Lecture 7-2015.pdf

Recap from last week:Recap from last week:Recap from last week:Recap from last week: Robot Dynamics Robot Dynamics Newton EulerNewton Euler Robotic control Robotic control position controlposition control

Independent joint controllerIndependent joint controller Computed torque controlComputed torque control

Prepared by Gu Fang (lecture 7)Prepared by Gu Fang (lecture 7) 11

Contents of week:Contents of week:Contents of week:Contents of week: Robot force controlRobot force control Robot trajectory planningRobot trajectory planning Artificial Neural Network Artificial Neural Network -- introductionintroduction


44..11..33 AdaptiveAdaptive controlcontrol ModelModel--referencereference adaptiveadaptive controlcontrol (MRAC)(MRAC)


SelfSelf--tuningtuning controlcontrol

Assumes that interaction fforces among the joints are negligible.


44..22 ForceForce ControlControl

44..22..11 StaticStatic force/torqueforce/torque relationshiprelationshipLetLet xx bebe thethe virtualvirtual endend effectoreffectorss displacementdisplacementLetLet xx bebe thethe virtualvirtual endend--effectoreffector ss displacementdisplacementFF bebe thethe endend--effectoreffector forceforce.. FF=(=(FFxx,, FFyy,, FFzz,, mmxx,, mmyy,, mmzz))TT

bebe thethe virtualvirtual jointjoint displacementdisplacement bebe thethe jointjoint torquestorquesjj qq

x J FTx Tx JFT J T

FJ TPrepared by Gu Fang (lecture 7)Prepared by Gu Fang (lecture 7) 55

44..22..22 ImpedanceImpedance controlcontrol

F KPx JTF K J

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T

Impedance controller with NO damping

JTKPJ

c K ( d )Impedance controller with NO damping

c ( )Impedance controller with damping & gravity compensation

c K ( d )KV ( d ) c( )Prepared by Gu Fang (lecture 7)Prepared by Gu Fang (lecture 7) 66

4.2.3 Passive compliance

Remote Centre Compliance (RCC) deviceRemote Centre Compliance (RCC) device


5.5. Robot Trajectory PlanningRobot Trajectory Planningj y gj y g55..11 IntroductionIntroductionPath vs trajectory:Path vs trajectory:Path vs. trajectory:Path vs. trajectory:

J i tJ i t C t iC t i d i tid i tiJointJoint--spacespace vsvs.. CartesianCartesian--spacespace descriptionsdescriptions::

BasicsBasics ofof trajectorytrajectory planningplanningAsynchronised trajectories:Asynchronised trajectories:Asynchronised trajectories:Asynchronised trajectories:


SynchronisedSynchronisedSynchronisedSynchronisedtrajectoriestrajectories::

CartesianCartesian spacespacetrajectoriestrajectories::


55..22 JointJoint--SpaceSpace TrajectoryTrajectory planningplanning

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55..33 CartesianCartesian--SpaceSpace TrajectoryTrajectory planningplanning55..33 CartesianCartesian SpaceSpace TrajectoryTrajectory planningplanningWhenWhen thethe robotrobot pathpath isis defineddefined inin thethe CartesianCartesian--space,space, therethereareare generallygenerally twotwo methodsmethods thatthat cancan bebe usedused forfor trajectorytrajectoryareare generallygenerally twotwo methodsmethods thatthat cancan bebe usedused forfor trajectorytrajectoryplanningplanning::

ii WhenWhen thethe positionposition velocitiesvelocities ofof thethe endend--effectoreffector alongalong thetheii.. WhenWhen thethe position,position, velocitiesvelocities ofof thethe endend--effectoreffector alongalong thethepathpath areare defined,defined, thethe jointjoint positionspositions cancan bebe calculatedcalculated usingusinginverseinverse kinematicskinematics equations,equations, whilewhile thethe jointjoint velocitiesvelocities cancanq ,q , jjbebe calculatedcalculated usingusing JacobianJacobian calculationcalculation

iiii.. WhenWhen onlyonly thethe positionspositions ofof thethe endend--effectorseffectors areare defined,defined,yy pp ,,thethe CartesianCartesian trajectoriestrajectories cancan bebe plannedplanned usingusing thethe samesamemethodmethod introducedintroduced aboveabove.. AfterAfter thethe CartesianCartesian trajectorytrajectory isisobtained,obtained, thethe inverseinverse kinematicskinematics equationsequations areare thenthen usedused totocalculatecalculate thethe jointjoint trajectoriestrajectories..


Artificial IntelligenceArtificial IntelligenceggBrain inspiredBrain inspiredArtificial Neural networksArtificial Neural networks

Human reasoning inspiredHuman reasoning inspiredFuzzy LogicFuzzy Logic

Human evolution inspiredHuman evolution inspiredGenetic AlgorithmGenetic Algorithm

Animal behaviour inspired Animal behaviour inspired Ant ColonyAnt ColonyParticle Swarm Particle Swarm OptimisationOptimisation


6. Artificial Neural Networks6. Artificial Neural Networks6.16.1 IntroductionIntroduction6.1.16.1.1 DefinitionsDefinitions

WhatWhat isis anan ANN?ANN?Neural networks are a promising new generation of Neural networks are a promising new generation of p g gp g ginformation processing systems that demonstrate the ability to information processing systems that demonstrate the ability to learn, recalllearn, recall, and , and generalize from training patterns or datageneralize from training patterns or data. . I i l ifi i l l k (ANN )I i l ifi i l l k (ANN )In particular, artificial neural networks (ANNs) are systems In particular, artificial neural networks (ANNs) are systems that are deliberately constructed to make use of some that are deliberately constructed to make use of some organizational principles resemblingorganizational principles resembling those of thethose of the humanhumanorganizational principles resemblingorganizational principles resembling those of the those of the human human brainbrain..

It is aIt is a neurallyneurally inspired mathematical modelinspired mathematical modelIt is a It is a neurallyneurally inspired mathematical model.inspired mathematical model. It consists of a large number of highly interconnected It consists of a large number of highly interconnected

processing elements.processing elements.


p gp g

Its connections (Its connections (weightsweights)) holdhold thethe knowledgeknowledgeIts connections (Its connections (weightsweights) ) holdhold the the knowledgeknowledge.. A processing element can dynamically respond to its A processing element can dynamically respond to its

input stimulus, and the response completely dependsinput stimulus, and the response completely dependsinput stimulus, and the response completely depends input stimulus, and the response completely depends on its local information; that is, the input signals arrive on its local information; that is, the input signals arrive at the processing element via impinging connections at the processing element via impinging connections and connection weights.and connection weights.

It has the ability to learn, recall, and generalize from It has the ability to learn, recall, and generalize from t i i d t b i i dj ti th tit i i d t b i i dj ti th titraining data by assigning or adjusting the connection training data by assigning or adjusting the connection weights.weights.Its collective behavior demonstrates the computationalIts collective behavior demonstrates the computational Its collective behavior demonstrates the computational Its collective behavior demonstrates the computational power, and no single neuron carries specific power, and no single neuron carries specific information (distributed representation property).information (distributed representation property).information (distributed representation property).information (distributed representation property).


WhyWhy itit isis calledcalled anan ANN?ANN?Neural networks are an attempt at creating machines that work Neural networks are an attempt at creating machines that work in a similar way to the human brain by building these machines in a similar way to the human brain by building these machines using components that behave like biological neuronsusing components that behave like biological neuronsusing components that behave like biological neurons.using components that behave like biological neurons.

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WhatWhat cancan anan ANNANN do?do?


6.1.26.1.2 Structure of ANNsStructure of ANNsBoolean NNsBoolean NNs WISARDWISARD


Biologically inspired NNs MP (McCulloch-Pitts) neuron( ) ADALINE

Types of learning rulesTypes of learning rules Supervised learning Reinforced learning


g Unsupervised learning

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Lecture 7-2015.pdf