chuang-hue moh spring 2002 6.836 embodied intelligence: final project
TRANSCRIPT
Evolution in the Micro-Sense: An Autonomous Learning Robot
Chuang-Hue Moh
6.836 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…)
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Explore
Recharge Recharge
Explore
Right Motor
Left Motor
Move Forward
Turn Right
Turn Left
s
sRandom Number
Exploress
Recharge
Light Sensor
Energy Level
Subsumption Architecture
Collision Detect
s
s
Proximity Sensor
Collision Resolve
Left Bumper Sensor
Right Bumper Sensor
ss
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
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
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