Chapter 21 Robotic Perception and action Chapter 21 Robotic Perception and action Artificial Intelligence ดร. วิภาดา เวทย์ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์ คณะ วิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์

Artificial Intelligence Lecture 45Page 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)

Artificial Intelligence Lecture 45Page 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

Artificial Intelligence Lecture 45Page 4 Robotic  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 Lecture 45Page 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

Artificial Intelligence Lecture 45Page 6 Speech Recognition  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.

Artificial Intelligence Lecture 45Page 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.

Artificial Intelligence Lecture 45Page 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

Artificial Intelligence Lecture 45Page 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

Chapter 22 Conclusion Chapter 22 Conclusion Artificial Intelligence ดร. วิภาดา เวทย์ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์ คณะ วิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์

Artificial Intelligence Lecture 45Page 11 Components of AI program  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.

Artificial Intelligence Lecture 45Page 12 Knowledge 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.

Artificial Intelligence Lecture 45Page 13 AI Knowledge Representatio n Problem and search AI Fields

Artificial Intelligence Lecture 45Page 14 problem and search Production system AI technique Heuristic search Generate and test best first search constrain satisfaction mean-end analysis hill- climbing

Artificial Intelligence Lecture 45Page 15 Knowledge Representation predicate logic frame resolution forward/ backward chaining rule uncertainty * semantic network conceptual dependenc y statistical model *

Artificial Intelligence Lecture 45Page 16 AI Fields planning understanding block world Expert system NLP NN learning conceptual dependency rule Heuristic search Computer Vision Robotic pattern recognition common sense *