GithubHelp home page GithubHelp logo

isabella232 / last-mile-fleet-solution-samples Goto Github PK

View Code? Open in Web Editor NEW

This project forked from googlemaps/last-mile-fleet-solution-samples

0.0 0.0 0.0 4.19 MB

License: Apache License 2.0

Shell 0.11% JavaScript 10.73% Ruby 0.08% Python 2.30% Java 54.23% CSS 1.02% Swift 26.30% HTML 5.23%

last-mile-fleet-solution-samples's Introduction

Last Mile Fleet Solution Sample Apps

Introduction

The LMFS Sample Apps provide reference implementations of the various integrations and applications that use the LMFS APIs. You can use these samples to quickly bootstrap a solution to create, update, and simulate vehicles, journeys and tasks. These apps work for both mobile and web.

To get started, use the getting started guide.

Components

There are three main pieces to the sample applications:

  1. The backend, a Java-based server using the Fleet Engine API, which supports services for the mobile applications as well as web-facing applications.

    The backend is contained in the backend/ directory.

  2. Mobile driver apps:

    • The Android Driver app, an Android client for delivery drivers written in Java using the Driver, Navigation, and Maps SDKs, which can be run on Android devices or simulators.

      The Android Driver app is contained in the android_driverapp_samples directory.

    • The iOS Driver app, an iOS client for delivery drivers written in Swift using the Driver, Navigation, and Maps SDKs, which can be compiled and run on iOS devices or simulators.

      The iOS Driver app is contained in the ios_driverapp_samples directory.

  3. Web apps:

    • The Shipment Tracking web app for users to track the progress of their shipments.
    • The Fleet Tracking web app used by operators to track delivery vehicles.

    The sample web apps are located in the backend/src/main/webapp/html directory, but can be hosted on any web server that can serve static HTML, CSS and JavaScript assets. These apps use the JavaScript Shipment Tracking and Fleet Tracking libraries.

In addition, some tools are shipped to help with the deployment and instantiation of a sample apps instance:

  • update_configuration.sh: this script is used during deployment to set up credentials for the whole project.
  • generator: this is a utility that creates scenario files that can be used to simulate delivery scenarios. For more information, consult the utility's README.

System architecture

System diagram

Backend

For a detailed listing of the web pages and endpoints, refer to the backend README file.

Authentication and token generation

The authentication component communicates with the Cloud Project for a LMFS deployment, generates JSON Web Tokens (JWTs) of various kinds, and passes the tokens onto the other apps to facilitate their communication with Fleet Engine.

This component makes use of the Fleet Engine Auth Library for Java, and requires that the the development host has been set up for Google Cloud authentication, and that the Google Cloud project used for Fleet Engine access has created service accounts used to sign these tokens.

For detailed setup procedure, refer to the getting started guide.

Scenario ingestion

The scenario ingestion component allows the user to upload a delivery configuration file, which contains definitions for delivery vehicles, stops, and tasks to be performed at each stop. A task can be a pick-up or delivery of a package. Upon receiving such a file, the component calls Fleet Engine to create these vehicles, stops, and tasks.

Delivery vehicle assignment

When a mobile driver app requests a vehicle assignment, the backend associates one of the defined (and available) delivery vehicles to the requester, and sends the information to the app. The app uses the information to set up its internal representations of these entities, and allows for simulation of a delivery vehicle as it travels through its assigned stops and performs its assigned tasks.

Vehicle and task updates

The backend exposes endpoints that allow the mobile driver app to request vehicle and task updates. These updates allow the mobile driver app to indicate, for example, that a task has been completed, that the vehicle is en route to its next stop, or that the driver has requested the stops to be re-sequenced.

Web pages and app serving

The backend serves the static resources for the shipment tracking and fleet tracking web apps: HTML, CSS and JavaScript. In addition, the backend has endpoints through which the web apps can obtain information about the tasks and vehicles that they are tracking.

Mobile driver app

The mobile driver app communicates with the backend, obtains a manifest of stop and task definitions, and shows the data in a user interface that allows the driver to perform the following actions:

  • Check the tasks to be performed at each stop.
  • Manually resequence the stops to change the order in which they are to be visited, if needed.
  • Initiate navigation to the first stop on the list.
  • Automatically send location updates to Fleet Engine.
  • Mark tasks as complete.

For details on the setup and usage of the mobile app, refer to the Android and iOS mobile driver app README files.

Web apps

The web apps communicate with the backend and Fleet Engine to retrieve status updates for a task or delivery vehicle. The apps then visualize the movement of the tracked entity in an automatically updated map and present data such as ETA in a tabular format. The web apps are designed for end users (such as package recipients) and delivery company dispatch operators.

The web apps integrate with the JavaScript Shipment Tracking and Fleet Tracking SDKs.

For details on the usage of the web apps, refer to the web apps document.

Delivery configuration file

The delivery configuration file specifies the list of vehicles, their manifests (consisting of a list of stops and tasks), and the order in which the stops are to be traversed. The file format is designed to be human-readable and easy to adapt to existing scheduling formats.

Command-line utility for generating a delivery configuration

This utility, written in Python, generates a delivery configuration file with stops and tasks scattered in a region. For details on the utility, refer to the generator's README file.

License

Copyright 2022 Google LLC.

Licensed to the Apache Software Foundation (ASF) under one or more contributor
license agreements.  See the NOTICE file distributed with this work for
additional information regarding copyright ownership.  The ASF licenses this
file to you under the Apache License, Version 2.0 (the "License"); you may not
use this file except in compliance with the License.  You may obtain a copy of
the License at

  http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.  See the
License for the specific language governing permissions and limitations under
the License.

last-mile-fleet-solution-samples's People

Contributors

anmatanm avatar googlemaps-bot avatar jpoehnelt avatar leovitch avatar mianala avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.