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Robotic
Chapter 8
Artificial Intelligence Chapter 72
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)
Artificial Intelligence Chapter 73
Artificial Intelligence Chapter 74
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
Artificial Intelligence Chapter 75
Artificial Intelligence Chapter 76
• 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
Artificial Intelligence Chapter 77
Artificial Intelligence Chapter 78
Artificial Intelligence Chapter 79
Artificial Intelligence Chapter 710
Artificial Intelligence Chapter 711
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
Artificial Intelligence Chapter 712
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.
Artificial Intelligence Chapter 713
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.
Artificial Intelligence Chapter 714
Artificial Intelligence Chapter 715
Artificial Intelligence Chapter 716
Artificial Intelligence Chapter 717
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
Artificial Intelligence Chapter 718
Artificial Intelligence Chapter 719
Artificial Intelligence Chapter 720
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
Artificial Intelligence Chapter 721
Artificial Intelligence Chapter 722
Artificial Intelligence Chapter 723
The End
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