robotic
DESCRIPTION
Chapter 8. Robotic. Robotic. 1) Robotics is the intelligent connection of perception action. 2) A robotic is anything that is surprisingly (moving target) animate. 3) perceptual (S/W) + motor task (H/W) [action] operate in the real world : searching and backtracking can be costly - PowerPoint PPT PresentationTRANSCRIPT
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Robotic
Chapter 8
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Robotic 1) Robotics is the intelligent connection of perception
action.2) A robotic is anything that is surprisingly (moving
target) animate.3) perceptual (S/W) + motor task (H/W) [action]• operate in the real world : searching and
backtracking can be costly• we need operating in a simulate world with full
information for an optimal plan by best-first search• we can checked preconditions of the operators
using perception to perform action• real time search : p. 562 A* algorithm, RTA* (Korf
1988)
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Vision
2D 3Dsignal processing : enhance the imagemeasurement analysis : for image containing a
single object determining the 2D extent of the object depicted
pattern recognition : for single object images, classify the object into category
image understanding : for image containing many objects in the image, classify them, build 3D model of the scene. see Figure 14.8 p. 367
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• Problem :• ambiguous image : see Figure 21.2 p. 564• Figure 21.3 p. 565 using low level knowledge to
interpret an image• image factor, sensor fusion : color, reflectance,
shading• Figure 21.4 p. 565 using high level knowledge to
interpret an image (a) use surroundings objects to help (b) baseball, log in a fireplace, amoeba, [egg, bacon, and plate]
• Figure 21.5 p. 567 Image understanding• analog signal Image 2D features
3D features• 3D composite objects
Object identification
Vision
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Speech Recognition• speaker dependence (we can train the system) /
speaker independence• continuous / isolated word speech• real time SPHINX (1988) / offline processing • large (difficult) / small vocabulary • broad (difficult) / narrow grammar: TANGORA
(1985) 20000 words vocabulary• HMM (Hidden Markov Modeling) SPHINX system
– statistical learning method– HMM is a collection of states and transitions
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Speech RecognitionHMM (Hidden Markov Modeling) SPHINX
system statistical learning method
HMM is a collection of states and transitionsthe problem of decoding a speech waveform turns
into the problem of finding the most likely path (set of transitions) through an
appropriate KMM.
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Action• p. 569 : navigation around the world
• planning routes / path planning• reaching desired destinations without bumping
into things• see Figure 21.6–21.9 p. 570-571
• constructing a visibility graph
• configuration space / C-space (Lozano-Perez 1984)
– basic idea is to reduce the robot to a point P and do path planning in an artificially constructed space
– rotation (X,Y,)
• obstacles can be transformed into 3D C-space objects, visibility graph can be created and
searched.
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Manipulation
• end-effectors (two-gripper) / a human like hand
• pick-and-place : grasp and object and move it to a specific location see Figure
21.10-21.11 p. 572-573• Figure 21.11-21.12 (a) naive strategy for grasping
and placement• Figure 21.11-21.12 (b) clever strategy for
grasping and placement• planning p. 332 e.g. Block world ON(A,B)
HOLDING , ARMEMPTY
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Manipulation• planning p. 332 e.g. Block world ON(A,B)
HOLDING , ARMEMPTY• Components of a planning system
1) choose the best rule to apply2) applying rules see Figure 13.2-13.3 p. 336-3373) detecting a solution4) detecting dead ends5) repairing an almost correct solution
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The End