Path-Planning Algorithms: Search and Motion Planning
Path planning connects algorithmic reasoning to real movement problems. This public repository contains independent Python implementations of Dijkstra, A*, RRT and RRT*, with demonstrations of how different search and sampling approaches construct paths through a map.
A public collection of four path-planning implementations and demonstrations, without claims of commercial robotics deployment.
My role: Independent algorithm developer
The challenge
Shortest-path search and sample-based motion planning make different trade-offs when navigating maps and obstacles. Exploring them side by side makes their behaviors easier to study and compare.
The part I led
Implemented the Python algorithm modules and published demonstrations for Dijkstra, A*, RRT and RRT*. The repository provides source code and example visualizations rather than evidence of integration with autonomous hardware.
The result
Made multiple algorithmic approaches available as inspectable Python source with video demonstrations. There is no verified production deployment, benchmarking result or real-world navigation claim.
The decisions and work behind it
- Implemented graph-search approaches with Dijkstra and A*.
- Explored sampling-based route construction using RRT and its optimization variant RRT*.
- Published source code and demonstrations for inspection and further experimentation.