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README.md

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-license: mit
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+
+
+![flux_krea_00365_](https://cdn-uploads.huggingface.co/production/uploads/68401f649e3f451260c68974/Er62mlciwE7gxap16wOk_.png)
+
+---
+license: mit
+tags:
+- pathfinding
+- simulation
+- reinforcement-learning
+- pyqt5
+- autonomous-agents
+- ai-education
+- vacuum-cleaner
+- search-algorithms
+- bfs
+- a-star
+- manhattan-distance
+- euclidean-distance
+- chebyshev-distance
+- turn-cost
+- performance-metrics
+- >-
+  - turn-cost - performance-metrics - algorithm-comparison - visualization-tool
+  - educational-software - python - artificial-intelligence -
+  robotics-simulation - grid-world - obstacle-avoidance -
+  multi-algorithm-framework - heuristic-evaluation - computational-efficiency -
+  node-expansion - path-optimization - interactive-learning -
+  real-time-simulation - gui-application - academic-project - research-tool -
+  algorithm-visualization - performance-analysis - turn-penalty - cost-analysis
+  - exploration-strategies - search-techniques - autonomous-navigation -
+  intelligent-agents - simulation-environment
+- >-
+  - path-planning - heuristic-search - comparative-analysis -
+  educational-resource - ai-simulation - robotics-education -
+  algorithm-benchmarking - performance-metrics - visualization-framework -
+  interactive-demonstration - learning-tool - academic-resource -
+  simulation-software - ai-visualization - pathfinding-algorithms -
+  search-methods - heuristic-functions - turn-cost-modeling -
+  performance-evaluation - algorithm-testing - simulation-platform -
+  educational-application - ai-demonstration - robotics-simulation -
+  autonomous-systems - intelligent-systems - search-strategies -
+  path-optimization - performance-comparison - heuristic-performance -
+  algorithm-efficiency - simulation-tool - visualization-software -
+  educational-software - ai-education - robotics-education -
+  pathfinding-visualization - algorithm-visualization -
+  performance-visualization - turn-cost-visualization -
+  multi-algorithm-comparison - interactive-simulation - real-time-visualization
+  - gui-simulation - pyqt5-application - python-simulation - grid-simulation -
+  obstacle-navigation - dirty-cell-cleaning - autonomous-cleaning -
+  vacuum-simulation - search-algorithm-comparison - heuristic-comparison
+---
+---
+title: Vacuum Cleaner Search Simulation
+emoji: 🏠
+colorFrom: blue
+colorTo: green
+pinned: false
+license: mit
+---
+
+# Vacuum Cleaner Search Simulation
+
+An interactive simulation that demonstrates various search algorithms for vacuum cleaner pathfinding in a grid environment.
+
+## 🎯 Overview
+
+This application visualizes how different search algorithms navigate through a grid to find and clean dirty cells while avoiding obstacles. The simulation compares the performance of BFS and A* search with different heuristics.
+
+## 🧠 Features
+
+- **Multiple Search Algorithms**:
+  - BFS (Breadth-First Search)
+  - A* with Manhattan distance heuristic
+  - A* with Euclidean distance heuristic  
+  - A* with Chebyshev distance heuristic
+
+- **Interactive Controls**:
+  - Reset environment
+  - Step-by-step simulation
+  - Auto-run mode
+  - Turn cost toggle (adds cost for direction changes)
+
+- **Real-time Metrics**:
+  - Steps taken and total cost
+  - Nodes explored and expanded
+  - Computation time
+  - Algorithm performance comparison
+
+## 🎮 How to Use
+
+1. **Setup the Environment**:
+   - The grid automatically generates with obstacles (blue) and dirty cells (red)
+   - The vacuum starts at a random clean position
+
+2. **Choose Algorithm**:
+   - Select from the dropdown menu (BFS, A* Manhattan, A* Euclidean, A* Chebyshev)
+
+3. **Configure Options**:
+   - Toggle "Turn Cost" to enable/disable penalty for direction changes
+   - Turn cost adds +0.5 for each 90° direction change
+
+4. **Run Simulation**:
+   - Click **Next** to advance one step
+   - Click **Run** for continuous automatic execution
+   - Click **Stop** to pause automatic execution
+   - Click **Reset** to generate a new environment
+
+## 🏗️ Technical Details
+
+### Search Algorithms
+
+- **BFS**: Explores all directions equally, guarantees shortest path
+- **A* Manhattan**: Uses city-block distance heuristic (`|x1-x2| + |y1-y2|`)
+- **A* Euclidean**: Uses straight-line distance heuristic
+- **A* Chebyshev**: Uses chessboard distance heuristic (`max(|x1-x2|, |y1-y2|)`)
+
+### Cell Types
+
+- 🟡 **Yellow**: Clean cells
+- 🔴 **Red**: Dirty cells (need cleaning)
+- 🔵 **Blue**: Obstacles (block movement)
+- 🟢 **Green**: Explored cells
+- 🟠 **Orange**: Current path
+
+### Performance Metrics
+
+- **Nodes Explored**: Total unique positions visited
+- **Nodes Expanded**: Total nodes processed by algorithm
+- **Computation Time**: Time taken to find paths
+- **Turn Cost**: Additional cost from direction changes
+
+## 📊 Algorithm Comparison
+
+The application tracks and compares:
+- Average nodes expanded per run
+- Average computation time
+- Efficiency across different heuristics
+
+Typically:
+- **BFS** explores more nodes but finds optimal paths
+- **A* Euclidean** is often most efficient for direct paths
+- **A* Chebyshev** may explore more nodes in grid environments
+
+## 🚀 Local Development