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Project AERIS-Rover (Autonomous Eco-Robotic Intelligence System)

Hardware
  • 1 Devlogs
  • 2 Total hours

A complete, modular system of Autonomous Ground Vehicles (AGVs) for outdoor areas, designed for autonomous environmental data collection and automatic return-to-base (Return-To-Base / RTB).

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Devlog: AERIS Backend & Fleet Management Architecture

Overview

This devlog details the core backend infrastructure, API endpoints, and real-time mapping interface developed for the AERIS-Rover (Autonomous Eco-Robotic Intelligence System) project. The system is designed to coordinate outdoor AGVs (Autonomous Ground Vehicles) for environmental data collection and automated Return-to-Base (RTB) operations[cite: 1].


1. Core Backend Architecture (server.py)

Built using Python (Flask), the backend acts as the central command node running on a Raspberry Pi 5 (8GB)[cite: 1, 2]. It handles multi-robot telemetry, dynamic mission routing, and real-time client requests.

Key Technical Features:

  • RESTful API Endpoints:
    • /api/robot/location (POST): Receives real-time latitude, longitude updates from rovers and calculates operational distances[cite: 2].
    • /api/robot/status (POST): Tracks live health metrics including battery percentages, internet connectivity, error flags, and active mission states[cite: 2].
    • /api/robot/missions/set & /api/robot/missions (GET/POST): Manages mission generation, payload parsing, and waypoint distribution[cite: 2].
    • /api/robot/request (GET/POST): Handles asynchronous command queues for individual robot agents[cite: 2].
  • Haversine Distance Calculation: Implemented mathematical spatial formulas to compute precise ground distances in meters between rovers and designated zones (such as the main Base)[cite: 2].

2. Real-Time Map & Telemetry Dashboard (index.html)

The frontend dashboard provides visualization of the robot fleet operating in outdoor environments[cite: 1].

Key Technical Features:

  • Leaflet.js Integration: Renders dynamic interactive maps centered around defined operational sectors[cite: 1].
  • Geo-fencing & Zones: Programmatically draws circular zones (Base, Parks, Stadiums) with custom radius parameters and color codes[cite: 1].
  • Dynamic Polling & Marker Tracking: Asynchronously fetches active robot positions from the Flask backend every 2 seconds, updating marker coordinates, status popups, and battery indicators on the fly[cite: 1].
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