Chuang-Hue Moh Spring Embodied Intelligence: Final Project

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

Goal  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. 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]

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…)

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

Robot Implementation  Lego RCX tm 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

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

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

Demonstration Demo available at

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