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AI Architecture for Racing Vehicle Control November 3, 2004 Upmanyu Misra Scope 8.1: Racing Vehicle Control using Insect Intelligence 8.2: Fast and Efficient Approximation of Racing Lines Downforce by Smartdog Alex Darby Alex Darby

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Ai architecture for racing vehicle control l.jpg

AI Architecture for Racing Vehicle Control

November 3, 2004

Upmanyu Misra


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Scope

  • 8.1: Racing Vehicle Control using Insect Intelligence

  • 8.2: Fast and Efficient Approximation of Racing Lines

  • Downforce by Smartdog

  • Alex Darby


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Alex Darby

  • Joint Honors in AI, Psychology and CS from Univ. of Nottingham

  • Was senior developer at Smartdog, that developed PS2 futuristic racing game Downforce

  • Now works with FreeStyle Games Ltd.


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Futuristic?

  • Video game titles in which players take control of not-yet-invented vehicles

  • Different from NFS

  • Similar to Star Wars, anyone??


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Big Deal, eh?

  • Deformable car models

  • Off-the-wall additions to the F-1 racing genre

    • Thrust blasters

    • Super-engines

    • Insane speeds and stunts


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Right Intentions

  • Intelligent

  • Behavior must be intentional

  • Intentions ≡ Intelligence

Agent’s view

Player’s view


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Simplify F-1 Racing

  • Speed is highest priority

  • Keep on track, preferably on optimal racing curve

  • Easy, yet realistic braking

  • Easy steering

  • Realistic throttle control


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Overview

MOVIE

Downforce Simulation

Data Representation

AI Framework

Track Spaces

Brooks’ Subsumption

Insect AI

The Four main layers of Downforce AI

Racing Lines

DEMO

DEMO


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Data Abstraction

  • Need Data Abstraction and representation

    - For Downforce, concept is “track space”

    - Track space transforms 3-D position

    to 2-D position



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Track Space

  • Calculating <fWidthPos, fLengthPos> requires two vector normalizations per track position

  • Optimized by caching most common vectors


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Disadvantage

  • Unreliable for detecting fine-grain collisions

  • To avoid that …

    we transform the whole bounding box of the car to 2-D


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Downforce AI

  • Used Rodney Brooks’ Subsumption Architecture [Brooks91] and Insect AI architecture [Porcino03]

  • Downforce AI is primarily reactive


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Brooks’ Subsumption Architecture

  • Intelligent behavior can be generated without explicit representations or abstract reasoning of the kind that symbolic AI proposes

  • Intelligence is an emergent property of certain complex systems. Each layer does its job, they don’t necessarily coordinate. But the result seems “Intelligent”



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Insect AI

  • Introduced by Nick Porcino

  • Ch. 6.2

  • It provides

    • Architecture

    • Notation

    • Design Methodology


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As smart as a Bug…

  • Insects demonstrate a wide variety of interesting and successful behaviors

    • Seek (pursuit of a target)

    • Flee

    • Steer (away/into)

  • Yet, their nervous systems are simple

  • Hence its possible to analyze, compute and create simulation of their behaviors

  • This is called Computational Neuroethology



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Behaviors

  • Each layer is composed of smaller sub-tasks called Behaviors

  • Each Behavior deals with a single aspect of the task(s) that the parent layer is responsible for

  • The overall behavior of the layer emerges from the interaction between constituent behaviors


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Interaction between Layers

  • Three main ways of interactions

    • Directly override the output of another layer,

    • Directly affect the internal state of another layer,

    • One layer alters the input data of another layer to achieve its own task


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Output Contention System

  • Based on three variables

    • Steering

    • Throttle

    • Braking

  • Every layer tries to manipulate them to achieve their goal

  • Contention system uses explicit priority system based on order of execution of layers and state dependent rules for situations that cannot be handled otherwise


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Path Following Layer

  • Generates correct controller input for vehicle to follow a specific path at max speed

  • Consists of two independent behaviors

    • Steering control

    • Speed Control


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Independent?!

  • Throughout the Downforce system it is assumed that other behaviors will fulfill their responsibilities

  • This facilitates emergent nature

    reduced responsibilities

    reduced problem space

    reduced computational load per task

    overall reduction in execution time


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Steering Control

  • Calculates steering input that makes vehicle follow racing line

  • Needs to look ahead along the racing line from its current position to generate the value

  • Hence data needs to be represented accordingly (remember track space?)

  • Intelligent steering using PID controllers (Ch. 2.8 pg 171, demo on CD)


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Wicked Speed Control

  • Only responsible for regulating speed wrt path

  • Uses Newtonian physics equations for bodies in circular motion (remember centripetal/centrifugal forces?) to calculate max speed for any given track section

  • Uses FSM based around corners


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Corner Identification

  • Start – point from where the current speed becomes unfeasible

  • Apex – first point after the start when the change in turn radius is opposite to that of the start of the corner (where the corner begins to “open out”)


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Approaching a Corner

  • Calculate difference between the calculated max speed for the corner and the current speed

  • Derive the braking distance based on maximum braking (using good ol’ Newtonian eqns.)

  • If vehicle is outside the braking zone simply keep accelerating


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Round the Corner

  • Once the braking zone is reached, apply maximum breaking

  • On reaching max acceptable speed for the corner, cruise to apex

  • By the time apex is reached, the system will be ready with another set of behaviors (hopefully!)


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Tactical Racing Layer

  • Assesses the race conditions and changes the speed and racing line to gain racing advantage

  • Transforms an Expert Driver to an Expert Racer

  • Consists three behaviors

    • Optimum road position

    • New racing line

    • Collision avoidance speed control


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Optimum Road Positioning

  • Visual perception

    • One pair of eyes facing in each direction

      • One eye responds to relative speed of other cars

      • Other responds to their proximity

    • Each eye stores a one-dimensional image that represents the entire track width and has a resolution of 20 “perceptual pixels” or percepcels.



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Compound Eye

  • Products of the proximity and relative speed is stored in an array

  • Finally, we add the effect of the racing line to the compound eye with a high weightage

  • The tuning of the parameters takes time and experimentation. Makes it essential to have a method of editing parameters at run-time, ideally real-time Scripting


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Collision Avoidance Speed Control

  • Overrides the speed control set by the PF layer to avoid collision, primarily front to back

  • Collision test is performed using velocities measured in the track space domain

  • Its further refined by calculating the relative fWidthPos component of the track space velocity wrt that of the racing line


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Fine Grain Avoidance Layer

  • Deals with potential collision situations that were missed by previous layers

  • Consists of two behaviors

    • Race Priority/Racing Etiquette

      • Decides which car should back down

    • Fine Grain Collision Avoidance (they ran out of names!)

      • To avoid side-on collisions by restricting throttle and steering


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Driving Assist Layer

  • Controls problems such as wheel spin and brake locks

  • Consists of two behaviors

    • Traction Control

      • Minimizes wheel spin/slide

      • The slip/spin of the wheel is normalized between their corresponding min and max

      • Modifies throttle speeds set by other layers

    • Anti-lock Braking

      • Same as traction control except that the brake value is scaled instead of the throttle


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DEMO

  • http://www.atomicmedia.com/autonomous/

  • By Clint Hannaford

  • Based on Reynolds’ work on Autonomous Character Locomotion


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Fixing Racing Lines

  • They are desirable

    • give AI vehicles a hint of realism

    • helps AI vehicles to present better challenge

  • Record path taken by an AI vehicle driven by a competent human player

    • fast and efficient

    • cannot be used for randomly generated or player created tracks


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Fixing Racing Lines

  • Restrict random and player created tracks to being assembled from predefined segments with pre-computed racing lines

  • But

    • its not that much fun

    • racing line is context dependent and cannot be produced by plugging predefined sections


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Approximating

  • Ignore the physics of vehicle movement

  • Concentrate on minimizing curvature

  • Start with mid-points on the track


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Sources

  • AI Game Programming Wisdom 2

  • http://www.gamespot.com/ps2/driving/downforce/index.html

  • Rodney A. Brooks, “A Robust Layered Control System for a Mobile Robot”

  • Brian Beckman, “The Physics of Racing,”www.miata.net/sport/Physics/


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THANKS

PHEW!!!