb b/notebooks/localization_hw8.ipynb index f0e3a3d..cbdd66a 100644 --- a/notebooks/localization_hw8.ipynb +++ b/notebooks/localization_hw8.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 127, "id": "artistic-holocaust", "metadata": {}, "outputs": [], @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 35, "id": "freelance-clothing", "metadata": {}, "outputs": [], @@ -62,36 +62,22 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "f923d813", + "execution_count": 48, + "id": "b122958d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[4.45, -0.99], [4.49, 1.09]]\n", - "2.0\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'list' object has no attribute 'real'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1j\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m8j\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreal\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'real'" + "1.09\n" ] } ], "source": [ - "print(observations[\"yellow_posts\"])\n", - "d = 3\n", - "d /= 1.5\n", - "print(d)\n", - "c = [1 + 1j, 1 - 8j]\n", + "obs = np.array(observations['yellow_posts'])\n", + "print(obs[1, 1])\n", + "\n", "\n" ] }, @@ -138,79 +124,119 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "241ed55d", + "execution_count": 145, + "id": "03d05774", "metadata": {}, "outputs": [], "source": [ - "def find_coord(observations, landmarks):\n", - " lm1 = np.array(observations[\"yellow posts\"][0])\n", - " lm2 = np.array(observations[\"yellow posts\"][1])\n", - " r1 = np.array([observations[\"yellow_posts\"][0, 0],\n", - " observations[\"yellow_posts\"][0, 1]])\n", - " r2 = np.array([observations[\"yellow_posts\"][0, 0],\n", - " observations[\"yellow_posts\"][0, 1]])\n", - " dist_1 = np.linalg.norm(r1, ord=2)\n", - " dist_2 = np.linalg.norm(r2, ord=2)\n", - " alpha = np.square(dist_1) - np.square(dist_2) \n", - " alpha += np.square(lm2[0]) - np.square(lm1[0])\n", - " alpha += np.square(lm2[1] - lm1[1])\n", - " alpha /= 2 * (lm2[1] - lm1[1])\n", - " beta = (lm1[0] - lm2[0]) / (lm2[1] - lm1[1])\n", - " a = 1 + np.square(beta)\n", - " b = -2*lm1[0] + 2 * alpha * beta \n", - " c = np.square(lm1[0]) + np.square(alpha) - np.square(dist_1)\n", - " Hypoths_x = np.roots(a, b, c)\n", - " Hypoths_x = np.array([x.real for x in Hypoths_x])\n", - " Hypoths_y = lm1[1] + alpha + beta * Hypoths_x\n", - " if Hypoths_y[0] < lm_1[1]:\n", - " return(np.array[Hypoths_x[0], Hypoths_y[0]])\n", - " else:\n", - " return(np.array[Hypoths_x[1], Hypoths_y[1]])\n", "\n", - "def find_yaw(observations, coord):\n", - " lm1 = np.array(observations[\"yellow posts\"][0]) \n", - " r1 = np.array([observations[\"yellow_posts\"][0, 0],\n", - " observations[\"yellow_posts\"][0, 1]])\n", - " \n", - " cos = (lm1[0] - coord[0]) * r1[0] + (lm1[1]- coord[1]) * r1[1]\n", - " cos /= np.square(r1[0]) + np.square(r1[1])\n", - " sin = (r1[0] * cos + coord[0] - lm1[0]) / r1[1]\n", - " hypoth = np.arccos(cos)\n", - " if sin >= 0:\n", - " return hypoth\n", - " else:\n", - " return -hypoth + 2*np.pi\n", - " \n", " \n", " " ] }, { "cell_type": "code", - "execution_count": 14, - "id": "d07cc219", + "execution_count": 188, + "id": "1b50b45f", + "metadata": {}, + "outputs": [], + "source": [ + "loc = localization_triangulation(Robot(0.0, 0.0, 0.0), Field(\"../localization/parfield.json\"), landmarks)" + ] + }, + { + "cell_type": "code", + "execution_count": 190, + "id": "74721026", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.99999747379999\n", + "0.002247753019777479\n", + "(0.052236517079623464, 0.0, 0.002247754912605406)\n" + ] + } + ], + "source": [ + "observations_2 = {\"yellow_posts\":[[1, 0],[4.92, 0]], \"blue_posts\":[]}\n", + "loc.update(observations_2)\n", + "print(loc.return_position())" + ] + }, + { + "cell_type": "code", + "execution_count": 187, + "id": "124e8bf1", "metadata": {}, "outputs": [], "source": [ - "?np.roots" + "def find_coord(observations, landmarks):\n", + " obs = np.array(observations['yellow_posts'])\n", + " lm = np.array(landmarks['yellow_posts'])\n", + " lm1 = lm[0, :]\n", + " lm2 = lm[1, :]\n", + " r1 = np.array([obs[0, 0],\n", + " obs[0, 1]])\n", + " r2 = np.array([obs[0, 0],\n", + " obs[0, 1]])\n", + " dist_1 = np.linalg.norm(r1, ord=2)\n", + " dist_2 = np.linalg.norm(r2, ord=2)\n", + "\n", + " alpha = np.square(dist_1) - np.square(dist_2) \n", + " alpha += np.square(lm2[0]) - np.square(lm1[0])\n", + " alpha += np.square(lm2[1] - lm1[1])\n", + " alpha /= 2 * (lm2[1] - lm1[1])\n", + " beta = (lm1[0] - lm2[0]) / (lm2[1] - lm1[1])\n", + "\n", + " a = 1 + np.square(beta)\n", + " b = -2*lm1[0] + 2 * alpha * beta \n", + " c = np.square(lm1[0]) + np.square(alpha) - np.square(dist_1)\n", + " Hypoths_x = np.roots((a, b, c))\n", + " Hypoths_x = np.array([x.real for x in Hypoths_x])\n", + " Hypoths_y = lm1[1] + alpha + beta * Hypoths_x\n", + "\n", + " if Hypoths_x[0] < lm1[0]:\n", + " return np.array([Hypoths_x[0], Hypoths_y[0]])\n", + " else:\n", + " return np.array([Hypoths_x[1], Hypoths_y[1]])\n", + "\n", + "def find_yaw(observations, landmarks, coord):\n", + " obs = np.array(observations['yellow_posts'])\n", + " lm = np.array(landmarks['yellow_posts'])\n", + " lm1 = lm[0, :] \n", + " r1 = np.array([obs[0, 0],\n", + " obs[0, 1]])\n", + "\n", + " cos = (lm1[1] - coord[1]) * r1[1] + (lm1[0]- coord[0]) * r1[0]\n", + " cos /= np.square(r1[0]) + np.square(r1[1])\n", + "\n", + " sin = -(lm1[1] - coord[1]) * r1[0] + (lm1[0]- coord[0]) * r1[1]\n", + " sin /= np.square(r1[0]) + np.square(r1[1]) \n", + " \n", + " print(cos)\n", + " print(sin)\n", + " hypoth = np.arccos(cos)\n", + " if sin >= 0:\n", + " return hypoth \n", + " else:\n", + " return -hypoth + 2*np.pi" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 164, "id": "funded-external", "metadata": {}, "outputs": [], "source": [ "class localization_triangulation:\n", " \n", - " def __init__(self, myrobot, field, landmarks, n=100, sense_noise = 0.4, apr = True):\n", - " self.n = n\n", + " def __init__(self, myrobot, field, landmarks):\n", " self.myrobot = myrobot\n", " self.landmarks = landmarks\n", - " self.count = 0\n", - " self.p = []\n", " \n", " with open('../localization/pf_constants.json', 'r') as constants:\n", " constants = json.load(constants)\n", @@ -230,43 +256,15 @@ " self.con_threshold = constants['consistency']['con_threshold']\n", " self.spec_threshold = constants['consistency']['spec_threshold']\n", " \n", - " def return_coord(self):\n", + " def return_position(self):\n", " return self.myrobot.x, self.myrobot.y, self.myrobot.yaw\n", " \n", - " def particles_move(self, coord):\n", - " self.myrobot.move(coord['shift_x'],\n", - " coord['shift_y'], coord['shift_yaw'])\n", - " # now we simulate a robot motion for each of\n", - " # these particles\n", - " for partic in self.p:\n", - " partic[0].move(coord['shift_x'], coord['shift_y'],\n", - " coord['shift_yaw'])\n", - " self.count += 1\n", - " \n", - " def observation_to_predict(self, observations):\n", - " predicts = []\n", - " for color_landmarks in observations:\n", - " if (color_landmarks not in self.landmarks):\n", - " continue\n", - "\n", - " for landmark in self.landmarks[color_landmarks]:\n", - " if len(observations[color_landmarks]) != 0:\n", - " for obs in observations[color_landmarks]:\n", - " y_posts = self.myrobot.x + \\\n", - " obs[0]*math.sin(self.myrobot.yaw) + \\\n", - " obs[1]*math.cos(self.myrobot.yaw)\n", - " x_posts = self.myrobot.y + \\\n", - " obs[0]*math.cos(self.myrobot.yaw) - \\\n", - " obs[1]*math.sin(self.myrobot.yaw)\n", - " predicts.append([x_posts, y_posts])\n", - " return predicts\n", - " def \n", - " \n", - " def update(self):\n", - " \n", - " #put your code here\n", - " \n", - " pass" + " def update(self, observations): \n", + " coord = find_coord(observations, self.landmarks)\n", + " yaw = find_yaw(observations, self.landmarks, coord)\n", + " self.myrobot.x = coord[0]\n", + " self.myrobot.y = coord[1]\n", + " self.myrobot.yaw = yaw" ] }, { From 9eec211d9226304be0e10d8d5825f813a7ce4e6d Mon Sep 17 00:00:00 2001 From: ivan-shargin Date: Sat, 30 Apr 2022 18:18:32 +0300 Subject: [PATCH 4/4] alpha-beta works somehow --- notebooks/localization_hw8.ipynb | 124 ++++++++++++++++++++++++++++--- 1 file changed, 112 insertions(+), 12 deletions(-) diff --git a/notebooks/localization_hw8.ipynb b/notebooks/localization_hw8.ipynb index cbdd66a..32b50b1 100644 --- a/notebooks/localization_hw8.ipynb +++ b/notebooks/localization_hw8.ipynb @@ -26,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 21, "id": "artistic-holocaust", "metadata": {}, "outputs": [], @@ -51,12 +51,13 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 37, "id": "freelance-clothing", "metadata": {}, "outputs": [], "source": [ "observations = {\"yellow_posts\":[[4.45, -0.99],[4.49, 1.09]], \"blue_posts\":[]}\n", +<<<<<<< Updated upstream "shift = {'shift_x':0.0, 'shift_y':0.01, 'shift_angle':-0.4}" ] }, @@ -79,6 +80,9 @@ "print(obs[1, 1])\n", "\n", "\n" +======= + "shift = {'shift_x':1, 'shift_y':-1, 'shift_angle':-1.5}" +>>>>>>> Stashed changes ] }, { @@ -92,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 23, "id": "f7bd8767", "metadata": {}, "outputs": [], @@ -103,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 24, "id": "c6d2b08b", "metadata": {}, "outputs": [], @@ -124,6 +128,7 @@ }, { "cell_type": "code", +<<<<<<< Updated upstream "execution_count": 145, "id": "03d05774", "metadata": {}, @@ -170,6 +175,10 @@ "cell_type": "code", "execution_count": 187, "id": "124e8bf1", +======= + "execution_count": 25, + "id": "479ae827", +>>>>>>> Stashed changes "metadata": {}, "outputs": [], "source": [ @@ -227,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 26, "id": "funded-external", "metadata": {}, "outputs": [], @@ -256,6 +265,11 @@ " self.con_threshold = constants['consistency']['con_threshold']\n", " self.spec_threshold = constants['consistency']['spec_threshold']\n", " \n", + " def set_position(self, x, y, yaw):\n", + " self.myrobot.x = x\n", + " self.myrobot.y = y\n", + " self.myrobot.yaw = yaw\n", + " \n", " def return_position(self):\n", " return self.myrobot.x, self.myrobot.y, self.myrobot.yaw\n", " \n", @@ -267,6 +281,38 @@ " self.myrobot.yaw = yaw" ] }, + { + "cell_type": "code", + "execution_count": 27, + "id": "4e17a84a", + "metadata": {}, + "outputs": [], + "source": [ + "loc = localization_triangulation(Robot(0.0, 0.0, 0.0), Field(\"../localization/parfield.json\"), landmarks)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "d0c1e828", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.34394083931511e-08\n", + "1.0\n", + "(4.499999936560592, 0.0, 1.5707962633554882)\n" + ] + } + ], + "source": [ + "observations_2 = {\"yellow_posts\":[[1, 0],[-1, 0]], \"blue_posts\":[]}\n", + "loc.update(observations_2)\n", + "print(loc.return_position())" + ] + }, { "cell_type": "markdown", "id": "freelance-messaging", @@ -277,22 +323,76 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 34, "id": "cordless-messenger", "metadata": {}, "outputs": [], "source": [ "class localization_alphabeta:\n", " \n", - " #put your code here\n", + " def __init__(self, myrobot, field, landmarks, alpha, beta):\n", + " self.myrobot = myrobot\n", + " self.landmarks = landmarks\n", + " \n", + " with open('../localization/pf_constants.json', 'r') as constants:\n", + " constants = json.load(constants)\n", + "\n", + " self.forward_noise = constants['noise']['forward_noise']\n", + " self.turn_noise = constants['noise']['turn_noise']\n", + " self.sense_noise = constants['noise']['sense_noise']\n", + " self.gauss_noise = constants['noise']['gauss_noise']\n", + " self.yaw_noise = constants['noise']['yaw_noise']\n", + "\n", + " self.number_of_res = constants['consistency']['number_of_res']\n", + " self.consistency = constants['consistency']['consistency']\n", + " self.goodObsGain = constants['consistency']['goodObsGain']\n", + " self.badObsCost = constants['consistency']['badObsCost']\n", + " self.stepCost = constants['consistency']['stepCost']\n", + " self.dist_threshold = constants['consistency']['dist_threshold']\n", + " self.con_threshold = constants['consistency']['con_threshold']\n", + " self.spec_threshold = constants['consistency']['spec_threshold']\n", + " \n", + " def set_position(self, x, y, yaw):\n", + " self.myrobot.x = x\n", + " self.myrobot.y = y\n", + " self.myrobot.yaw = yaw\n", + " \n", + " def return_position(self):\n", + " return self.myrobot.x, self.myrobot.y, self.myrobot.yaw\n", " \n", " pass\n", " \n", - " def update():\n", - " \n", - " #put your code here\n", - " \n", - " pass" + " def update(self, observations, shift):\n", + " observe_coord = find_coord(observations, self.landmarks)\n", + " observe_yaw = find_yaw(observations, self.landmarks, observe_coord) \n", + " \n", + " self.myrobot.x = alpha * (self.myrobot.x + shift['shift_x']) + beta * observe_coord[0]\n", + " self.myrobot.y = alpha * (self.myrobot.y + shift['shift_y']) + beta * observe_coord[1]\n", + " self.myrobot.yaw = alpha * (self.myrobot.yaw + shift['shift_angle']) + beta * observe_yaw" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "3fd1c5ab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.99999747379999\n", + "0.002247753019777479\n", + "(0.9905223651707962, -0.99, -1.4849775224508739)\n" + ] + } + ], + "source": [ + "alpha = 0.99\n", + "beta = 0.01\n", + "loc_ab = localization_alphabeta(Robot(0.0, 0.0, 0.0), Field(\"../localization/parfield.json\"), landmarks, alpha, beta)\n", + "loc_ab.update(observations, shift)\n", + "print(loc_ab.return_position())" ] }, {