Pathfinding & Optimization for Autonomous Navigation

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prerequisites
Intermediate Python • basic data handling
skills learned
BFS, DFS, and Dijkstra’s pathfinding • heuristic design • Pygame visualization • performance profiling
Ronnie Rahman
1 week · 4-6 hours per week · INTERMEDIATE
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In this liveProject, you’ll join SkyRoutes Inc., where drones navigate crowded cityscapes—but their inefficient routing is draining power and eroding customer trust. To fix it, you’ll create a drone pathfinding engine using Python. You’ll compare core search algorithms—BFS, DFS, and Dijkstra’s—to build a flexible navigation system that handles terrain costs and dynamic obstacles. You’ll animate routes using Pygame, accelerate your code with NumPy, and learn how to model grids as graphs in real-world scenarios. By the end, you’ll have a functional and visually interactive pathfinding system.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Ronnie Rahman
Ronnie Rahman is a Senior Software Developer and an AI engineer with ilem Group in Casablanca in collaboration with Swisstiming in Switzerland where he delivers algorithm-driven software for digital-transformation projects. He earned a BSc (Hons) in Computer Science in AI from the University of Hertfordshire, concentrating on neural networks, robotics, quantum computing, and AI algorithms. A frequent speaker at regional Python and data-science meet-ups, he contributes to open-source performance tools and mentors developers on writing efficient and explainable code.

prerequisites

This liveProject is aimed at Python programmers interested in turning theoretical algorithms into a portfolio of practical applications. To begin you will need to be familiar with:


TOOLS
  • Intermediate Python
  • Beginner Pygame
  • Beginner NumPy
  • Basic NumPy array manipulation

TECHNIQUES
  • Algorithm design and analysis
  • Graph theory
  • Computational thinking

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