Smart Panel Cleaner Simulation with Gemini AI

The Problem: Solar energy is vital for a sustainable future, but environmental factors like dust, sandstorms, and debris can reduce solar panel efficiency by up to 30%. Manual cleaning is labor-intensive, costly, and often dangerous. The Solution : This project, Smart Panel Cleaner, is an intelligent simulation of an autonomous cleaning robot designed to maintain a commercial solar farm (represented by a 10x10 grid of 100 panels). Unlike standard robots that follow a fixed path, this agent uses Google Gemini 2.0 Flash as its "brain" to make real-time strategic decisions based on battery levels and grid cleanliness. Key Features AI Copilot: The robot communicates with Google Gemini to analyze telemetry data and decide whether to clean, charge, or wait. Dynamic Weather System: The simulation reacts to environmental triggers. "Sandstorms" drastically lower efficiency and revenue, while "Rain" provides natural cleaning, allowing the robot to save energy. Smart Navigation: Uses Breadth-First Search (BFS) algorithms to navigate around static walls and dynamic obstacles to find the nearest dirty panel. Real-Time Economics: The dashboard tracks the direct business impact, calculating "Revenue (USD)" lost to dirt versus energy gained from cleaning. Technical Implementation Built using Python and Streamlit, the application features a custom Matplotlib rendering engine to ensure a flicker-free, non-blocking visual experience. The AI logic runs asynchronously, ensuring the robot moves smoothly every second while the LLM processes data in the background.