Mobile robots generally need to move from a START position (0) to a GOAL position (1) in order to accomplish tasks.

The simulator makes use of the Quad Tree method to create a distance graph and then this distance graph is used by the A* (A-star) path finding algorithm to find the best path.

##### Instructions

Click on the map to create an obstacle (Pink square = obstacle, Green square = no obstacle)
Use the 'g' key to change the GOAL position
Use the 's' key to change the START position

##### Note:

The map's grid can be expanded to accommodate a larger map. For this simulation, however, it is fixed to an 8x8 array.

Although the Quad Tree method is applied using a grid, the robot can move freely around inside each grid and is not bound by the center point of any quad.

The numbers next to each Node ID is the values used by the A-star algorithm to determine the best path. The tutorial will follow soon explaining all the detail.

## Fitting a Linear Line to Data Points

A line of best fit is a line that is the best approximation of a given set of data. This line can be linear, exponential, logarithmic, polynomial, a power average or a moving average and will depend on the type of data you have and the purpose of the approximated line. Your dataset can even be in 3 dimensions.

I'm only going to look at 2D data (ordered pairs) and the Least Square Method to find a straight line ($y=mx+c$) that best fit the given data points.