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Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
- Introduction to Robotics
Introduction to RoboticsLecture 13
Jianwei Zhang, Lasse Einig[zhang, einig]@informatik.uni-hamburg.de
University of HamburgFaculty of Mathematics, Informatics and Natural SciencesDepartment of InformaticsTechnical Aspects of Multimodal Systems
July 08, 2016
J. Zhang, L. Einig 456
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems Introduction to Robotics
Outline
IntroductionKinematic EquationsRobot DescriptionInverse Kinematics for ManipulatorsDifferential motion with homogeneous transformationsJacobianTrajectory planningTrajectory generationDynamicsRobot Control
J. Zhang, L. Einig 457
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems Introduction to Robotics
Outline (cont.)
Task-Level Programming and Trajectory GenerationTask-level Programming and Path PlanningTask-level Programming and Path PlanningArchitectures of Sensor-based Intelligent Systems
The CMAC-ModelThe Subsumption-ArchitectureControl Architecture of a FishProcedural Reasoning SystemBehavior FusionHierarchyArchitectures for Learning Robots
J. Zhang, L. Einig 458
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems Introduction to Robotics
Architectures of Sensor-based Intelligent Systems
OverviewI Basic behaviorI Behavior fusionI SubsumptionI Hierarchical architecturesI Interactive architectures
J. Zhang, L. Einig 459
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems Introduction to Robotics
The Perception-Action-Model with Memory
perception behaviors
memory
actionsensors
J. Zhang, L. Einig 460
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - The CMAC-Model Introduction to Robotics
CMAC-Model
CMAC: Cerebellar Model Articulation Controller
S sensory input vectors (firing cell patterns)A association vector (cell pattern combination)P response output vector (A ·W )W weight matrix
The CMAC model can be viewed as two mappings:
f : S −→ A
g : A W−→ P
J. Zhang, L. Einig 461
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - The CMAC-Model Introduction to Robotics
CMAC-Model (cont.)
S1
S2
S3
S4
S5
A1
A2
A3
A4
A5
A6
R1
R2
R3
W1,1
W1,2
W1,3
W1,4
W1,5
W1,6
W2,1
W2,2
W2,3
W2,4
W2,5
W2,6
W3,1
W3,2
W3,3
W3,4
W3,5
W3,6
R1
R2
R3
sensory cells association cells adjustable weight response cells
J. Zhang, L. Einig 462
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - The CMAC-Model Introduction to Robotics
B-Spline-Model
The B-Spline model is an ideal implementation of theCMAC-Model. The CMAC model provides an neurophysiologicalinterpretation of the B-Spline model.
J. Zhang, L. Einig 463
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - The CMAC-Model Introduction to Robotics
Alvinn – Visual Navigation
CMU – Carnegie Mellon UniversityJ. Zhang, L. Einig 464
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - The CMAC-Model Introduction to Robotics
Action-oriented Perception
Percept-1 Behavior-1 Response-1Percept-2 Behavior-2 Response-2Percept-3 Behavior-3 Response-3
Fusion
Percept-1
Percept-2
Percept-3
Percept Behavior Response
Percept-1
Percept-2
Percept-3
Behavior Responseone of
J. Zhang, L. Einig 465
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - The Subsumption-Architecture Introduction to Robotics
The Subsumption ArchitectureI hierarchical structure of behaviorI higher level behaviors subsumpt lower level behaviors
Modeling/planning paradigm
I program composed of sequence of stepsI functional units transform sensory data snapshots to actionsI series of actions achieve a specified goal
Subsumption architecture
I e.g. the mobile robot Rug WarriorI begins with cruise behaviorI moves forward
I a follow behavior subsumpts the cruise behaviorI triggered by photo cellsI moves towards the light
I avoid behavior suppresses follow and cruiseI infrared sensors detect imminent collisionI attempts to avoid the obstacles
I escape behavior uses bumper sensor to sense collisionsI reverses and bypasses the obstacleI top-level behavior, detect sound pattern, makes the robot play a
tuneI detect sound pattern subsumpts all lower level behaviors by
stopping or following the sound pattern
cruise
follow
avoid
escape
detect sound pattern
S
S
S S
photo cells
IR detector
bumper
microphone piezo buzzer
motors
[18]
J. Zhang, L. Einig 466
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - The Subsumption-Architecture Introduction to Robotics
Foraging and FlockingI multi-robot architectureI basic behaviors are sequentially executed
I composite behaviors built from basicsI by vector summationI hierachical composite behaviorsI include composite behaviors
homing
dispersion
aggregation
safe wandering
following
basic behaviors composite behaviors
sensory inputs/sensory conditions
actuator output
flocking
foraging=wandering+dispersion+following+homing+flocking
herding=wandering+surrounding+flockingsurrounding=wandering+following+aggregationflocking=wandering+aggregation+dispersion
[19]J. Zhang, L. Einig 467
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - The Subsumption-Architecture Introduction to Robotics
Cockroach Neuron / Behaviors
ringfrequency
synapscurrents currents
R Ccell membrane
thresholdvoltage
SENSORS BEHAVIORS
ingestion
findingfood
edgefollowing
wandering
mouthtactile
mouthchemical
antennachemical
antenna
J. Zhang, L. Einig 468
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Control Architecture of a Fish Introduction to Robotics
Control Architecture of a Fish
Control and information flow in artificial fishPerception sensors, focuser, filterBehaviors behavior routinesBrain/mind habits, intention generatorLearning optimizationMotor motor controllers, actuators/muscles
J. Zhang, L. Einig 469
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Control Architecture of a Fish Introduction to Robotics
Control Architecture of a Fish (cont.)
J. Zhang, L. Einig 470
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Procedural Reasoning System Introduction to Robotics
Procedural Reasoning System
INTERPRETERMONITOR COMMANDGENERATOR
OPERATORINTERFACE
plansdesires intentionsbeliefs
actuatorssensors [20]
J. Zhang, L. Einig 471
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Behavior Fusion Introduction to Robotics
Hierarchical Fuzzy-Control of a Robot
subgoalapproaching
local collisionavoidance
commandfrom others
situationevaluation
knowledge base
worldmodel
fuzzy controller
subgoal planning
robot perception
replanningsubgoal sequence
commandingof other robots,human users
[21]
J. Zhang, L. Einig 472
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Behavior Fusion Introduction to Robotics
Behavior FusionFuzzy rules evaluate current situation.Situation evaluation determines 3 fuzzy-parameters
I the priority K of the LCA rule baseI the replanning selectorI NextSubgoal (whether a subgoal has been reached)
Typical rule IF (SL85 IS HIGH) AND (SL45 IS VL) AND (SLR0 IS VL) AND(SR45 IS VL) AND (SR85 IS VL) THEN (Speed IS LOW) AND (Steer ISPM) K IS HIGH AND Replan IS LOW
Translation If the leftmost proximity sensor detects an obstaclewhich is near and the other sensors detect no obstacle atall, then steer halfway to the right at low speed. Mainlyperform obstacle avoidance. No re-planning required.
Coordination of multiple rule basesSpeed = SpeedLCA · K + SpeedSA · (1− K )Steer = SteerLCA · K + SteerSA · (1− K )
LCA: Local Collision Avoidance SA: Subgoal Approach
J. Zhang, L. Einig 473
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Hierarchy Introduction to Robotics
Hierarchy
Real-Time Control System (RCS)I RCS reference model is an architecture for intelligent systems.I Processing modes are organized such that the BG (Behavior
Generation) modules form a command tree.I Information in the knowledge database is shared between WM
(World Model) modules in nodes within the same subtree.
[22]
Examples of functional characteristics of the BG and WM modules:
J. Zhang, L. Einig 474
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Hierarchy Introduction to Robotics
Hierarchy (cont.)
J. Zhang, L. Einig 475
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Hierarchy Introduction to Robotics
Hierarchy (cont.)
J. Zhang, L. Einig 476
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Hierarchy Introduction to Robotics
Hierarchy (cont.)
G1 M1 H1
G2 M2 H2
G3 M3 H3
G4 M4 H4
from sensors to servos
to higher levelsof interpretation
from a prioriknowledge base
from higher levelsof control
parameters ofsensed world
parameters ofexpected world
specification ofworld model error
parameters ofworld model
parameters of higherlevel world model
specification ofsub-task
[22]
J. Zhang, L. Einig 477
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Hierarchy Introduction to Robotics
Sensor-Hierarchy
Level 0
Level I
Level II
Level III
Level IV
raw data
Properties of points in space
Descriptions of aggregatesof points and their features(object elements)
Recognition of aggregates offeatures (objects)
Descriptions of relations ofobjects or unrecognized aggre-gates
Recognition of relations ofobjects
[22]
J. Zhang, L. Einig 478
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Hierarchy Introduction to Robotics
Other examples
[23]
J. Zhang, L. Einig 479
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Architectures for Learning Robots Introduction to Robotics
An Architecture for Learning Robots
[24]
J. Zhang, L. Einig 480
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Architectures for Learning Robots Introduction to Robotics
AuRA Architecture
schema controllermotor perceptual
REPRESENTATION
learning
plan recognitionuser profile
spatial learning
opportunism
on-lineadpation
user input
user intentions
spatial goals
missionalterations
teleautonomy
mission planner
spatial reasoner
plan sequencer
Actuation Sensing
HiearchicalComponent
ReactiveComponent
[25]J. Zhang, L. Einig 481
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Architectures for Learning Robots Introduction to Robotics
Atlantis Architecture
CONTROL
SEQUENCING
DELIBERATIVE
SENSORS ACTUATORS
statusactivation
invocationresults
[26]
J. Zhang, L. Einig 482
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Architectures for Learning Robots Introduction to Robotics
RACERobustness by Autonomous Competence Enhancement
Blackboard
HTN PlannerOWLOntology
ExecutionMonitor
Robot
Capabil-ities
Conceptualizer
Reasoningand
Interpretation
ExperienceExtractor/Annotator
UserInterface
Perception
PerceptualMemory
OWLconcepts
OWLconcepts
new concepts
experiences
fluents
fluentsfluents
expe-riences instructions
instructions
fluents
inital state,goal plan
plan,goal
fluents,schedule
planROSactions
actionresults
continuousdata
sensor data
[27]
J. Zhang, L. Einig 483
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Architectures for Learning Robots Introduction to Robotics
[1] K. Fu, R. González, and C. Lee, Robotics: Control, Sensing, Vision,and Intelligence.McGraw-Hill series in CAD/CAM robotics and computer vision,McGraw-Hill, 1987.
[2] R. Paul, Robot Manipulators: Mathematics, Programming, andControl : the Computer Control of Robot Manipulators.Artificial Intelligence Series, MIT Press, 1981.
[3] J. Craig, Introduction to Robotics: Pearson New InternationalEdition: Mechanics and Control.Always learning, Pearson Education, Limited, 2013.
[4] J. F. Engelberger, Robotics in service.MIT Press, 1989.
[5] W. Böhm, G. Farin, and J. Kahmann, “A Survey of Curve andSurface Methods in CAGD,” Comput. Aided Geom. Des., vol. 1,pp. 1–60, July 1984.
J. Zhang, L. Einig 483
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Architectures for Learning Robots Introduction to Robotics
[6] J. Zhang and A. Knoll, “Constructing fuzzy controllers with B-splinemodels-principles and applications,” International Journal ofIntelligent Systems, vol. 13, no. 2-3, pp. 257–285, 1998.
[7] M. Eck and H. Hoppe, “Automatic reconstruction of b-splinesurfaces of arbitrary topological type,” in Proceedings of the 23rdAnnual Conference on Computer Graphics and InteractiveTechniques, SIGGRAPH ’96, (New York, NY, USA), pp. 325–334,ACM, 1996.
[8] M. C. Ferch, Lernen von Montagestrategien in einer verteiltenMultiroboterumgebung.PhD thesis, Bielefeld University, 2001.
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[10] J. H. Reif, “Complexity of the mover’s problem and generalizationsextended abstract,” Proceedings of the 20th Annual IEEE
J. Zhang, L. Einig 483
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Architectures for Learning Robots Introduction to Robotics
Conference on Foundations of Computer Science, pp. 421–427,1979.
[11] J. T. Schwartz and M. Sharir, “A survey of motion planning andrelated geometric algorithms,” Artificial Intelligence, vol. 37, no. 1,pp. 157–169, 1988.
[12] J. Canny, The complexity of robot motion planning.MIT press, 1988.
[13] T. Lozano-Pérez, J. L. Jones, P. A. O’Donnell, and E. Mazer,Handey: A Robot Task Planner.Cambridge, MA, USA: MIT Press, 1992.
[14] O. Khatib, “The potential field approach and operational spaceformulation in robot control,” in Adaptive and Learning Systems,pp. 367–377, Springer, 1986.
[15] J. Barraquand, L. Kavraki, R. Motwani, J.-C. Latombe, T.-Y. Li,and P. Raghavan, “A random sampling scheme for path planning,”
J. Zhang, L. Einig 483
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Architectures for Learning Robots Introduction to Robotics
in Robotics Research (G. Giralt and G. Hirzinger, eds.),pp. 249–264, Springer London, 1996.
[16] R. Geraerts and M. H. Overmars, “A comparative study ofprobabilistic roadmap planners,” in Algorithmic Foundations ofRobotics V, pp. 43–57, Springer, 2004.
[17] K. Nishiwaki, J. Kuffner, S. Kagami, M. Inaba, and H. Inoue, “Theexperimental humanoid robot h7: a research platform forautonomous behaviour,” Philosophical Transactions of the RoyalSociety of London A: Mathematical, Physical and EngineeringSciences, vol. 365, no. 1850, pp. 79–107, 2007.
[18] R. Brooks, “A robust layered control system for a mobile robot,”Robotics and Automation, IEEE Journal of, vol. 2, pp. 14–23, Mar1986.
[19] M. J. Mataric, “Interaction and intelligent behavior.,” tech. rep.,DTIC Document, 1994.
J. Zhang, L. Einig 483
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Architectures for Learning Robots Introduction to Robotics
[20] M. P. Georgeff and A. L. Lansky, “Reactive reasoning andplanning.,” in AAAI, vol. 87, pp. 677–682, 1987.
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[24] T. Fukuda and T. Shibata, “Hierarchical intelligent control forrobotic motion by using fuzzy, artificial intelligence, and neuralnetwork,” in Neural Networks, 1992. IJCNN., International JointConference on, vol. 1, pp. 269–274 vol.1, Jun 1992.
J. Zhang, L. Einig 483
Universität HamburgDER FORSCHUNG | DER LEHRE | DER BILDUNG
MIN FacultyDepartment of Informatics
Architectures of Sensor-based Intelligent Systems - Architectures for Learning Robots Introduction to Robotics
[25] R. C. Arkin and T. Balch, “Aura: principles and practice in review,”Journal of Experimental & Theoretical Artificial Intelligence, vol. 9,no. 2-3, pp. 175–189, 1997.
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