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Download Robotic Soccer. Outline History Motivation Comparison with real soccer Artificial Intelligence in Robotic Soccer Simulation of Robotic Soccer

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  • Slide 1
  • Robotic Soccer
  • Slide 2
  • Outline History Motivation Comparison with real soccer Artificial Intelligence in Robotic Soccer Simulation of Robotic Soccer
  • Slide 3
  • What is Soccer Robot? A soccer robot is a specialized autonomous robot and mobile robot that is used to play variants of soccer -Wikipedia
  • Slide 4
  • Robots Playing Soccer The idea of robots playing soccer was first mentioned by Professor Alan Mackworth (University of British Columbia, Canada) in a paper entitled On Seeing Robots presented at VI-92, 1992.
  • Slide 5
  • History Independently, a group of Japanese researchers organized a Workshop on Grand Challenges in Artificial Intelligence in October, 1992 in Tokyo, discussing possible grand challenge problems. This workshop led to a serious discussions of using the game of soccer for promoting science and technology.
  • Slide 6
  • Cont.. Factors Considered in the discussion: Technology feasibility. Social impact assessment. Financial feasibility.
  • Slide 7
  • Result As a result of these studies, it has been concluded that the project is feasible and desirable. In June 1993, a group of researchers, including Minoru Asada, Yasuo Kuniyoshi, and Hiroaki Kitano, decided to launch a robotic competition, tentatively named as Robot J-League Within a month, on receiving overwhelming reactions from researchers outside of Japan, renamed the project as the Robot World Cup Initiative, RoboCup for short.
  • Slide 8
  • The Year 1997 In the history of artificial intelligence and robotics, the year 1997 will be remembered as a turning point. In May 1997, IBM Deep Blue defeated the human world champion in chess. Forty years of challenge in the AI community came to a successful conclusion. On July 4, 1997, NASAs pathfinder mission made a successful landing and the first autonomous robotics system, Sojourner, was deployed on the surface of Mars.
  • Slide 9
  • Together with these accomplishments, RoboCup made its first step towards the development of robotic soccer with an aim of beating human World Cup champion team by the year 2050. RoboCup and its aim
  • Slide 10
  • Motivation Why all this focus letting robots play games of soccer? Dealing with the real world properties. The element of competition is not only an interesting research question, it also serves as a way to put the research to the test. Element of Competition.
  • Slide 11
  • Motivation Cont. Standard problems. Research on games, chess in particular, has generated significant advances in the theory of search algorithms and search control, as well as motivating cognitive studies into the ways that humans approach the same problems
  • Slide 12
  • Comparison Distributed CentralControl IncompleteCompleteInfo. accessibility Real timeTurn takingState Change DynamicStaticEnvironment SoccerChess
  • Slide 13
  • Lessons from real Soccer The problem of creating a team strategy from individual behavior Performance is probably very bad on skills like passing and shooting. Aritificial Intelligence.
  • Slide 14
  • Solutions for good team work in human soccer Game system Positioning Offensive Positioning Defensive Positioning The golie
  • Slide 15
  • Goalie
  • Slide 16
  • Others Freezing the game Positioning at special kick situations
  • Slide 17
  • Tactics Simplicity Let the ball work Only Stop when one cannot advance immediately Be receptible for a pass Make passes playable for the receiving robot.
  • Slide 18
  • Tactics Cont.. Dont be to obvious in your intentions. Camouflage your weaknesses. Move towards a passed ball. Shield the ball of the opponent. Do not slide unless absolutely necessary. Be a good team player.
  • Slide 19
  • AI in Robotic Soccer Three divisions Machine Vision Camera Snapshots + Image Processing etc. Artificial Intelligence Logic of players movements. Communication Signal Transmission etc.
  • Slide 20
  • AI in Robotic Soccer Some Basic Definitions Roles Modularity Layered Operations Decision Matrix
  • Slide 21
  • A decision matrix is a list of values in rows and columns that allows an analyst to systematically identify, analyze, and rate the performance of relationships between sets of values and information.
  • Slide 22
  • AI in Robotic Soccer Artificial Intelligence Layers Strategy layer Path planning layer Fuzzy reactive layer Motor control layer
  • Slide 23
  • Strategy Layer Strategy layer Operations Task identification Role prioritization Role assignment Robot destination assignment
  • Slide 24
  • Role Assignment Using weighted importance of roles Role allocation Heuristic
  • Slide 25
  • Task Identification & Role prioritization Inputs Ball speed towards own goal Ball distance from own goal Large, Multi-dimensional decision matrix Outputs Decision Matrix Heuristic weights of importance of the goalie, attacker role, defender role.
  • Slide 26
  • Robot destination assignment Based on role stereotype
  • Slide 27
  • Robot destination assignment Based on role stereotype Goalie Movement between the goal and the position of the ball but within the goal area. Ball moving toward the goal action Zonal division of goal area
  • Slide 28
  • Robot destination assignment
  • Slide 29
  • Based on role stereotype Defender Common Role Interchangeable role Similarity with Goalies target destination
  • Slide 30
  • Robot destination assignment
  • Slide 31
  • Based on role stereotype Attacker Aggressive Behavior Evasive Behavior Similarity with Goalies target destination
  • Slide 32
  • Merits & Demerits Operations satisfactory in simulation Inflexibility of fixed arrays to future additions Lack of sensible interpolation policy for unknowns Constructing, storing and accessing in decision matrix Inflexibility of this approach
  • Slide 33
  • FUZZY Algorithms Fuzzification Fuzzy Input Sets Fuzzy Output Sets Fuzzy Rules & Association memory matrix Defuzzification
  • Slide 34
  • Fuzzification Fuzzy set pair (U,m) where U is a set and sets whose elements have degrees of membership. Classical set and Fuzzy set Input variables are mapped into fuzzy sets Process of converting a crisp input value to a fuzzy value
  • Slide 35
  • Fuzzy Input Sets
  • Slide 36
  • Slide 37
  • Fuzzify ball speed and ball position. Example: Fuzzy Input for Fuzzy ball speed = - 0.25 Fuzzified Ball Speed = { 0.5, 0.5, 0 }
  • Slide 38
  • Fuzzy Output Sets Zero, Small Positive, Medium Positive, Large Positive, Very Large Positive
  • Slide 39
  • Fuzzy Output Sets one Fuzzy Output Value representing each of the three robot roles Output = { Attacker Fuzzy Output Set, Defender Fuzzy Output Set, Goalie Fuzzy Output Set, }
  • Slide 40
  • Fuzzy Rules & Association memory matrix Fuzzy Rules are very natural and can be expressed in natural Language. Example: If ball speed is negative and ball position is away then attacker priority is very large and defender priority is zero and goalie priority is small. If ball speed is zero and ball position is away then attacker priority is large and defender priority is small and goalie priority is medium.
  • Slide 41
  • Fuzzy Rules & Association memory matrix Fuzzy Association Memory Matrix (FAMM) is a complete mapping of all the fuzzy rules in our controller. Example
  • Slide 42
  • Defuzzification Necessity of defuzzifying our Fuzzy Output Values. Aggregation procedure v i is the centre value for the rule that corresponds to weight w i.
  • Slide 43
  • Path Planning Layer Inputs are outputs from Strategy Layer. A* implementation Fuzzy logic and A* navigation challenge Remedy: Create a supplementary algorithm to generate a map of robot soccer environment. Extensive refinement to ensure compatibility of A* with Fuzzy logic system.
  • Slide 44
  • Path Planning Layer Red bot is expected to move along the dotted path. Supplementary Algorithm generates the Map. A* Algorithm generates the points.
  • Slide 45
  • Fuzzy Reactive layer Inputs are outputs from Path Planning Layer Calculates angular velocity and forward speed for the bot
  • Slide 46
  • Motor control layer Inputs are outputs from Fuzzy reactive Layer Calculates the left motor velocity and right motor velocity Informs the communication module to transmit the required data to the correct bot.
  • Slide 47
  • Soccer Simulation Evaluation of various multi-agent systems and cooperative algorithms. Components :- Soccer Server Client
  • Slide 48
  • Soccer Server o Provides Virtual soccer field o Simulates the movements of players and the ball. o client-server communication
  • Slide 49
  • Modules Simulator module Message-board module Referee module
  • Slide 50

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