Cloud-Hosted Fleet Monitoring and Control App for Mobile Robots
Real-time fleet management system with web and mobile interfaces for monitoring and controlling autonomous mobile robots
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Real-time fleet dashboard showing active robots and status
Project Overview
Built a comprehensive cloud-based platform for real-time fleet monitoring, control, and analytics with web and mobile interfaces. The system enables operators to monitor robot statuses, track locations, analyze performance metrics, and remotely control autonomous mobile robots from anywhere.
Technical Implementation
- Developed a React-based web application with responsive design for real-time fleet visualization
- Built a Node.js backend server with WebSocket support for bidirectional communication with robots
- Implemented RESTful APIs for fleet management operations including scheduling, routing, and emergency controls
- Deployed services on AWS using EC2 instances for scalable architecture
- Integrated MongoDB database for storing historical data, logs, and fleet configurations
- Created Docker containers for consistent deployment across development and production environments
- Implemented real-time data streaming from robots to the cloud platform
- Developed authentication and authorization system for secure fleet access
Key Features
- Real-time position tracking and visualization on interactive maps
- Live status monitoring for battery levels, operational states, and error reporting
- Performance analytics dashboard with customizable metrics and historical trends
- Remote control capabilities for emergency interventions and manual overrides
- Alert system for critical events and maintenance notifications
- User management with role-based access control
- Mobile-responsive design for on-the-go fleet management
- Historical data analysis and reporting tools
Results & Future Work
The fleet monitoring system successfully enables remote management of autonomous mobile robots with real-time data visualization and control capabilities. Future enhancements include predictive maintenance using machine learning models to anticipate equipment failures, advanced route optimization algorithms to minimize travel time and energy consumption, and integration of computer vision for automated quality inspection during operations.