From f2c69f40d0797310a59db3aa0f1eecf62e1ed1e7 Mon Sep 17 00:00:00 2001 From: DuP-491 Date: Fri, 21 Jan 2022 21:05:33 +0530 Subject: [PATCH] changed some configurations --- RoboClub/settings.py | 32 +++++++++++++++++++++++--------- loaddata.json | 1 - 2 files changed, 23 insertions(+), 10 deletions(-) delete mode 100644 loaddata.json diff --git a/RoboClub/settings.py b/RoboClub/settings.py index 013a670d..85daa426 100644 --- a/RoboClub/settings.py +++ b/RoboClub/settings.py @@ -10,6 +10,7 @@ from pathlib import Path #import django_heroku import os +from decouple import config from django.contrib.messages import constants as messages @@ -32,11 +33,12 @@ # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/3.1/howto/deployment/checklist/ +IS_LOCAL=config('DEBUG', default=False, cast=bool) # SECURITY WARNING: keep the secret key used in production secret! -SECRET_KEY = 'ar4n33rnd^y^c!z8!*9dr8o2m5$n@5voqep&&0--h0ahszz&c#' +SECRET_KEY = config('SECRET_KEY', default='django-insecure-dfv22x*65_x&xp_x@v$s*&ieo()*@3!*499lxdfaqgk$(gbw3x') # SECURITY WARNING: don't run with debug turned on in production! -DEBUG = True +DEBUG = IS_LOCAL ALLOWED_HOSTS = ['*'] @@ -105,12 +107,24 @@ # Database # https://docs.djangoproject.com/en/3.1/ref/settings/#databases -DATABASES = { - 'default': { - 'ENGINE': 'django.db.backends.sqlite3', - 'NAME':os.path.join(BASE_DIR, 'db.sqlite3'), +if IS_LOCAL: + DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.sqlite3', + 'NAME':os.path.join(BASE_DIR, 'db.sqlite3'), + } + } +else: + DATABASES = { + 'default': { + 'ENGINE': 'django.db.backends.postgresql_psycopg2', + 'NAME': config('DB_NAME',default=''), + 'USER': config('DB_USER',default=''), + 'PASSWORD': config('DB_PASSWORD',default=''), + 'HOST': config('DB_HOST',default=''), + 'PORT': '', + } } -} # Password validation @@ -168,8 +182,8 @@ EMAIL_BACKEND='django.core.mail.backends.smtp.EmailBackend' EMAIL_USE_TLS = True EMAIL_HOST = 'smtp.gmail.com' -EMAIL_HOST_USER = '' #enter your email -EMAIL_HOST_PASSWORD = '' #enter your app password (remove them before commiting) +EMAIL_HOST_USER = config('EMAIL_USER',default='') #enter your email +EMAIL_HOST_PASSWORD = config('EMAIL_PASSWORD',default='') #enter your app password (remove them before commiting) EMAIL_PORT = 587 #django_heroku.settings(locals()) diff --git a/loaddata.json b/loaddata.json deleted file mode 100644 index 799904d0..00000000 --- a/loaddata.json +++ /dev/null @@ -1 +0,0 @@ -[{"model": "admin.logentry", "pk": 1, "fields": {"action_time": "2021-03-13T12:13:21.866Z", "user": 1, "content_type": 10, "object_id": "1", "object_repr": "divyansh-1", "action_flag": 2, "change_message": "[{\"changed\": {\"fields\": [\"first_name\", \"last_name\", \"role\"]}}]"}}, {"model": "admin.logentry", "pk": 2, "fields": {"action_time": "2021-03-13T14:29:42.786Z", "user": 3, "content_type": 10, "object_id": "3", "object_repr": "anurag-3", "action_flag": 2, "change_message": "[{\"changed\": {\"fields\": [\"first_name\", \"last_name\", \"regnum\", \"branch\", \"role\"]}}]"}}, {"model": "admin.logentry", "pk": 3, "fields": {"action_time": "2021-03-13T17:17:47.856Z", "user": 1, "content_type": 4, "object_id": "2", "object_repr": "divupa", "action_flag": 3, "change_message": ""}}, {"model": "admin.logentry", "pk": 4, "fields": {"action_time": "2021-03-13T17:40:05.516Z", "user": 1, "content_type": 4, "object_id": "5", "object_repr": "divupa", "action_flag": 2, "change_message": "[{\"changed\": {\"fields\": [\"Email address\"]}}]"}}, {"model": "admin.logentry", "pk": 5, "fields": {"action_time": "2021-03-15T14:53:07.488Z", "user": 1, "content_type": 4, "object_id": "5", "object_repr": "divupa", "action_flag": 3, "change_message": ""}}, {"model": "auth.permission", "pk": 1, "fields": {"name": "Can add log entry", "content_type": 1, "codename": "add_logentry"}}, {"model": "auth.permission", "pk": 2, "fields": {"name": "Can change log entry", "content_type": 1, "codename": "change_logentry"}}, {"model": "auth.permission", "pk": 3, "fields": {"name": "Can delete log entry", "content_type": 1, "codename": "delete_logentry"}}, {"model": "auth.permission", "pk": 4, "fields": {"name": "Can view log entry", "content_type": 1, "codename": "view_logentry"}}, {"model": "auth.permission", "pk": 5, "fields": {"name": "Can add permission", "content_type": 2, "codename": "add_permission"}}, {"model": "auth.permission", "pk": 6, "fields": {"name": "Can change permission", "content_type": 2, "codename": "change_permission"}}, {"model": "auth.permission", "pk": 7, "fields": {"name": "Can delete permission", "content_type": 2, "codename": "delete_permission"}}, {"model": "auth.permission", "pk": 8, "fields": {"name": "Can view permission", "content_type": 2, "codename": "view_permission"}}, {"model": "auth.permission", "pk": 9, "fields": {"name": "Can add group", "content_type": 3, "codename": "add_group"}}, {"model": "auth.permission", "pk": 10, "fields": {"name": "Can change group", "content_type": 3, "codename": "change_group"}}, {"model": "auth.permission", "pk": 11, "fields": {"name": "Can delete group", "content_type": 3, "codename": "delete_group"}}, {"model": "auth.permission", "pk": 12, "fields": {"name": "Can view group", "content_type": 3, "codename": "view_group"}}, {"model": "auth.permission", "pk": 13, "fields": {"name": "Can add user", "content_type": 4, "codename": "add_user"}}, {"model": "auth.permission", "pk": 14, "fields": {"name": "Can change user", "content_type": 4, "codename": "change_user"}}, {"model": "auth.permission", "pk": 15, "fields": {"name": "Can delete user", "content_type": 4, "codename": "delete_user"}}, {"model": "auth.permission", "pk": 16, "fields": {"name": "Can view user", "content_type": 4, "codename": "view_user"}}, {"model": "auth.permission", "pk": 17, "fields": {"name": "Can add content type", "content_type": 5, "codename": "add_contenttype"}}, {"model": "auth.permission", "pk": 18, "fields": {"name": "Can change content type", "content_type": 5, "codename": "change_contenttype"}}, {"model": "auth.permission", "pk": 19, "fields": {"name": "Can delete content type", "content_type": 5, "codename": "delete_contenttype"}}, {"model": "auth.permission", "pk": 20, "fields": {"name": "Can view content type", "content_type": 5, "codename": "view_contenttype"}}, {"model": "auth.permission", "pk": 21, "fields": {"name": "Can add session", "content_type": 6, "codename": "add_session"}}, {"model": "auth.permission", "pk": 22, "fields": {"name": "Can change session", "content_type": 6, "codename": "change_session"}}, {"model": "auth.permission", "pk": 23, "fields": {"name": "Can delete session", "content_type": 6, "codename": "delete_session"}}, {"model": "auth.permission", "pk": 24, "fields": {"name": "Can view session", "content_type": 6, "codename": "view_session"}}, {"model": "auth.permission", "pk": 25, "fields": {"name": "Can add project", "content_type": 7, "codename": "add_project"}}, {"model": "auth.permission", "pk": 26, "fields": {"name": "Can change project", "content_type": 7, "codename": "change_project"}}, {"model": "auth.permission", "pk": 27, "fields": {"name": "Can delete project", "content_type": 7, "codename": "delete_project"}}, {"model": "auth.permission", "pk": 28, "fields": {"name": "Can view project", "content_type": 7, "codename": "view_project"}}, {"model": "auth.permission", "pk": 29, "fields": {"name": "Can add share key", "content_type": 8, "codename": "add_sharekey"}}, {"model": "auth.permission", "pk": 30, "fields": {"name": "Can change share key", "content_type": 8, "codename": "change_sharekey"}}, {"model": "auth.permission", "pk": 31, "fields": {"name": "Can delete share key", "content_type": 8, "codename": "delete_sharekey"}}, {"model": "auth.permission", "pk": 32, "fields": {"name": "Can view share key", "content_type": 8, "codename": "view_sharekey"}}, {"model": "auth.permission", "pk": 33, "fields": {"name": "Can add blog", "content_type": 9, "codename": "add_blog"}}, {"model": "auth.permission", "pk": 34, "fields": {"name": "Can change blog", "content_type": 9, "codename": "change_blog"}}, {"model": "auth.permission", "pk": 35, "fields": {"name": "Can delete blog", "content_type": 9, "codename": "delete_blog"}}, {"model": "auth.permission", "pk": 36, "fields": {"name": "Can view blog", "content_type": 9, "codename": "view_blog"}}, {"model": "auth.permission", "pk": 37, "fields": {"name": "Can add profile", "content_type": 10, "codename": "add_profile"}}, {"model": "auth.permission", "pk": 38, "fields": {"name": "Can change profile", "content_type": 10, "codename": "change_profile"}}, {"model": "auth.permission", "pk": 39, "fields": {"name": "Can delete profile", "content_type": 10, "codename": "delete_profile"}}, {"model": "auth.permission", "pk": 40, "fields": {"name": "Can view profile", "content_type": 10, "codename": "view_profile"}}, {"model": "auth.permission", "pk": 41, "fields": {"name": "Can add component", "content_type": 11, "codename": "add_component"}}, {"model": "auth.permission", "pk": 42, "fields": {"name": "Can change component", "content_type": 11, "codename": "change_component"}}, {"model": "auth.permission", "pk": 43, "fields": {"name": "Can delete component", "content_type": 11, "codename": "delete_component"}}, {"model": "auth.permission", "pk": 44, "fields": {"name": "Can view component", "content_type": 11, "codename": "view_component"}}, {"model": "auth.permission", "pk": 45, "fields": {"name": "Can add request", "content_type": 12, "codename": "add_request"}}, {"model": "auth.permission", "pk": 46, "fields": {"name": "Can change request", "content_type": 12, "codename": "change_request"}}, {"model": "auth.permission", "pk": 47, "fields": {"name": "Can delete request", "content_type": 12, "codename": "delete_request"}}, {"model": "auth.permission", "pk": 48, "fields": {"name": "Can view request", "content_type": 12, "codename": "view_request"}}, {"model": "auth.permission", "pk": 49, "fields": {"name": "Can add tag", "content_type": 13, "codename": "add_tag"}}, {"model": "auth.permission", "pk": 50, "fields": {"name": "Can change tag", "content_type": 13, "codename": "change_tag"}}, {"model": "auth.permission", "pk": 51, "fields": {"name": "Can delete tag", "content_type": 13, "codename": "delete_tag"}}, {"model": "auth.permission", "pk": 52, "fields": {"name": "Can view tag", "content_type": 13, "codename": "view_tag"}}, {"model": "auth.permission", "pk": 53, "fields": {"name": "Can add tagged item", "content_type": 14, "codename": "add_taggeditem"}}, {"model": "auth.permission", "pk": 54, "fields": {"name": "Can change tagged item", "content_type": 14, "codename": "change_taggeditem"}}, {"model": "auth.permission", "pk": 55, "fields": {"name": "Can delete tagged item", "content_type": 14, "codename": "delete_taggeditem"}}, {"model": "auth.permission", "pk": 56, "fields": {"name": "Can view tagged item", "content_type": 14, "codename": "view_taggeditem"}}, {"model": "auth.permission", "pk": 57, "fields": {"name": "Can add news", "content_type": 15, "codename": "add_news"}}, {"model": "auth.permission", "pk": 58, "fields": {"name": "Can change news", "content_type": 15, "codename": "change_news"}}, {"model": "auth.permission", "pk": 59, "fields": {"name": "Can delete news", "content_type": 15, "codename": "delete_news"}}, {"model": "auth.permission", "pk": 60, "fields": {"name": "Can view news", "content_type": 15, "codename": "view_news"}}, {"model": "auth.permission", "pk": 61, "fields": {"name": "Can add workshop", "content_type": 16, "codename": "add_workshop"}}, {"model": "auth.permission", "pk": 62, "fields": {"name": "Can change workshop", "content_type": 16, "codename": "change_workshop"}}, {"model": "auth.permission", "pk": 63, "fields": {"name": "Can delete workshop", "content_type": 16, "codename": "delete_workshop"}}, {"model": "auth.permission", "pk": 64, "fields": {"name": "Can view workshop", "content_type": 16, "codename": "view_workshop"}}, {"model": "auth.user", "pk": 1, "fields": {"password": "pbkdf2_sha256$216000$YtIxBeztk9Hp$J8FSQQAIJayxxUrprdyFfObANc5NUupY+JZW9PXMq6M=", "last_login": "2021-03-15T19:06:26.669Z", "is_superuser": true, "username": "divyansh", "first_name": "", "last_name": "", "email": "divanimax@gmail.com", "is_staff": true, "is_active": true, "date_joined": "2021-03-13T12:12:19.142Z", "groups": [], "user_permissions": []}}, {"model": "auth.user", "pk": 3, "fields": {"password": "pbkdf2_sha256$216000$GrliQkLkyYMG$AdQpeRLaQcfu5gwKkTSm1uz8sTVYuxtJbCsijMHz6jI=", "last_login": "2021-03-14T08:53:54.199Z", "is_superuser": true, "username": "anurag", "first_name": "", "last_name": "", "email": "anurag@mailu.com", "is_staff": true, "is_active": true, "date_joined": "2021-03-13T14:28:45.256Z", "groups": [], "user_permissions": []}}, {"model": "auth.user", "pk": 4, "fields": {"password": "pbkdf2_sha256$216000$ES93pANlmk2w$pyrc8N+Ksn9jmqaVbg2GAAOtEeclctxtUGUpmOPqbTk=", "last_login": "2021-03-14T08:33:56.320Z", "is_superuser": false, "username": "Saitama", "first_name": "", "last_name": "", "email": "anurag.20195168@mnnit.ac.in", "is_staff": false, "is_active": true, "date_joined": "2021-03-13T15:28:37.640Z", "groups": [], "user_permissions": []}}, {"model": "auth.user", "pk": 6, "fields": {"password": "pbkdf2_sha256$216000$nJSePAFk79F1$2K9+PTVD0Db91DPUK+SzdFs12FbjxBtTh2aSvz/2n0c=", "last_login": null, "is_superuser": false, "username": "DuP-491", "first_name": "", "last_name": "", "email": "divyansh.20198117@mnnit.ac.in", "is_staff": false, "is_active": true, "date_joined": "2021-03-13T17:41:48.453Z", "groups": [], "user_permissions": []}}, {"model": "auth.user", "pk": 7, "fields": {"password": "pbkdf2_sha256$216000$V1fb4mWJY80J$twVY6jpq+lLtpUaaRz1B86Q8NZd41B2PVxTRBu2+GwU=", "last_login": null, "is_superuser": false, "username": "ardhya", "first_name": "", "last_name": "", "email": "kumar.20174014@mnnit.ac.in", "is_staff": false, "is_active": true, "date_joined": "2021-03-14T08:58:01.810Z", "groups": [], "user_permissions": []}}, {"model": "auth.user", "pk": 8, "fields": {"password": "pbkdf2_sha256$216000$Sv5IvHs3evep$vR1cQusu1z7zP1WPyS/ufSYEnmzErSVbQBOJytXJW6g=", "last_login": "2021-03-15T05:19:14.570Z", "is_superuser": false, "username": "ashutoshkr", "first_name": "", "last_name": "", "email": "ashutosh.20184029@mnnit.ac.in", "is_staff": false, "is_active": true, "date_joined": "2021-03-15T05:18:15.877Z", "groups": [], "user_permissions": []}}, {"model": "auth.user", "pk": 9, "fields": {"password": "pbkdf2_sha256$216000$dcur8gDraxiz$3NnVAklFa4wyQPsbpjzhbXs2Nvyeoh3D250F41gKdaI=", "last_login": null, "is_superuser": false, "username": "Robot", "first_name": "", "last_name": "", "email": "bhuvan@mnnit.ac.in", "is_staff": false, "is_active": false, "date_joined": "2021-03-15T19:25:56.029Z", "groups": [], "user_permissions": []}}, {"model": "auth.user", "pk": 10, "fields": {"password": "pbkdf2_sha256$216000$wqdksg8gx4V0$R178FmhNcboExhfuwHoDXAXimXhGvV5JFbOHesse1b4=", "last_login": "2021-03-15T19:30:04.830Z", "is_superuser": false, "username": "Robot_456", "first_name": "", "last_name": "", "email": "roboticsclub@mnnit.ac.in", "is_staff": false, "is_active": true, "date_joined": "2021-03-15T19:29:34.902Z", "groups": [], "user_permissions": []}}, {"model": "contenttypes.contenttype", "pk": 1, "fields": {"app_label": "admin", "model": "logentry"}}, {"model": "contenttypes.contenttype", "pk": 2, "fields": {"app_label": "auth", "model": "permission"}}, {"model": "contenttypes.contenttype", "pk": 3, "fields": {"app_label": "auth", "model": "group"}}, {"model": "contenttypes.contenttype", "pk": 4, "fields": {"app_label": "auth", "model": "user"}}, {"model": "contenttypes.contenttype", "pk": 5, "fields": {"app_label": "contenttypes", "model": "contenttype"}}, {"model": "contenttypes.contenttype", "pk": 6, "fields": {"app_label": "sessions", "model": "session"}}, {"model": "contenttypes.contenttype", "pk": 7, "fields": {"app_label": "project", "model": "project"}}, {"model": "contenttypes.contenttype", "pk": 8, "fields": {"app_label": "project", "model": "sharekey"}}, {"model": "contenttypes.contenttype", "pk": 9, "fields": {"app_label": "blog", "model": "blog"}}, {"model": "contenttypes.contenttype", "pk": 10, "fields": {"app_label": "user", "model": "profile"}}, {"model": "contenttypes.contenttype", "pk": 11, "fields": {"app_label": "component", "model": "component"}}, {"model": "contenttypes.contenttype", "pk": 12, "fields": {"app_label": "component", "model": "request"}}, {"model": "contenttypes.contenttype", "pk": 13, "fields": {"app_label": "taggit", "model": "tag"}}, {"model": "contenttypes.contenttype", "pk": 14, "fields": {"app_label": "taggit", "model": "taggeditem"}}, {"model": "contenttypes.contenttype", "pk": 15, "fields": {"app_label": "news", "model": "news"}}, {"model": 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".eJxVjEEOwiAQRe_C2pBOaXFw6d4zEIYZpGogKe3KeHfbpAvd_vfefysf1iX7tcnsJ1YXBer0u1GITyk74Eco96pjLcs8kd4VfdCmb5XldT3cv4McWt7qEXnAnkZh6glcZ4wQEhuIBMgygBjrnOlccAnsyJQs46YmsgkFz-rzBe1tOHM:1lLAaX:3JdXZrsMOSc_coogrOAopjke2IIMMhHcz5yw31Ho-Qo", "expire_date": "2021-03-27T20:10:17.470Z"}}, {"model": "sessions.session", "pk": "dadssyy08c750li7uwc9coir6e1e4rkq", "fields": {"session_data": "eyJfcGFzc3dvcmRfcmVzZXRfdG9rZW4iOiJhamhybXgtODI2M2M3ZDBjYjU3NTkyZTZmMDk2MTQ2NzYxYmFkNDQifQ:1lLM9Y:oXoEztDlP15MtFczJlmO27iGKW1O4Vv1RPONw-s8FjQ", "expire_date": "2021-03-28T08:31:12.850Z"}}, {"model": "sessions.session", "pk": "lan69i3xuvdy2yccfrk6ajaxptre47n9", "fields": {"session_data": ".eJxVjEEOwiAQAP_C2ZAVuqAevfsGwrKsVA0kpT0Z_25IetDrzGTeKsRtLWHreQkzq4uy6vDLKKZnrkPwI9Z706nVdZlJj0Tvtutb4_y67u3foMRextaDdWAygs0YASfrBZwIeop-IhBKho8ZGRO7ZFDkJERn5x17KwnU5wvTPzgw:1lLMVW:mvPPGxpkg2SoBKvaIPwrhCVdAYQbKDZq-Wa0M3YiPVA", "expire_date": "2021-03-28T08:53:54.201Z"}}, {"model": "sessions.session", "pk": "msn4urfx2acns9pla65v7ow0pa1frzmz", "fields": {"session_data": ".eJxVjMsOwiAQRf-FtSF0eLt07zcQBgapGkhKuzL-uzbpQrf3nHNfLMRtrWEbtIQ5szNz7PS7YUwPajvI99hunafe1mVGviv8oINfe6bn5XD_Dmoc9VtD1BQdUBHCY1FJe5LOGJEtTNII8EIpcslbwEyAbjIovUK0WmckXdj7A9lYN7A:1lLfdK:YFW6umQLVAU_aHZ5KV8NNHMHIuRSGApYaxvaRooZdpM", "expire_date": "2021-03-29T05:19:14.621Z"}}, {"model": "sessions.session", "pk": "u44gg4p67ea4s2tdioja9eatdvk85gs8", "fields": {"session_data": ".eJxVjEEOwiAQRe_C2pBOaXFw6d4zEIYZpGogKe3KeHfbpAvd_vfefysf1iX7tcnsJ1YXBer0u1GITyk74Eco96pjLcs8kd4VfdCmb5XldT3cv4McWt7qEXnAnkZh6glcZ4wQEhuIBMgygBjrnOlccAnsyJQs46YmsgkFz-rzBe1tOHM:1lLsXq:W5fvZuM1ybpWndXmFnMuzaDHjUw8OBvOyadlNzBNu4s", "expire_date": "2021-03-29T19:06:26.675Z"}}, {"model": "sessions.session", "pk": "x9wuthakq7pogwvv3zlyfgqdswu77o1p", "fields": {"session_data": ".eJxVjEEOwiAQRe_C2pBOaXFw6d4zEIYZpGogKe3KeHfbpAvd_vfefysf1iX7tcnsJ1YXBer0u1GITyk74Eco96pjLcs8kd4VfdCmb5XldT3cv4McWt7qEXnAnkZh6glcZ4wQEhuIBMgygBjrnOlccAnsyJQs46YmsgkFz-rzBe1tOHM:1lL8Cl:LvLKkzrimapwSwXuwEVKgzvL6xxfyAYMeNm3_z023rE", "expire_date": "2021-03-27T17:37:35.474Z"}}, {"model": "sessions.session", "pk": "yyz9u26o7yfx3ga5ec0judq2dnmatq1m", "fields": {"session_data": ".eJxVjMEOwiAQRP-FsyFLhS549O43kAUWqRqalPZk_HdL0oOeZjJvZt7C07YWvzVe_JTERSgQp98wUHxy7SQ9qN5nGee6LlOQvSIP2uRtTvy6Ht2_g0Kt9HUerYJkCBmdy9oNZtjFgCNUGJJWQBhsAMOWTeYY8hn0qFxEpN2Kzxf7nTfl:1lLsui:7tZpriMVIc4kYFv5kqJwBj2PqhZsIzfkkyYZvdAOPek", "expire_date": "2021-03-29T19:30:04.840Z"}}, {"model": "project.project", "pk": 2, "fields": {"title": "Indoor Navigation Robot", "aim": "Simulation of a Roomba like Robot for Internal Navigation purposes", "github": "", "vidlink": "https://www.youtube.com/embed/u8A2fI4MmqA", "detail": "

Introduction:

\r\n\r\n

The project aims to simulate a differential drive ground robot capable of autonomously mapping an unknown environment using sensors onboard and then autonomously navigate in the environment avoiding the obstacles using the map build and sensors onboard the robot.

\r\n\r\n

The whole project is designed for indoor environments where GPS information is either not directly available or weak enough so it can’t be relied upon, thus complicating the localisation process.

\r\n\r\n

We have simulated the project using a differential drive robot but the same can be extended to any aerial or ground vehicle easily as we have tried to keep the project as modular as possible.

\r\n\r\n

Direct Applications of our project:

\r\n\r\n

1)Such Robots can be used to generate maps of any building.

\r\n\r\n

2)Our vision-based navigation approach can be used for navigation in environments where GPS information is not directly available.

\r\n\r\n

3)Our navigation approach is capable of working in completely unknown environments thus making them a perfect candidate for 1st hand responders of disaster management situations.

\r\n\r\n

Future Work:

\r\n\r\n

We had to stop this project at the Simulation Stage because of lockdown imposed by COVID-19. We Plan to make this robot at hardware level when college reopens.

\r\n\r\n

 

", "overview": "

Introduction:

\r\n\r\n

The project aims to simulate a differential drive ground robot capable of autonomously mapping an unknown environment using sensors onboard and then autonomously navigate in the environment avoiding the obstacles using the map build and sensors onboard the robot.

\r\n\r\n

The whole project is designed for indoor environments where GPS information is either not directly available or weak enough so it can’t be relied upon, thus complicating the localisation process.

\r\n\r\n

We have simulated the project using a differential drive robot but the same can be extended to any aerial or ground vehicle easily as we have tried to keep the project as modular as possible.

\r\n\r\n

Direct Applications of our project:

\r\n\r\n

1)Such Robots can be used to generate maps of any building.

\r\n\r\n

2)Our vision-based navigation approach can be used for navigation in environments where GPS information is not directly available.

\r\n\r\n

3)Our navigation approach is capable of working in completely unknown environments thus making them a perfect candidate for 1st hand responders of disaster management situations.

\r\n\r\n

Future Work:

\r\n\r\n

We had to stop this project at the Simulation Stage because of lockdown imposed by COVID-19. We Plan to make this robot at hardware level when college reopens.

\r\n\r\n

Project by:

\r\n\r\n

Bhuvan Jhamb(20174009) ,Saurabh Kumar Yadav(20185051)

\r\n\r\n

Contact-bhuvanjhamb29@gmail.com,saurabh99184@gmail.com

\r\n\r\n

 

", "status": 1, "comp_and_tech": "Linux, ROS(Robot Operating System), Gazebo(Simulation Software), Robot Modelling, Control Systems, SLAM(Simultaneous Localisation and Mapping), Localisation, Path Planning", "image": "projects/9b1a719c85c911eba5cab4969153f04c.jpg", "members": [3]}}, {"model": "project.project", "pk": 4, "fields": {"title": "WanderBot", "aim": "Pybullet Simulation of a Robotic arm mounted on Ground Robot for Warehouse Automation Task.", "github": "", "vidlink": "https://www.youtube.com/embed/x9zvJwYl4sc", "detail": "

The robotic arm is used for multiple industrial applications, from welding, material handling, and thermal spraying, to painting and drilling and obviously picking some items and placing them somewhere else. In this project we have tried to mount the robotic arm(6DOF kuka arm) on a wheeled bot(husky mobile bot) so that the arm can pick some object from one point and place it in another. The project is built in Pybullet Simulation environment with the hope of implementing the same on hardware.

\r\n\r\n

 

\r\n\r\n

Main Technologies used in the Project:

\r\n\r\n
    \r\n\t
  1. Simulation in Pybullet
    2. \r\n\t
  2. Used real robots KUKA 6DOF robotic arm and HUSKY mobile robot in simulation.
    3. \r\n\t
  3. Self written Inverse kinematics used for robotic arm.
    4. \r\n\t
  4. Image processing. 
    5. \r\n\t
  5. For navigation, used grass fire and Astar algorithm.
    6. \r\n\t
  6. PID controlled motion.
    7. \r\n
\r\n\r\n

 

\r\n\r\n

Practical Applications and Uses:

\r\n\r\n

Application:

\r\n\r\n
    \r\n\t
  1. Health Care
    2. \r\n
\r\n\r\n

Such robots can be used to perform surgical operations on patients with the arm being controlled by the surgeon. 

\r\n\r\n

In such times of pandemic where infection spreads by contact such robots can be effective to deliver medicines to the patients’ beds who are admitted in the hospital.

\r\n\r\n
    \r\n\t
  1. Agriculture
    2. \r\n
\r\n\r\n

These kinds of robots can be used in harvesting the agricultural yield.

\r\n\r\n
    \r\n\t
  1. WareHouse
    2. \r\n
\r\n\r\n

These robots can be used to automate the process of packing and loading the goods in warehouses.

\r\n\r\n
    \r\n\t
  1. Security Services
    2. \r\n
\r\n\r\n

These robots can be extremely beneficial in emergency situations. They can be used during a bomb threat to locate and defuse the explosive without risking human life.

\r\n\r\n

Project by: Team !ABHIMANYU

\r\n\r\n", "overview": "

The robotic arm is used for multiple industrial applications, from welding, material handling, and thermal spraying, to painting and drilling and obviously picking some items and placing them somewhere else. In this project we have tried to mount the robotic arm(6DOF kuka arm) on a wheeled bot(husky mobile bot) so that the arm can pick some object from one point and place it in another. The project is built in Pybullet Simulation environment with the hope of implementing the same on hardware.

\r\n\r\n

 

\r\n\r\n

Main Technologies used in the Project:

\r\n\r\n
    \r\n\t
  1. Simulation in Pybullet
    2. \r\n\t
  2. Used real robots KUKA 6DOF robotic arm and HUSKY mobile robot in simulation.
    3. \r\n\t
  3. Self written Inverse kinematics used for robotic arm.
    4. \r\n\t
  4. Image processing. 
    5. \r\n\t
  5. For navigation, used grass fire and Astar algorithm.
    6. \r\n\t
  6. PID controlled motion.
    7. \r\n
\r\n\r\n

 

\r\n\r\n

Practical Applications and Uses:

\r\n\r\n

Application:

\r\n\r\n
    \r\n\t
  1. Health Care
    2. \r\n
\r\n\r\n

Such robots can be used to perform surgical operations on patients with the arm being controlled by the surgeon. 

\r\n\r\n

In such times of pandemic where infection spreads by contact such robots can be effective to deliver medicines to the patients’ beds who are admitted in the hospital.

\r\n\r\n
    \r\n\t
  1. Agriculture
    2. \r\n
\r\n\r\n

These kinds of robots can be used in harvesting the agricultural yield.

\r\n\r\n
    \r\n\t
  1. WareHouse
    2. \r\n
\r\n\r\n

These robots can be used to automate the process of packing and loading the goods in warehouses.

\r\n\r\n
    \r\n\t
  1. Security Services
    2. \r\n
\r\n\r\n

These robots can be extremely beneficial in emergency situations. They can be used during a bomb threat to locate and defuse the explosive without risking human life.

\r\n\r\n

Project by: Team !ABHIMANYU

\r\n\r\n", "status": 1, "comp_and_tech": "Python,Pybullet", "image": "projects/a5ada77c85c511eb8be5b4969153f04c.jpg", "members": [3]}}, {"model": "project.project", "pk": 5, "fields": {"title": "Person Follower Robot", "aim": "A Robot capable of following a robot to follow a human in challenging situations using CNN and stereo depth camera.", "github": "", "vidlink": "https://www.youtube.com/embed/OKQsDz6z3-8&ab_channel=RoboticsClub", "detail": "

In this project, we build a bot that can follow a person in real-world challenging situations. This bot has many applications such as autonomous carts in grocery stores, personal guides in hotels and hospitals, or as an autonomous suitcase. A person following robots in dynamic environments needs to address the tracking problem under different challenging situations (appearance changes, varying illumination, occlusions, pose changes such as crouching, exchanging jackets, etc.). 

\r\n\r\n

An online convolutional neural network (CNN) is used to track the given target under different situations. The target being tracked might move around corners making it disappear from the field of view of the robot. We address this problem by computing the recent poses of the target and have the robot replicate the local path of the target when the target is not visible in the current frame. We made a robot using DC encoder motors with Arduino, Raspberry Pi, and Realsense Depth Camera.

\r\n\r\n

We are following a research paper Integrating Stereo Vision with a CNN Tracker for a Person following Robot.

\r\n\r\n

 

\r\n\r\n

Project by: 

\r\n\r\n

Gaurav Bansal(20165142),  Anuj Tiwari(20176032),  Ayush Agrawal(20175064)

", "overview": "

In this project, we build a bot that can follow a person in real-world challenging situations. This bot has many applications such as autonomous carts in grocery stores, personal guides in hotels and hospitals, or as an autonomous suitcase. A person following robots in dynamic environments needs to address the tracking problem under different challenging situations (appearance changes, varying illumination, occlusions, pose changes such as crouching, exchanging jackets, etc.). 

\r\n\r\n

An online convolutional neural network (CNN) is used to track the given target under different situations. The target being tracked might move around corners making it disappear from the field of view of the robot. We address this problem by computing the recent poses of the target and have the robot replicate the local path of the target when the target is not visible in the current frame. We made a robot using DC encoder motors with Arduino, Raspberry Pi, and Realsense Depth Camera.

\r\n\r\n

We are following a research paper Integrating Stereo Vision with a CNN Tracker for a Person following Robot.

\r\n\r\n

 

\r\n\r\n

Project by: 

\r\n\r\n

Gaurav Bansal(20165142),  Anuj Tiwari(20176032),  Ayush Agrawal(20175064)

\r\n\r\n

 

", "status": 1, "comp_and_tech": "CNN, Arduino, Raspberry Pi", "image": "projects/a10f7bb885c611eb820fb4969153f04c.jpg", "members": [3]}}, {"model": "project.project", "pk": 6, "fields": {"title": "Drone Automation for Indoor Environment", "aim": "PID Tuning of a quadcopter and automation of its flight through Python scripts using Robot Operating System.", "github": "", "vidlink": "https://www.youtube.com/embed/eLivFID9gQE", "detail": "

In this project, we have tried to mimic the pollination process of a honeybee. The drone (bee) rises from its hive, pollinates a flower (lights the circuit), and comes back to its hive. The entire process is completely autonomous and can be initiated by one click. An overhead camera detects the position of the drone (Whycon marker attached on it) as well as position of flower (using image processing). This data is fed to a Python script as a ROS topic which then controls the drone using a PID Controller. The communication between laptop and drone is through the WiFi module. The entire project is developed on the ROS framework and PlutoX drone and was first simulated using V-REP Software.This project was done as a part of e-Yantra Robotics Competition 2018-19 under the theme ‘Pollinator Bee’.

\r\n\r\n

Languages Used: Python

\r\n\r\n

Acknowledgments:

\r\n\r\n

We are thankful to e-Yantra Robotics Competition for giving a chance to carry this project, and for support and guidance throughout the competition.

\r\n\r\n

Project by:

\r\n\r\n

Bhuvan Jhamb(20174009), Keshari Tiwari(20175054),

\r\n\r\n

Archit Chaudhary(20173053), Parag Saroha(20175018)

", "overview": "

In this project, we have tried to mimic the pollination process of a honeybee. The drone (bee) rises from its hive, pollinates a flower (lights the circuit), and comes back to its hive. The entire process is completely autonomous and can be initiated by one click. An overhead camera detects the position of the drone (Whycon marker attached on it) as well as position of flower (using image processing). This data is fed to a Python script as a ROS topic which then controls the drone using a PID Controller. The communication between laptop and drone is through the WiFi module. The entire project is developed on the ROS framework and PlutoX drone and was first simulated using V-REP Software.This project was done as a part of e-Yantra Robotics Competition 2018-19 under the theme ‘Pollinator Bee’.

\r\n\r\n

Languages Used: Python

\r\n\r\n

Acknowledgments:

\r\n\r\n

We are thankful to e-Yantra Robotics Competition for giving a chance to carry this project, and for support and guidance throughout the competition.

\r\n\r\n

Project by:

\r\n\r\n

Bhuvan Jhamb(20174009), Keshari Tiwari(20175054),

\r\n\r\n

Archit Chaudhary(20173053), Parag Saroha(20175018)

\r\n\r\n

 

", "status": 1, "comp_and_tech": "Linux, ROS (Robot Operating System), V-REP (simulation software), control systems, PID tuning, Whycon markers, image processing, camera calibration", "image": "projects/849232c285c711ebb6b0b4969153f04c.jpg", "members": [3]}}, {"model": "project.project", "pk": 7, "fields": {"title": "Obstacle Avoidance using Behaviour Cloning", "aim": "A nitro engine based RC car modified to run on BLDC motor and LiPo battery capable of avoiding obstacles using Behaviour Cloning", "github": "", "vidlink": "https://www.youtube.com/embed/6onzXsYBDBA", "detail": "

In this project, we made a self-driving car with RC Car using Behaviour Cloning, which is one of the methods by which human sub cognitive skills can be captured and reproduced in a computer program.

\r\n\r\n


\r\nSelf Driving Car is a vehicle capable of sensing its environment and operating without human involvement. A human passenger is not required to take control of the vehicle at any time, nor is a human passenger required to be present in the vehicle at all. An autonomous car can go anywhere a traditional car goes and do everything that an experienced human driver does.  

\r\n\r\n

Self Driving Cars are the future and we have to update with new technologies, So we started this project which is just a start to get the idea of how self-driving cars work. In this project, we converted a small RC car to a self-driving car that can run autonomously in corridors without human help.

\r\n\r\n

 

\r\n\r\n

Methodology:

\r\n\r\n

 

\r\n\r\n

First, we converted RC car to electric car by replacing its engine with an electric motor, batteries, and microcontroller Arduino.

\r\n\r\n

The second step in this project is to collect data to train our CNN model by controlling the car manually using the joystick. To control the car manually using a joystick we use jetson nano with realsense depth camera. We create a python code using pygame library in python to control the car.

\r\n\r\n

At the time of data collection, we save the RGB and depth image from the camera with the steering value and speed value of the car at that time.

\r\n\r\n

In the final dataset, we have 5173 images of (210*600) with steering and speed value of the car for model training

\r\n\r\n

\r\n\r\n

The third step is to train our ML model with the help of a colletected dataset. We build a CNN model to predict the steering value and speed value. The CNN model takes the image and predicts the speed and steering value.

\r\n\r\n

\r\n\r\n

The last layer of our model is the predicted value of speed and steering. In our CNN model, we used Conv layers, Dropout layers, Flatten layers, and some dense layers.

\r\n\r\n

\r\n\r\n

Project by: 

\r\n\r\n

Gaurav Bansal(20165142), Anuj Tiwari(20176032)

", "overview": "

In this project, we made a self-driving car with RC Car using Behaviour Cloning, which is one of the methods by which human sub cognitive skills can be captured and reproduced in a computer program.

\r\n\r\n


\r\nSelf Driving Car is a vehicle capable of sensing its environment and operating without human involvement. A human passenger is not required to take control of the vehicle at any time, nor is a human passenger required to be present in the vehicle at all. An autonomous car can go anywhere a traditional car goes and do everything that an experienced human driver does.  

\r\n\r\n

Self Driving Cars are the future and we have to update with new technologies, So we started this project which is just a start to get the idea of how self-driving cars work. In this project, we converted a small RC car to a self-driving car that can run autonomously in corridors without human help.

\r\n\r\n

Methodology:

\r\n\r\n

First, we converted RC car to electric car by replacing its engine with an electric motor, batteries, and microcontroller Arduino.

\r\n\r\n

The second step in this project is to collect data to train our CNN model by controlling the car manually using the joystick. To control the car manually using a joystick we use jetson nano with realsense depth camera. We create a python code using pygame library in python to control the car.

\r\n\r\n

At the time of data collection, we save the RGB and depth image from the camera with the steering value and speed value of the car at that time.

\r\n\r\n

In the final dataset, we have 5173 images of (210*600) with steering and speed value of the car for model training

\r\n\r\n

\r\n\r\n

The third step is to train our ML model with the help of a colletected dataset. We build a CNN model to predict the steering value and speed value. The CNN model takes the image and predicts the speed and steering value.

\r\n\r\n

\r\n\r\n

The last layer of our model is the predicted value of speed and steering. In our CNN model, we used Conv layers, Dropout layers, Flatten layers, and some dense layers.

\r\n\r\n

\r\n\r\n

Project by: 

\r\n\r\n

Gaurav Bansal(20165142), Anuj Tiwari(20176032)

\r\n\r\n

 

", "status": 1, "comp_and_tech": "Pygame, CNN, OpenCV", "image": "projects/812efe6685c811eba5cab4969153f04c.png", "members": [3]}}, {"model": "project.project", "pk": 8, "fields": {"title": "CNC X-Y Plotter", "aim": "A CNC (Computer Numeric Control) plotter is able to draw complex line drawings", "github": "", "vidlink": "https://www.youtube.com/embed/eoYF8SyKTyU", "detail": "

Introduction

\r\n\r\n

 

\r\n\r\n

CNC stands for Computer Numeric Control and typically refers to a machine whose operation is controlled by a computer. The most common usage of CNC, and the one relevant to us, is the name given to devices that, under computer control are able to cut, etch, mill, engrave, build, turn and otherwise perform manufacturing operations on various materials. Typically, a CNC machine has the ability to move a cutting or 3D printing head in 2 to 6 axes, meaning that it can position that tool head at a precise point in or on the material to create the cut or operation desired at that point. By moving the head through multiple points, the cutting head can cut or sculpt the design represented by a data stream of positioning points being sent by the PC. By controlling a CNC machine through a PC it is possible for the user to design a product on-screen, convert it to CNC-readable code and then send that data to the CNC machine for it to produce a physical copy of the item designed. Robotic 2D Plotter basically works with two stepper motors and a servo motor, wherein the robot plots the input is given from the computer on the drawing board using ATMEGA 328p microcontroller on an open-source physical computing platform Arduino. The Robotic 2D plotter has a two-axis control and a special mechanism to raise and lower the pen. Each axis is powered and driven by using an Arduino compactable driver L293D. Pen control is achieved using a servo. The software used for programming the Arduino board are namely Inkscape (0.48.5), GRBL, Arduino IDE, Universal G-Code Sender.

\r\n\r\n


\r\n 

\r\n\r\n

Principle

\r\n\r\n

 

\r\n\r\n

A CNC plotter is able to draw complex line drawings. The coordinates are uploaded to the machine controller by a separate program. The image file is transformed into G-code via Inkscape. Then the code is transferred to the microcontroller by which the motor mechanism is instructed to draw the image. The machine has two motors to implement the X and Y axis. A server motor is used along the Z axis for positioning the pen.

\r\n\r\n

 

\r\n\r\n

     

\r\n\r\n


\r\n 

\r\n\r\n

Hardware

\r\n\r\n

 

\r\n\r\n

Electronic hardware consists of interconnected electronic components which perform analog or logic operations on received and locally stored information to produce as output or store resulting new information or to provide control for output actuator mechanisms. Electronic hardware can range from individual chips/circuits to distributed information processing systems. Well-designed electronic hardware is composed of hierarchies of functional modules which inter-communicate via precisely defined interfaces The XY-plotter consists of two axes operating orthogonally to each other. Each axis includes a Stepper Motor drive system that is driven by an appropriate means. Additionally, a third axis, with limited motion capability is used to actuate the write head by using a servo motor.

\r\n\r\n

 

\r\n\r\n

Component Description

\r\n\r\n

 

\r\n\r\n
    \r\n\t
  1. Arduino Uno: The Arduino Uno is an open-source microcontroller board based on the Microchip ATmega328P microcontroller and developed by Arduino.cc.                        
    2. \r\n
\r\n\r\n

 

\r\n\r\n
    \r\n\t
  1. Stepper Motors: A stepper motor, also known as step motor or stepping motor, is a brushless DC electric motor that divides a full rotation into a number of equal steps. The motor’s position can then be commanded to move and hold at one of these steps without any position sensor for feedback (an open-loop controller), as long as the motor is carefully sized to the application in respect to torque and speed
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  1. Servo Motor: A servomotor is a rotary actuator or linear actuator that allows for precise control of angular or linear position, velocity and acceleration. It consists of a suitable motor coupled to a sensor for position feedback... Servomotors are used in applications such as robotics, CNC machinery or automated manufacturing,
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  1. CNC Shield V3 for motor A4988: This expansion board as a driver expansion board, can be used for engraving machines, 3D printers, CNC. It is a total of four slots, can drive four A4988 stepper motor. Each road stepper motors only need IO pots. In other words, six IO ports can be well managed three stepper motors.  
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  1. Aluminum Cylindrical Rods and Ball-Bearing:  We used these rods for stable and smooth motion of motors along X and Y axes. 
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Software

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Computer software, or simply software, is that part of a computer system that consists of

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encoded information or computer instructions, in contrast to the physical hardware from which the system is built. The software used in this project comes under open source. Open-source software (OSS) is computer software with its source code made available with a license in which the copyright holder provides the rights to study, change, and distribute the software to anyone 15 and for any purpose. Open-source software may be developed in a collaborative public manner. Open-source software is the most prominent example of open-source development.

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  1. Ardiuno IDE: The Arduino project provides the Arduino integrated development environment (IDE), which is a cross-platform application written in the programming language Java. It originated from the IDE for the languages Processing and Wiring. It is designed to introduce programming to artists and other newcomers unfamiliar with software development. It includes a code editor with features such as syntax highlighting, brace matching, and automatic indentation, and provides simple one-click mechanism to compile and load programs to an Arduino board. program written with the IDE for Arduino is called a "sketch".
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(Download Link: https://www.arduino.cc/en )

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  1. Inkscape: There are two basic types of graphic images: bitmap (or raster) images and vector images. In the 1st case, the image is defined in terms of rows and columns of individual pixels, each with its own colour. In the second case, the image is defined in terms of lines, both straight and 17 curved. A single straight line is described in terms of its two end points.
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The difference in these types of graphic images becomes readily apparent when a drawing is enlarged. The same line is shown on the left and right. On the left it is displayed as a bitmap image, while on the right it is displayed as a vector. In both cases, the line has been scaled up by a factor of four from its nominal size.

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To obtain vector image of desired shape we use this software.

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 (Download Link: https://inkscape.org)

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  1. Universal G-Code Sender: Used to generate G-Code from vector image.
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(Download Link: https://winder.github.io/ugs_website)

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  1. GRBL: Provide G-Code to Ardiuno board attached with CNC shield.
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(Download Link: https://github.com/grbl/grbl) 

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", "overview": "

Introduction: In this project, we have made a CNC plotter. It is able to draw complex line drawings. CNC stands for Computer Numeric Control and typically refers to a machine whose operation is controlled by a computer. The coordinates are uploaded to the machine controller by a separate program. The image file is transformed into G-code via Inkscape. Then the code is transferred to the microcontroller by which the motor mechanism is instructed to draw the image. The machine has two motors to implement the X and Y axis. A server motor is used along the Z axis for positioning the pen.

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Project by: Amit  Gupta, Divyansh Upadhyay

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Siddhesh Sunil Patil, Omesh Kumar 

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Devang More, Parag Kumar Gupta

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Anshuman Chowdhary, Diptarag Ray Chaudhri

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\r\nMentored by: Gaurav Bansal, Anuj Tiwari

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