1 Robotic Chapter 7

AI & ESChapter 7 Robotic 2 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)

AI & ESChapter 7 Robotic 3 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

AI & ESChapter 7 Robotic 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

AI & ESChapter 7 Robotic 5 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

AI & ESChapter 7 Robotic 6 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.

AI & ESChapter 7 Robotic 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 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.

AI & ESChapter 7 Robotic 8 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

AI & ESChapter 7 Robotic 9 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

AI & ESChapter 7 Robotic 10 The End