Natchanon Wongwilai Adviser: Nattee Niparnan, Ph.D. M.Eng. 1
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- Natchanon Wongwilai Adviser: Nattee Niparnan, Ph.D. M.Eng.
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- Introduction How to grasp?, Why failed to grasp?, Goal Related
Works Vision-based grasping, Manipulation under uncertainty Our
Problem Challenge, Proposed method Etc. Scopes, Work plan 2
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[http://spectrum.ieee.org/robotics/robotics-software/slideshow-born-bionic/0]
!? 3 ? ? ? ? ? ? Model = 0.53 W = 39 g
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- 2D [Borst el at.,00; Chinellato el at.,05; Calli el at.,11;...]
3D [Miller el at.,03; Goldfeder el at.,07; Hubner el at.,08;...]
2.5D [Richtsfeld el at.,08;...] Others [Saxena el at.,08;...]
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- 6 (Video)
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- The most common failure mode I've seen is that the closing
fingers bump the object so that the fingers don't touch the
intended contact points. Then the fingers knock the object
completely out of the grasp. I think the causes are localization
errors from the perception system and asking the robot to carry out
an inherently dynamic task that was planned with static analysis
tools. Jeff Trinkle GRSSP Workshop 2010 7 The most common failure
mode I've seen is that the closing fingers bump the object so that
the fingers don't touch the intended contact points. Then the
fingers knock the object completely out of the grasp. I think the
causes are localization errors from the perception system and
asking the robot to carry out an inherently dynamic task that was
planned with static analysis tools. Jeff Trinkle GRSSP Workshop
2010
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- Contact position error Theory vs. Practical Cause of error
Sensor Control Computation Uncertainty 8
[http://www.cs.columbia.edu/~cmatei/]
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- Accuracy of fingertip placement Planning Using camera 9
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- SensorPriceAccuracyData type Tactile sensorExpensiveHighForce
array Laser range finderExpensiveHighRange CameraVaryModerateImage
Tactile sensor [Bekiroglu el at., 11] Laser range finderCamera
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- Vision-based grasping Stereo camera Eye-in-hand camera
Manipulation under uncertainty Independent contact region Visual
servoing Reactive grasping Probabilistic model 11
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- Stereo vision based grasping [Popovic et al.,11; Gratal el at.,
12] 12
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- Eye-in-hand camera [Walck el at., 10; Lippiello el at., 11;
Calli el at., 11] 13
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- 14 (Video)
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- Independent contact region [Nilwatchararang et al., 08; Roa et
al.,09] 15
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- Visual servoing [Gratal el at., 12; Calli el at., 11] 16
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- Reactive Grasping [Teichmann et al.,94; Hsiao et al.,09; Hsiao
et al.,10] 17
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- Probabilistic model [Laaksonen et al.,11; Dogar et al.,11;
Platt et al.,11] 18
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- Propose online planning method for accurate fingertip placement
under uncertainty using eye-in-hand camera 19
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- ACCURACY!!! Insufficient information Bearing-only data Unknown
object model and properties Dont have any initial information
Close-up view with featureless image Kinematic constraint
Unreachable position Object out of view Uncertainty Unpredictable
noise 20
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- ModelingGrasp planningLocalizationGrasping 22
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- Grasping Localization Modeling Grasp planning 23
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- Robot build up a map and localize itself simultaneously while
traversing in an unknown environment [Paul Newman, 06] 24
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- Robot locationHand(Fingertips) location Environment mapObject
model 25
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http://www.biorobotics.org/projects/tslam/experiments/slam1experiment.html
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- Probabilistic SLAM [Smith and Cheeseman, 86] The probability
distribution of robot state and landmark locations The observation
model The motion model 27
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- SLAM recursive algorithm Time-update Measurement Update 28
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- Feature detection Point features, Line features Feature
association How features associate with landmarks Feature
measurements Observation model 29
[http://www.sciencedirect.com/science/article/pii/S0377042711002834]
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- How to represent a map (object model) from available features
30 [http://www.deskeng.com/articles/aaayex.htm]
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- Exploration How to explore for object modeling Strategy
Close-up strategy Out of view strategy 31
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- Fingertips placement evaluation Using ground truth data Contact
position marking Modeling evaluation Using ground truth data from
structural environment Database Kinect 32
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- Develop online planning method for accurate fingertip placement
using eye-in-hand camera Not develop algorithm to find grasping
points No clutter in work space Simple & Textured object
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- Study the works in the related fields Develop algorithms Test
the system Evaluate a result Prepare and engage in a thesis defense
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