Intelligent Legged Robot Systems AME498Q/598I Intelligent Systems 19NOV03

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