Drone Survey Management System

A web application for managing drone surveys, including mission planning, monitoring, reporting, and a chatbot for user assistance. Built with React (frontend) and Express with Socket.IO (backend).

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Project Links:

• Postman Documentation: https://documenter.getpostman.com/view/34455053/2sAYkKJdAt
• Live Application: https://drone-ops.netlify.app/
• GitHub Repository: https://github.com/abhiSolankii/drone-ops.git

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Setup and Running Instructions

Prerequisites

• Node.js (v16 or higher)
• MongoDB (running locally or via a cloud provider like MongoDB Atlas)
• Google Generative AI API Key (for chatbot functionality)

Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Install dependencies:

   npm install

3. Create a .env file in the backend directory with the following:

   PORT=3000
   MONGODB_URI=mongodb://localhost:27017/drone-survey
   GEMINI_API_KEY=your-google-generative-ai-key
   FRONTEND_URL=http://localhost:5173
   

4. Start the backend server:

   npm start

Frontend Setup

1. Navigate to the frontend directory using:

   cd frontend

2. Install dependencies:

   npm install

3. Start the frontend development server:

   npm run dev

   The app will be available at http://localhost:5173.

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Functionalities Available

• Dashboard: View overall stats (active missions, fleet status, survey sites, alerts), mission statistics, drone statistics, active missions, and drone fleet details.
• Mission Planning: Create and schedule new missions with survey area, parameters (altitude, speed, overlap, pattern), and scheduling options.
• Mission Monitoring: Monitor in-progress missions, view drone positions on a map, control missions (pause, resume, abort), update mission details, and delete missions.
• Reports: Generate, view, and delete survey reports. Visualize mission and drone statistics with bar charts.
• Chatbot: A floating chatbot bubble on all pages for user assistance. Supports checking drone/mission status, scheduling missions, and general help via natural language.

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Design Decisions and Architectural Choices

Frontend

• React with React Router: Used for a single-page application (SPA) with client-side routing to navigate between Dashboard, Mission Planning, Mission Monitoring, and Reports pages.
• Socket.IO Client: Integrated for real-time communication with the backend, enabling live drone position updates and chatbot interactions.
• OpenLayers: Used for map visualization in Mission Monitoring to display drone positions dynamically.
• Recharts: Utilized for bar charts in the Reports page to visualize mission and drone statistics.
• Lucide-React Icons: Chosen for a consistent and modern icon set across the UI.
• Toast Notifications: Implemented with react-hot-toast for user feedback on actions (e.g., errors, success messages).
• Modular Components: Created reusable components like MissionCard, ReportCard, and Chatbot to maintain a clean and maintainable codebase.

Backend

• Express with Socket.IO: Used for the API and real-time communication. Socket.IO handles drone position updates and chatbot messages.
• MongoDB with Mongoose: Chosen for the database to store missions, drones, and reports, with geospatial indexing for survey areas.
• Google Generative AI: Integrated for the chatbot to process natural language queries and provide intelligent responses.
• Rate Limiting: Added with express-rate-limit to prevent abuse of API endpoints.
• Winston Logger: Implemented for logging server events and errors to both console and file (server.log).
• Error Handling: Global error handler to catch unhandled errors and return a 500 response, with detailed logging.

Architectural Choices

• Client-Server Architecture: Separated frontend and backend for scalability and maintainability. The frontend communicates with the backend via REST API (/api/missions, /api/drones, /api/reports) and WebSocket for real-time features.
• Real-Time Features: Used Socket.IO for live updates (drone positions, chatbot responses) to enhance user experience in Mission Monitoring and Chatbot.
• Chatbot Integration: Placed the chatbot at the root level in AppRouter.jsx to ensure the floating bubble is available on all pages, providing seamless user assistance.
• Modular Routes: Backend routes are split into missions.js, drones.js, and reports.js for better organization and scalability.

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AI Tools Integrated

Google Generative AI (Gemini 1.5 Flash)

• Integration: Used in the backend (chatbotHandler) to process natural language queries for the chatbot. It handles commands like "drone status ", "mission status ", and "schedule mission at ".
• Impact on Workflow:
  • Enhanced User Interaction: Enabled natural language support for the chatbot, allowing users to interact conversationally (e.g., "schedule mission TestMission at 2025-03-26T10:00:00.000Z").
  • Dynamic Responses: The AI fetches data from the database (e.g., drone/mission details) and generates context-aware responses, improving usability.
  • Development Efficiency: Reduced the need for hardcoding complex response logic by leveraging AI to interpret user intents and generate replies.
  • Limitations: Requires an API key and is subject to quota limits, which are handled with error messages in the chatbot.

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Notes

• Ensure MongoDB is running before starting the backend.
• Replace your-google-generative-ai-key in the .env file with a valid Google Generative AI API key.
• The chatbot currently uses simulated data for some responses; extend the chatbotHandler to integrate more backend logic as needed.