🚁 Probability Shadow Pathfinding System (PSPS)

A simulation framework for autonomous drone navigation using probability shadow pathfinding. This project implements advanced uncertainty-aware path planning with real-time collision risk assessment and dynamic rerouting.

🌟 Key Features

Probability Shadow Navigation

• Dynamic Risk Assessment: Real-time collision probability calculation
• Uncertainty Propagation: Growing uncertainty shadows for future positions
• Adaptive Rerouting: Intelligent path adjustment based on risk assessment

Advanced Analytics

• Real-time Metrics: Comprehensive tracking of system performance
• Collision Risk Analysis: Detailed probability-based risk assessment
• Mission Progress Tracking: Real-time completion monitoring
• Path Optimization: Analysis of route efficiency and safety

Visualization

• 3D Shadow Visualization: Real-time display of probability shadows
• Risk Mapping: Visual representation of collision risks
• Path History: Track historical movements and decisions
• Analytics Dashboard: Real-time performance metrics

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/yourusername/probability-shadow-pathfinding.git
cd probability-shadow-pathfinding

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install the package
pip install -e .

Basic Usage

from psps.simulation import ProbabilityShadowSimulation

# Create and run a simulation
sim = ProbabilityShadowSimulation(space_size=(50, 50, 30))
sim.run()

🎮 Advanced Usage

Custom Configuration

from psps.simulation import ProbabilityShadowGrid
import numpy as np

# Initialize the grid
grid = ProbabilityShadowGrid(space_size=(50, 50, 30))

# Configure simulation parameters
grid.prediction_steps = 20
grid.uncertainty_growth_rate = 0.08
grid.collision_threshold = 0.25
grid.safe_distance = 4.0
grid.time_horizon = 6.0

# Add drones with specific configurations
grid.initialize_drone(
    drone_id="drone_1",
    start=np.array([10, 10, 5]),
    goal=np.array([40, 40, 25])
)

Analytics Configuration

# Configure analytics collection
simulation.enable_analytics({
    'collision_risk': True,
    'path_efficiency': True,
    'mission_progress': True,
    'rerouting_events': True
})

🔧 Technical Details

Core Components

1. Probability Shadow Generation

   • Multivariate normal distribution modeling
   • Uncertainty growth prediction
   • Dynamic shadow point generation

2. Collision Risk Assessment

   • Probabilistic collision detection
   • Risk threshold management
   • Real-time path validation

3. Path Planning

   • Risk-aware route planning
   • Dynamic obstacle avoidance
   • Efficient alternative path finding

Performance Optimization

• Vectorized operations for shadow calculations
• Efficient spatial queries
• Optimized probability computations
• Streamlined visualization rendering

📊 Applications

• Urban Air Mobility: Safe autonomous drone navigation
• Risk Assessment: Probabilistic collision prediction
• Path Planning: Uncertainty-aware route optimization
• Multi-Agent Systems: Coordinated navigation with uncertainty
• Safety Analysis: Risk assessment for autonomous systems

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Probability theory concepts from advanced robotics research
• Visualization components built on Matplotlib and NumPy
• Statistical computations powered by SciPy
• Special thanks to all contributors and the open-source community

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