Fleet Manager

Project Description

During my internship at the Center for Automation Systems Engineering (CASE), KLE Technological University, I was a part of the development of a robust framework for multi-robot path planning and navigation tailored for challenging agricultural environments. Leveraging RTK-GPS-based localization and waypoint navigation, the system ensured precise and safe traversal across dynamic outdoor terrains.

At the core of this project lied the Fleet Manager — a Flask-based web application designed for real-time robot monitoring, task allocation, and system diagnostics. The app bridged the gap between robotic operations and user control, streamlining task management and enhancing field deployment efficiency..

Technologies Used

HTML, CSS, JavaScript, Folium(leaflet.js), Flask, Python, MySQL

Key Features

Login Page

Fleet Manager
  • Login Interface: Secured access through user authentication.

Dashboard

Fleet Manager
  • Interactive Map (Folium Integration): Visualizes robot positions and destination landmarks.
  • Task Allocation Interface: Assigns instructions to robots with location and operator details.
  • Job Card: Displays current assignments, robot ID, and employee ID.
  • Battery Status Monitor: Shows real-time electronics and motor battery levels.

System Logs

Fleet Manager
  • Comprehensive Log Viewer: Filters by operator, task, battery, and movement logs.
  • Search & Filter: Search by robot ID or employee ID for faster diagnostics.

My Contributions

As the Lead Developer, I was responsible for architecting and implementing the Fleet Manager system. From interface design to database management, my focus was on creating a scalable, field-ready application that could support real-time monitoring and control of multiple robots. My work emphasized reliability, intuitive user experience, and adaptability for real-world agricultural applications.

Project Outcomes & Impact

The Fleet Manager significantly enhanced operational efficiency in the trials by offering a centralized control hub for robotic systems. The platform not only simplified task allocation and tracking but also improved overall system observability through detailed logs and status indicators.

This project stands as a proof-of-concept for developing real-time, operator-friendly solutions in autonomous systems, and laid the groundwork for future development.