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Proceedings - OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
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SinceJNJ,M≡0, the transformation leads to the redundantcoordinates formulation M˜(q)q¨+ C˜(q, q˙)q˙+Q˜(q, q˙)= A˜ T (q)c (17) with NJ,M= ( P˜ A˜ ) , A˜∈Rm,n, P˜∈Rn−m,n (18) M˜ :=NTJ,MMNJ,M, C˜ :=N T J,M(CNJ,M+MN˙J,M), Q˜ :=N T J,MQ. (19) Unlikebefore, this formulationconsistsofnequations,whereδonesare independent. 4. Model-BasedControlwithanAugmentedPD-Controller Model-based control is very important for parallelmechanismswith actuation redundancy, because of the antagonistic forces. As the name, redundant actuation, implies, there aremore driving forces thandegreesof freedomm>δloc to control themechanism. However,with this feature it is possible to increase the internal preload and thus, e.g. to annihilate backlashdue tomanufacturingormanipulate theEEstiffness [9], [5], [6]. Thegeneralized force of an augmentedPDController consists of three parts. Thefirst part is a feed forward termcalculatedwith the inversedynamics,which releases the feedbackcontroller. Thus, the joint angle error ismuch smaller. The second one is a feedback term,withweighted error position andvelocity of the joint angles. Andfinally the third onehas nodynamic effect i.e. it increases the internal forces. For further information, see [2], [4], [8], [7]. 4.1. InverseDynamics The inversedynamics solution is thebasis for anaugmentedPDoracomputed torquecontroller. 4.1.1. MinimalCoordinatesFormulation Thesolutionof the inversedynamics isgivenbyminimizationof (c−c0)TW(c−c0)as c= ( AT (q) )+ W ( M(q) q¨i+C(q, q˙) q˙i+Q(q, q˙) )︸ ︷︷ ︸ 1 +NAT,W(q)c 0︸ ︷︷ ︸ 3 (20) with theweightingmatrixW and an arbitrary preload parameter vector c0. Furthermore, (AT)+W= W−1A(ATW−1A)−1 is the rightpseudoinverseandNAT ,W= Im−(AT) + WA T is anull spaceprojector ofmatrixAT. 4.1.2. RedundantCoordinatesFormulation Thenumberofequationsof the redundant coordinates formulation ishigher, than thenumberof free parametersc∈Rm (n<m). Furthermore, there areδ independent equations, i.e. onlyδ columnsof A˜ T are linear independent. ThereforeEq. 17mustbe rewrittenas y˜ = A˜ T c= A˜ T 1c1+ A˜ T 2c2, c1∈Rδ, c2∈Rm−δ, (21) c1 = ( A˜ T 1 )+( y˜− A˜T2c2 ) , ( A˜ T 1 )+ = ( A˜1A˜ T 1 )−1 A˜1, (22) 213
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Proceedings OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
Titel
Proceedings
Untertitel
OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
Autoren
Peter M. Roth
Kurt Niel
Verlag
Verlag der Technischen Universität Graz
Ort
Wels
Datum
2017
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-85125-527-0
Abmessungen
21.0 x 29.7 cm
Seiten
248
Schlagwörter
Tagungsband
Kategorien
International
Tagungsbände

Inhaltsverzeichnis

  1. Learning / Recognition 24
  2. Signal & Image Processing / Filters 43
  3. Geometry / Sensor Fusion 45
  4. Tracking / Detection 85
  5. Vision for Robotics I 95
  6. Vision for Robotics II 127
  7. Poster OAGM & ARW 167
  8. Task Planning 191
  9. Robotic Arm 207
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