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

Current State (1) Source: PlusTech, Inc 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) AttilaTITAN VIII RHex Lauron IICWU Hexapod

Applications Urban 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 Force Low High

Movies

Intelligent Leg Systems Intelligent: 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-Propagation Input 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

Quiz In 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 CNH Temp LMH Humidity Temperature Humidity ColdNiceHot Off Slow Med Slow Med Fast Med Fast Flyin’ Low Med High Fan Power (V) N 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)

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)

Conclusions Machine Learning (ANN) Computational Evolution (GA) Digital Interfaces with Analog World (FL) Combination of Strategies