TroglodyteDerivations/Autonomous_Vacuum_Simulator

---

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