An AI-powered simulation of an autonomous agent navigating a dynamic grid environment using the A* path planning algorithm.
The system performs real-time path replanning in response to obstacles and continuously updates navigation decisions while tracking performance metrics.
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Real-time A* path planning
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Dynamic obstacle handling & replanning
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Smooth agent movement simulation
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Click-to-set goal interaction
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Performance tracking:
- Path Length
- Execution Time
- Session & Total Replans
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Clean grid-based visualization using Pygame
- Generate grid-based environment
- Place static and dynamic obstacles
- Compute optimal path using A*
- Monitor environment continuously
- Trigger replanning when path is blocked
- Execute navigation to goal
- Python
- Pygame
- NumPy
- A* Algorithm
pip install -r requirements.txt
python main.py- Run the simulation
- Click anywhere to set a goal
- Observe real-time navigation and replanning
- Efficient path optimization using A*
- Adaptive navigation in dynamic environments
- Real-time performance monitoring
- YOLO-based real-world obstacle detection
- Integration with CARLA simulator
- Reinforcement Learning-based navigation
- Multi-agent path coordination
Varda Kunde