###IGV_Simulator

####Simulates the Motion of the IGV

Pathfinding..

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Project Details:

• Simualator contains 2D map of environment..
• Environment contains: Bot, Obstacles, GPS_Waypoints
• Bot needs to be able to move through the simulator; Pathfind from Start to Target
• Bot contains: It's own information about the environment..

Problems:

• How can we describe objects in the map?
• How do we find the list of nearby objects we are interested in?
• What methodology is needed to find a path?
• How can we be sure that our path is drivable? (A Path is one thing, the physical limitations of the IGV are another..)
• How do we define drivable? (ie: max turn radius at given speeds, not enough space, ..)

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• Bot has Search Radius. When an obstacle enters the Bot radius.. the Bot must store the position of said obstacle
• in it's internal map. (The Obstacle has it's own properties/functions that describe it's surface)
• When we need to generate the Bot's Path, the Bot needs to consider Obstacles in it's map
• When we need to identify obstacles.. we need to make a prediction given the information about the obstacle..

Solutions:

1. Points, whose shape covers a discrete surface area of the map.. stored in the Grid Map System
2. Using IGV(x,y) .. index into Grid Map.. +/- searchRadius
3. A* -> store/update path in graph structure (of paths) .. based on IGV's Discovered Environment
4. JSmoke libraries :)
5. (Define Limitations.. apply them to generating a close to optimal path.. maybe use some sampling techniques based on previous turning experience.. aka: we would have to train the bot by manually driving it... and generate Weights for turns and motion.. etc.. then we come back here and use our collected data...)

Ideas:

Simulator: --

Map contains:

• - IGV
  

• - Obstacles
  

Logic contains:

• - Collisions
  

• - Drawing stuff onto screen
  

IGV --

Map contains:

• 	( generated as exploration continues..
  

	  IGV update's its location and it's environment
	  in it's own map of the world )

• - IGV
  

• - Obstacles
  

• - Waypoints
  

Graph contains:

• - Pathing data.. (relative/(and based solely on)/to the Map it has generated..)
  

Logic contains:

• 	- Driving, (updating it's location on the Simulator map..)
  

• 	- Update It's own map by discovering Surroundings.
  

• 	- Determine Pathing_Data given IGVs Map.. 
  

• 	- Determine the likeliness this path is Correct, given Limitations and Error of Data..
  

• 	- Make corrections and chose better path -> so the that IGV doesnt crash and burn..
  

• 		(better is not more optimal.. it is just Drivable..)
  

####Please make changes to Ideas that need be updated. (for: accuracy & feasability)