Chuang-Hue Moh

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6.836 Embodied Intelligence Final Project
Chuang-Hue Moh Spring 2002
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Evolution in the Micro-Sense An Autonomous
Learning Robot

• Chuang-Hue Moh
• 6.836 Embodied Intelligence, Spring 2002

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Goal
Complex emergent behaviors of the honeybee colony
are results of interaction of individuals with
simple behaviors and learning capabilities
Capaldi et. al. Ontogeny of orientation flight
in honeybee revealed by harmonic radar

• Build a real physical robot with simple behavior
  and controls.
• Provide the robot with simple learning
  capabilities and allow them the interact using
  subsumption.
• Explore into applying genetic algorithms to the
  robots controller as a form of learning.

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Robot Design

• Subsumption network architecture
• Exploration mode when energy is high, recharging
  mode (seeks light source) when energy is low
• Learns
• Avoid obstacles (online self-adaptation) (current
  status completed)
• Navigate towards light (remembers experiences)
  (current status completed)
• Experimented with genetic algorithms in an
  attempt to evolve a controller to avoid obstacles
  (current status implemented but no experimental
  results yet)

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Subsumption Architecture
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Robot Implementation

• Lego RCXtm Microcomputer
• Hitachi H8/3292 micro-controller (16 MHz) with 16
  KB ROM and 16 KB RAM.
• In-built 10-bit ADC
• Memory-mapped I/O
• 3 input / 3 output ports
• IR transmitter / receiver

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Robot Implementation

• 1 x proximity sensor (light sensor IR
  transmitter)
• 1 x light sensor (shared with proximity sensor)
• 2 x touch sensors (switches)
• 2 x 9V DC motors

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Light Seeking Behavior

• Remembering light intensity - simplified
  eligibility trace type data structure
• Zeroing into light source location reduce angle
  of search at each forward step
• Dynamic lighting conditions remembers last two
  light intensity levels

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Demonstration
Demo available at http//www.pmg.lcs.mit.edu/chmo
h/demo.avi
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Conclusion

• Lessons learnt
• Physical robots real world environment ?
  simulation
• Too many concurrent tasks causes problems
  complexity, time-slicing / polling
• Sensors does not always work as expected
• Non-uniformity of robot movement (due to battery
  levels / motors)
• Too much abstraction is not good for robot
  (real-time) control
• Future work
• Energy level real battery level (robot action
  dependent on battery level)
• Emergent behavior of multiple robots
• Learning algorithm optimization
• More efficient genetic algorithm