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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
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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 • 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) Chapter 7
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 Chapter 7
Vision • 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 Chapter 7
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 Chapter 7
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. Chapter 7
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. Chapter 7
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 Chapter 7
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 13.2-13.3 p. 336-337 3) detecting a solution 4) detecting dead ends 5) repairing an almost correct solution Chapter 7
The End Chapter 7
