This simple demo has been built during Udacity's CS373 "Programming A Robotic Car" class. It illustrates two algorithms:
- A-star for building a path to a goal location.
- A path smoothing algorithm.
You can set the goal point by double-tapping the screen, and - although somewhat difficult - you can also manipulate the maze (putting new blocks or removing existing ones) by touching the screen or sliding your finger across the screen.
You can play with many parameters in the settings. The following is a simple description of each one:
- Vertical/Horizontal/Diagonal cost: These motion costs will influence the final path. In particular, they influence the path-cost function (known as the g-value or g-function), which is the cost of going from the robot's current position to another position.
- Weigth data/smooth: These parameters are used in the path smoothing algorithm.
- The g value is incremental, meaning that if we're moving from point a to point b, then the g-value of b equals the g-value of a plus the cost of moving from a to b in a particular direction (i.e. vertical, horizontal or diagonal).
- The hueristic function between two points a and b equals the euclidean distance between a and b.
For more details on this algorithm, please see Udacity's CS373 course, unit 5, sections 1 through 6.
