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Chuang-Hue Moh Spring 2002

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

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Chuang-Hue Moh Spring 2002

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  1. 6.836 Embodied Intelligence: Final Project Chuang-Hue Moh Spring 2002

  2. Evolution in the Micro-Sense: An Autonomous Learning Robot Chuang-Hue Moh 6.836 Embodied Intelligence, Spring 2002

  3. 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 robot’s controller as a form of learning.

  4. 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…)

  5. Subsumption Architecture Left Bumper Sensor Recharge Collision Resolve Collision Detect Right Bumper Sensor Proximity Sensor s s s Light Sensor Energy Level Turn Right s Right Motor s Explore Move Forward s s Left Motor s Random Number Turn Left

  6. 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

  7. 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

  8. 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

  9. Demonstration Demo available at http://www.pmg.lcs.mit.edu/~chmoh/demo.avi

  10. 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

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