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---
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 |