Software Architecture: Difference between revisions

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* Input: from the Vision and world simulator
* Input: from the Vision and world simulator
* Output: high level orders to the car simulator
* Output: high level orders to the car simulator
=== Driving Hard Problems (Motion Planning) ===
* It seems like we encode rules like: standard practice is to trail a car by 2 seconds
* And then we mention things like avoid other cars, don't make 3-G turns. The question is, how are those rules encoded?
* What about the situation where we want to make notice of things for later, like the location of potholes. Assuming the vision has decided that is a pothole, how do we add that to map, remove it when we go by because it has been fixed? In a racing game, we should make note about a jump so that we don't go off a cliff. Supposed we would just drive the car through the map to have it gather data. What is the maximum speed it could go through the map the first time?
[[Codebase_Analysis#Motion_Planning]]
http://cig.dei.polimi.it/?cat=4
http://youtube.com/watch?v=ruHzCF3CHIA


== [[VisionArch |Vision]] ==
== [[VisionArch |Vision]] ==

Revision as of 06:24, 6 July 2008

Codebase_Analysis

Multithreading

Architecture

File:Dataflow.odg

Driving

Driving module will use an interface like this of Torcs:

  • Input: from the Vision and world simulator
  • Output: high level orders to the car simulator

Vision

Vision module I/O:

  • Input: screenshot of the world (real or virtual).
  • Output: object geometry, etc. and other information necessary to the AI.

World Simulator

World simulator I/O:

  • Input: Progression of time, instructions to the car
  • Output: visual output for the user, screenshots for vision module, APIs to find out where stuff actually is. A warning when our car has "crashed", which means there is a bug in the vision or driving logic.

Extra Modules

Between C# and TORCS, the dispatcher

The dispatcher splits the information from TORCS and sends it to the relevant parts. The current position and rotation of our car and the opponents (including "parked cars" as obstacles) is send to the world viewer. Informations about our fuel, brake temperature etc. it sends to the AI part (rules, learning) and to the driver. The driver gets informations from the onboard sensors (engines rpm etc.). From the driver it gets back the commands to send back to TORCS. While learning, we also can send information to other parts, to get feedback about the quality of our single steps and to help to work independent.

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