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Intelligent Legged Robot
Systems
AME498Q/598I
Intelligent Systems
19NOV03
Current State (1)
Source: PlusTech, Inc.
1995
1991
Forest Walker Hexapod
Current State (2)
Source: PlusTech, Inc.
Why Use Legged Locomotion?
Source: Machines That Walk
Fuel Economy
High Speed
Great Mobility
“The Great Chase” by Thomas D. Magelsen
Better Isolation from Terrain
Less Environmental Damage
Timeline (1)
The G.E. Quadruped
ASV Hexapod
OSU Hexapod
Timeline (2)
Attila TITAN VIII RHex
Lauron II CWU Hexapod
ApplicationsUrban Reconnaissance
Mapping over uneven terrain
Intelligence ?
Aspects of A Legged Vehicle
Design and actuation of legs and sensors
Control of Legged Vehicles
Gait Planning
Navigation, Self-Localization, Map-Building, etc.
Walk, Run, Trot, etc.
Position Control and Compliant ForceLow
High
Movies
Intelligent Leg SystemsIntelligent:
A capability of a system to sustain desired behavior under the condition of uncertainty.
>> Artificial Neural Network, Genetic Algorithms, Fuzzy Logic
Examples in Legged Robot
Foothold/slip detection and reflexes
Contour Predictions Ability
Disturbance Rejection and Fault Tolerance
Continuum Gait Generation
Artificial Neural Network (ANN)An imitation of biological nervous system (i.e. brain)
(+) Learning ability and adaptive-ness
(-) Ill-suited for logical and arithmetic operations
Weighting Factors, Unsupervised/Supervised Training,
Feed Forward and Back-PropagationInput
Output
Artificial Neural Network (ANN)
Differences between ANN and Traditional Computing
ANN is not sequential (one problem rule at a time), rather it is parallel
Learn by examples, rather than by rules (i.e. expert systems)
Computational Cost
ANNTraditional Program
Memory Cost More limitedGrows indefinitely
Can traditional program learn?
Same for all inputsGet worse w/ experience
Genetic Algorithm (GA)Search procedure using the mechanics of natural selections
Used to solve difficult optimization problems (with many local optima)
Differences between GA and Traditional Methods (GB)
GA uses a set of points rather than a single point
GA is probabilistic in nature, not deterministic
GA is inherently parallel
Gene, Chromosome, Fitness Function, Asexual/Sexual Reproduction, Crossover, Mutation
QuizIn term of exemplars, give one difference between ANN and GA!
Answer:
ANN requires well-chosen, representative exemplars to do well. GA has to make its own exemplars
Fuzzy Logic Controller (FLC)‘Crisp’ conclusion based upon noisy, imprecise inputs
Applications: Cruise Control, Washing Machines, etc.
Linguistic Terms, Membership Functions, Fuzzification, Inference, Defuzzification
C N H1
060 9030
Temp
L M H1
050 7525
Humidity
Temperature
Hu
mid
ity
Cold Nice Hot
Off
Slow
Med
Slow
Med
Fast
Med
Fast
Flyin’
Low
Med
High
Fan Power (V)
1
010 155 20
0.65N
0.35C
0.5M
0.5H
Open Forum
What are the shortfalls of FLC?
Answer:
Needs experts for rule discovery. Requires a lot of fine tuning.
FLC to Find Foothold (1) Source: AN710 Philips Semiconductor
FLC to Find Foothold (2)
FLC to Find Foothold (2)
Genetic-Fuzzy (1) Source: Design of a Genetic-Fuzzy System for Planning Optimal Path and Gait for Six-Legged Robot
Genetic-Fuzzy (2)
Genetic-Fuzzy (3)
Genetic-Fuzzy (4)
Predicting Terrain Contours (1)Source: Predicting Terrain Contours using a Feed-Forward Neural Networks
Predicting Terrain Contours (2)
Predicting Terrain Contours (3)
ConclusionsMachine Learning (ANN)
Computational Evolution (GA)
Digital Interfaces with Analog World (FL)
Combination of Strategies