Presented by Jay Hatcher

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Robot Control
Software Applications and Challenges

• Presented by Jay Hatcher

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The Role of Software

• Interpretation of sensory input
• Internal representation of the outside world
• Reaction to external stimuli
• Prediction/Anticipation of events
• Resource management
• Motion generation

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The Role of Software

• Interpretation of sensory input
• Vision
• Selecting meaningful objects from sensory data
• Tracking object motion over time
• Tactile data
• Detecting collisions
• Measuring pressure and joint force
• Traction and Friction measurement
• Auditory input
• Selecting relevant sound events
• Information content

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The Role of Software

• Internal representation of the outside world
• Mapping relative or absolute position over time
• Mapping of significant changes to input over time
• Memory of actions taken and the changes in
  sensory data that resulted
• Memory of unanticipated obstacles

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The Role of Software

• Reaction to external stimuli
• How best to handle foreseen obstacles
• How best to handle unforeseen obstacles
• How best to react to interesting data
• How best to look for interesting data if none is
  available
• Response to partial hardware failure
• Response to external commands

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The Role of Software

• Prediction/Anticipation of events
• Is input data converging to a known configuration
  that requires a response?
• Obstacle avoidance
• Obstacle navigation
• Estimated time to goal condition
• Synchronization

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The Role of Software

• Resource management
• Fuel/Power levels
• Load
• Power efficiency
• Heat production

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The Role of Software

• Motion generation
• Wheels/Treads
• Maintaining speed
• Turning
• Legs
• 1-Legged Motion (Snake/Caterpillar)
• 2-Legged Motion (Bipedal human/bird)
• 4-Legged Motion (Pack Animals)
• 6-Legged Motion (Insect)

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M-TRAN II

• Modular Robot
• Uses genetic algorithms to both reconfigure
  itself and design motions for the current
  configuration

• Modular, reconfigurable design useful in
  hazardous, unstructured, or unknown environments

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M-TRAN II Reconfiguration

• Each configuration has a unique sequence of motor
  positions and connections

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M-TRAN II Reconfiguration

• Each segment contains a gene for each particular
  configuration that stores the sequence of motions
  and connections necessary to perform its role in
  the reconfiguration process

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M-TRAN II Reconfiguration

• Crossover and mutation are performed on the
  sequences once performance is evaluated by the
  simulation software

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M-TRAN II Reconfiguration

• Crossover and mutation are performed on the
  sequences once performance is evaluated by the
  simulation software

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M-TRAN II Motion

• Motion is generated by modeling each segment as a
  neural oscillator (like the nerves controlling
  muscles)
• Motion is evolved for each configuration and each
  generation evaluated by a fitness function

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4 Legged Motion Pre-evolution
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4 Legged Motion after Evolution
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Caterpillar
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Sidewinder
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Wheel
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Spider
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Motion and Reconfiguration
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Walking Around
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References and Useful Links

• M-TRAN II Main Page
• M-TRAN II Videos
• M-TRAN II Paper
• M-TRAN III Information
• News Article Robot runs like humans
• Stage 2.0 Open source 2D robot sim
• MindRover
• Lego Mindstorm NXT