Formalizing Motion

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Formalizing Motion

• CIS 488/588
• Bruce R. Maxim
• UM-Dearborn

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Background - 1

• Robotics
• Systems use sensors to gather info, decide where
  to go next, and activate motors to make it happen
• Artificial Life
• Studies behaviors of synthetic creatures as part
  of a group (e.g. flocking and steering)
• Each agent sensors to detect obstacles in range
• Steering force comes from mathematical equations
  that translate sensor input into velocities

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Background - 2

• Academic Projects
• Tend to focus on 2D navigations as practical
  applications of algorithms (e.g. reinforcement
  problems)
• Game AI
• Simplified navigation to reduce computational
  overhead
• Data structures often pre-computed
• NPC position can be updated directly at run-time
  to simulate conscious movement

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Context

• The main consideration is determining how to
  model animats state in space
• Both position and orientation are important
  concepts for movement, but not AI
• AI can base decision by acquisition of local
  information
• Absolute vectors are expressed in global
  coordinates matching the world axis
• Relative coordinates are based on animats
  personal experiences

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Actions

• Can be designed at different levels
• AI requests one step at a time or full paths
• Actions provided as actual functions or
  implicitly using parameters
• Turns can be measured discretely or continuously
  (with several tradeoffs)
• Getting the grain size right for movement and
  commands is important to the quality of game play

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Senses - 1

• Animat needs to sense free space to perform
  navigation
• If environment info is not provide before game
  starts, it can be acquired by
• Point content (functions return type matter at
  specific locations)
• Line traces (used to find first segment with the
  structure of the world, can be used to find free
  space)

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Senses - 2

• Animat can simplify its task by using simpler
  sensors
• Collision sensors (detect when animate has hit an
  obstacle)
• Odometers (measure relative distance traveled
  relative to current state)
• Proprio-sensors (allow animat to detect its own
  state by monitoring its relative angular momentum)

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Senses - 3

• It is possible to understand environment layout
  just using contact sensors and position tracking
• Terrain learning is needed to prevent collisions
• Historically game developers have used failure
  relative load movement to determine that
  collisions have already taken place
• A better strategy would be to have the physics
  engine pass collision information directly to AI
  system rather than making it rediscover it again

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AI and Environmental Interface Concerns

• Consistency
• Information provide to animat must match the game
  environment
• Flexibility
• Keep specification as customizable as possible to
  ensure that similar problems can be handled by
  single interface
• Efficiency
• Minimize data exchange and limiting number of
  queries so that they can be optimized

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Proposed Specification

• Motor control requires two actions
• Move (continuous)
• Turn (continuous)
• Sensors
• Line traces return first obstacle from origin
• Physical feedback on collision (lowest level)
• Information passing
• AI lazy queries of environment info pull
• Environment broadcasts info info push

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Interfaces

• Motion module output
• void Move(const Vec3f direction)
• void Turn(const Vec3f orientation)
• Physics module
• bool Collision( ) // T or F on collision
• Visual interface simulation
• float Tracewalk(const float angle,
• const float steps 4.0f) const
• // returns how far animat got form origin
• // before bumping into something

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Code Skeleton

• void Think( )
• //check for contact
• bool col physics-gtCollision( )
• // locate obstacles in front
• float front_obstacle vision-gtTraceWalk(0.0f)
• // decide what actions to call
• motion-gtMove(Forward)
• if (col front_obstacle lt 2.0f)
• motion-gtTurn(orientation)

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Pinbot

• Can detect orientation of the wall when
  collisions occur
• Knowing direction of collision allows Pinbot to
  ricochet off the wall link a pin ball