Mechatronics und Bewegungssteuerung des humanoiden

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F 4.13 “F 4.13 “SteuerungSteuerung und und RegelungRegelung von von RoboternRobotern””24. 24. JanuarJanuar 2007, Karlsruhe 2007, Karlsruhe

Mechatronics und Bewegungssteuerung des humanoiden Roboters ARMAR-III

T. Asfour and R. Dillmann

University of Karlsruhe (TH)Institute of Computer Science and Engineering (CSE)

http://wwwiaim.ira.uka.de

http://sfb588.uni-karlsruhe.de

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SFB 588: Project Research Topics

Cooperation

Learning

MultimodaleInteraction

Programming by Demonstration

InteractiveLearning

Human-RobotInteraction

Analysis of Human Motion

Speech

Gesture/mimic

TactileInteraction

Mechatronics and Motor Control

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Outline of the talk

The humanoid robot ARMAR-IIIMechatronicsHardware architecture Software framework

Implemented Skills • Object recognition and localization • Inverse Kinematics• Path Planning/Collision detection

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Kinematics of upper body

40Upper body

446

3

43

16

222

1

1

2

223

3

4

8

WristElbowShoulder

Torso

NeckEyes

Hand

totalamountD.O.FJoint

θ1

θ7θ6

θ4

θ2

θ11

θ9

θ10

θ8

θ5

θ3

θ12

θ13

θ14θ1

θ7θ6

θ4

θ2

θ11

θ9

θ10

θ8

θ5

θ3

θ12

θ13

θ14

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Range of motion of upper body

θ1

θ7θ6

θ4

θ2

θ11

θ9

θ10

θ8

θ5

θ3

θ12

θ13

θ14θ1

θ7θ6

θ4

θ2

θ11

θ9

θ10

θ8

θ5

θ3

θ12

θ13

θ14

-180° to 180°-10° to 60°-20° to 20°

θ12θ13θ14

Torso

-180° to 180°-45° to 45°-45° to 45°-60° to 60°

θ8θ9θ10θ11

Neck

-180° to 180°-45° to 180°-10° to 180°

θ5θ6θ7

Shoulder

-90° to 90°-10° to 150°

θ3θ4

Elbow

-30° to 30°-60° to 60°

θ1θ2

Wrist

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ARMAR-III: Head

Neck• 4 DOF (Pitch-Roll-Yaw-Pitch)• Incremental and absolute

position sensors for all DOFs

Head: • 4 Dragonfly Color cameras,

640x480 (30 Hz), Firewire,2,9 mm und 12 mm micro lenses

• Framerate: 30fps max.• Resolution: 640x480 max.• common tilt and independent pan• Variable stereo calibration• 6 microphones

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ARMAR III: Upper body

7 DOF Arms• Weight: 6 kg, max. Payload: 3 kg• Sensors: each joint

- motor encoders- Optical encoder in the axis- Joint torque sensor

(www.novatechuk.demon.co.uk)

• 6 DOF Force/Torque sensor (ATI) • Artificial skin

- Shoulder- Upper arm and forearm

3 DOF Torso• 2 Embedded PCs

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ARMAR III: 5-Finger Hands

Anthropomorphic5-finger lightweight hand

• 8 DOF• weight ca. 200 g• Holding force 2 kg• On-board controller• Fluidic/pneumatic actuators• Anthropomorphic shape

and motion characteristics

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ARMAR III: Platform

Application area: Kitchen

holonomic mobile platform• wheels with passive rolls at the

circumference (Mecanum wheels or Omniwheels)

• 3 laser scanner (Hokuyo Automatic)

Platform specification:• Height: 700 mm• Weight: 90 kg• Max. speed: 1.2 m/s• Power supply (whole system):

8 h with 25% drive• Spring-damper combination to reduce

vibrations

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Computer architecture

2 GHz PC/104• vision

• tracker

• object recognition

2 GHz PC/104• vision

• tracker

• object recognition

933 MHz PC/104• position control

• torque control

• movement coordination

933 MHz PC/104• position control

• torque control

• movement coordination

933 MHz PC/104• platform control

• localization

• collision avoidance

933 MHz PC/104• platform control

• localization

• collision avoidance

1,6 GHz IPC• speech recognition

• speech synthesis

• acoustic localization

1,6 GHz IPC• speech recognition

• speech synthesis

• acoustic localization

UCoM #1• motor control

• encoder acquisition

• current sensing



• current sensing

1,6 GHz IPC• Global models

• Fileserver

1,6 GHz IPC• Global models

• Fileserver



• current sensing



• current sensing

Task planning level

Task coordination level

Task execution level

Gigabit Ethernet

4 CAN-Busses

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Universal Controller Module (UCoM)

Motor board• Size: 80mm * 70mm * 20mm

• Up to 3 Motors (24V up to 5A)

• 6 Encoder-Ports (6-pin, 2 power supply, 4 IOs)

• Serial Peripheral Interface (SPI)

• 3 General Purpose IOs

DSP/FPGA:• 80MHz DSP: Motorola Hybrid Controller DSP56F803

- Size: 57mm * 70mm * 15mm- Interfaces: CAN, SCI, SPI, JTAG- Analog Ports: 2 * 4 channels ADC - Motorcontrol: 6 PWM

• External RAM • FPGA: Altera EPF10k30• FPGA-Configuration: Altera EPC2-ROM• All Components programmable via JTAG• DSP programmable via CAN-Bus

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Software Framework

Modular Controller Architecture http://www.mca2.org

• modular, network transparent and real-time capable C/C++ framework for controlling robots

• Supports distributed computing • The main platform is Linux/RTLinux;

support for Win32 and MCA OS/X also exists.

• Easy to parameterize; to administrate• Modules with defined interfaces• Combination of modules into groups, which

act like any other module but with more complex behavior.

• Reusability of modules

Linux

RT

Linux

RT

Tcp-socket

Linux

RT

Linux

RT

RTAI-Linux

Linux/Windows

RT-FIFO

Planning Level

Coordination Level

Execution Level

Firewire

CAN

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Implementation in ARMAR-III

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Object Recognition

Recognition of colored objects Segmentation by color [IROS 2006]

• Appearance-based recognition using a global approach

• Model-based generation of view sets• Combination of stereo vision and stored

orientation information for 6D-localization (with respect to a given 3D model)

Recognition of textured objects Recognition using local features [Humanoids 2006]

• Calculation of consistent featureswith respect to the pose of the object by using the Hough transform

• 2D-localization using image pointcorrespondences

• 3D-localization using stereo vision

The robot‘s view(left camera)

Recognition/Localizationresult

Correspondences between learned view and view in scene

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Integrating Vision Toolkit (IVT)

Developed by Pedram Azad (IAIM, Karlsruhe)

Available under: http://ivt.sourceforge.net

Features:• Integrating various cameras and camera interfaces (Dragonfly,

Digiclops/Bumblee, Videre, CMU1394, Unicap, Quicktime, uncompressed AVIs, bitmap sequences, ...)

• Using QT for visualization and creation of GUIs• Encapsulating OpenCV for functionality which has not been

implemented yet• Platform independent: being constantly used and tested under

Linux, Mac OS and Windows

IVT applications are portable automatically

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Natural Interaction

Multimodal human-robot dialogs• For robot control, requesting

services, knowledge acquisition• Task oriented dialogs• Error handling• Learning dialog strategies with

reinforcement learning

Speech recognition for continuous speech (German/ English)

• Semantic context free grammars for recognition and understanding

• Tight coupling of speech recognition and dialog manager

• Close and distant speech

Audio and visual tracking, localization and sound classification

ARMAR-III, Kitchen

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Inverse Kinematics for Reaching and PointingInverse Kinematics for Reaching and Pointing

SoechtingSoechting and Flanders:and Flanders: SSensorimotor transformation model that maps the Cartesian wrist position to a natural arm posture using four representation parameters

ShShoulderoulder

ElbowElbow

WristWrist

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Inverse Kinematics for Reaching and PointingInverse Kinematics for Reaching and Pointing

Mapping of the posture representation parameters into the joint angles of the arm, which are responsible for the position of the wrist (θθ11, , θθ22, , θθ33, , θθ44))

Final adjustment step: Use the remaining DOFs (θθ55, , θθ66, , θθ77) ) to match the exact position und orientation of the TCP

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Collision Detection/Motion Planning

Armar-III Model• Detailed visualization (~20.000 triangles)• Simplified model for collision detection (~450

triangles)

Collisions with environment / Self collisions• Hierarchical model of scene and objects

(triangle meshes)• test on triangle level only for nearby parts of

the meshes (No unnecessary collision tests )• Real-time performance (query <1 ms)

Distance calculations ~ 3 msMotion planning with Rapidly Exploring Random Trees (RRT)

• Sampling-based approach

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Manipulation Planning Among Movable Obstacles

ARMAR: 20 DOF

Objects:• 4 fixed• 18 movable

Goal: Ketchup

Joint work with Mike Stilman, Jan-Ullrich Schamburek, and James Kuffner [ICRA 2007]

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ARMAR III: Retrieve Ketchup

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Conclusions

ARMAR-III• Humanoid robot for household applications • distributed hardware architecture • Software framework• Integration of motor control, vision and speech• All components on-board (Power supply, computers, ... )

Contributions • Mechanics developed at IPEK

(Prof. Albers, www.ipek.uni-karlsruhe.de)• 5-Finger hand at developed FZK

(Prof. Bretthauer, www.fzk.de) • Artificial skin developed at IPR

(Prof. Wörn, wwwipr.ira.uka.de)

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Thank you ...

... for your attention.

Documents

Mechatronics und Bewegungssteuerung des humanoiden