Path Planning in AI & Artificial Intelligence

A* is the industry standard for pathfinding in 2D and 3D grid environments (like a warehouse floor). It combines two scores: g(n) (the actual cost from the start to the current cell) and h(n) (the Heuristic estimate of the cost from the cell to the goal). By always exploring the node with the lowest combined score, A* finds the mathematically Optimal path with minimal effort. It is the core algorithm behind everything from video game NPCs to vacuum robot navigation.

When a robot has many 'Degrees of Freedom' (like a 7-joint industrial arm), a grid search becomes impossible—the number of combinations is too high. RRT solves this by using Random Sampling. It picks a random point in space and tries to 'Grow' its existing path tree toward that point. This approach is 'Probabilistically Complete': if you let it run long enough, it will find a path. RRT excels at finding valid paths through complex 'Keyhole' obstacles that would baffle other algorithms.

Navigation is split into two layers. The Global Planner (A*/RRT) runs once at the start (or whenever the destination changes) to find a high-level route. The Local Planner (also called a 'Path Follower') runs constantly (at 50Hz+). Its job is to keep the robot on the global path while performing Reactive Obstacle Avoidance. If a person walks in front of the robot, the local planner stops or swerves, then returns to the global path once the coast is clear.

Knowing where you are is only half the battle. Path Planning is the science of finding the shortest, safest route from point A to point B through a world full of obstacles.

A* (A-Star) is the classic algorithm for 'Discrete' space (like a grid). It uses a 'Heuristic' to guess which path is likely to be the shortest.

For high-dimensional space (like a 7-joint robotic arm), we use RRT (Rapidly-exploring Random Trees). It explores by 'growing' a tree of random samples.

Checkpoint: What is the main advantage of A* on a grid?

• →It's the easiest to write
• →It is guaranteed to find the shortest (optimal) path if a path exists

Path planning is 'Global'. It looks at the whole map. Once a path is found, it is passed to the 'Local Planner' which handles real-time obstacle avoidance.

By mastering Path Planning, you enable robots to navigate complex, maze-like environments with human-like efficiency and safety.

Checkpoint: Why use RRT instead of A* for a complex robotic arm?

• →It looks cooler
• →RRT is much more efficient at searching high-dimensional spaces (many joints) where a grid search would be too slow

Path Planning mastered! You've learned to navigate. Ready to optimize your speed and acceleration with Trajectory Planning?

Validate a Real Grid Move. Finish checking whether a grid cell is a valid, obstacle-free move for a path planner.