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Chapter 21 Robotic Perception and action

Chapter 21 Robotic Perception and action. 323-670 Artificial Intelligence ดร.วิภาดา เวทย์ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์. 1) Robotics is the intelligent connection of perception action.

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Chapter 21 Robotic Perception and action

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  1. Chapter 21RoboticPerception and action 323-670 Artificial Intelligence ดร.วิภาดา เวทย์ประสิทธิ์ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์

  2. 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) Robotic Page 2

  3. 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 Vision Page 3

  4. 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 Robotic Page 4

  5. 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 Speech Recognition Page 5

  6. HMM (Hidden Markov Modeling) SPHINX system statistical learning method HMM is a collection of states and transitions each transition learning a state is marked with 1) the probability which that transition is taken 2) an output symbol 3) the probability that the output symbol is emitted when the transition is taken. the problem of decoding a speech waveform turns into the problem of finding the most likely path (set of transitions) through an appropriate KMM. Speech Recognition Page 6

  7. 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. Action Page 7

  8. 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 Manipulation Page 8

  9. 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 13.2-13.3 p. 336-337 3) detecting a solution 4) detecting dead ends 5) repairing an almost correct solution Manipulation Page 9

  10. Chapter 22Conclusion 323-670 Artificial Intelligence ดร.วิภาดา เวทย์ประสิทธิ์ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์

  11. p. 579 1) Methods for representing and using knowledge 2) Methods for conducting heuristic search both methods relate to each other Knowledge Representation: use to solve the problem 1) Predicate Logic : use to solve a new derive inference problem 2) Semantic Networks : use for network search routines 3) Set of weight in NN : some relaxation or forward propagation search must be exploited. Components of AI program Page 11

  12. 1) “Essential Knowledge” : knowledge about defining what problem to be solved, how to solve the problem, and what is the outcome or solution of the problem solving. 2) “Heuristic Knowledge” : knowledge about the explanation of how to get the outcome or solution. Knowledge Page 12

  13. AI Knowledge Representation AI Fields Problem and search Page 13

  14. problem and search AI technique Production system Heuristic search Generate and test mean-end analysis best first search constrain satisfaction hill-climbing Page 14

  15. Knowledge Representation predicate logic semantic network rule frame statistical model * conceptual dependency resolution forward/ backward chaining uncertainty * Page 15

  16. AI Fields Robotic learning Expert system planning NLP common sense * Computer Vision NN block world pattern recognition understanding Heuristic search conceptual dependency rule Page 16

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