Robotic Chapter 8

Artificial IntelligenceChapter 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 IntelligenceChapter 73

Artificial IntelligenceChapter 74 Vision 2D  3D signal processing : enhance the image measurement 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 IntelligenceChapter 75

Artificial IntelligenceChapter 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 IntelligenceChapter 77

Artificial IntelligenceChapter 78

Artificial IntelligenceChapter 79

Artificial IntelligenceChapter 710

Artificial IntelligenceChapter 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) words vocabulary HMM (Hidden Markov Modeling) SPHINX system –statistical learning method –HMM is a collection of states and transitions

Artificial IntelligenceChapter 712 Speech Recognition HMM (Hidden Markov Modeling) SPHINX system statistical learning method HMM is a collection of states and transitions the problem of decoding a speech waveform turns into the problem of finding the most likely path (set of transitions) through an appropriate KMM.

Artificial IntelligenceChapter 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 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 IntelligenceChapter 714

Artificial IntelligenceChapter 715

Artificial IntelligenceChapter 716

Artificial IntelligenceChapter 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 p Figure (a) naive strategy for grasping and placement Figure (b) clever strategy for grasping and placement planning p. 332 e.g. Block world ON(A,B) HOLDING, ARMEMPTY

Artificial IntelligenceChapter 718

Artificial IntelligenceChapter 719

Artificial IntelligenceChapter 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 apply 2) applying rules see Figure p ) detecting a solution 4) detecting dead ends 5) repairing an almost correct solution

Artificial IntelligenceChapter 721

Artificial IntelligenceChapter 722

Artificial IntelligenceChapter 723 The End