Plan a Path
Path planning algorithms: A*, RRT, local vs global planning.
What You'll Learn
- Formulate path planning as a graph search problem
- Implement A* and Dijkstra on a grid-based occupancy map
- Understand sampling-based planners like RRT and PRM
- Compare global planning (route) vs local planning (obstacle avoidance)
- Follow a planned path using waypoint tracking and pure pursuit
What Is Path Planning?
How robots find their way from point A to point B without hitting obstacles, and why path planning is harder than it looks.
Grid-Based Planning
How to find the shortest path through a gridded world using A* search, the algorithm that powers most robot navigation.
Sampling-Based Planning
How RRT and PRM algorithms plan in high-dimensional spaces where grids fail, and when to use sampling-based approaches.
Local vs. Global Planning
How robots use a two-layer approach: global planning for routes and local planning for immediate obstacle avoidance.
Following the Plan
How robots turn a sequence of waypoints into smooth steering commands using pure pursuit and trajectory tracking.