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Robot Intelligence

Robot Intelligence. Kevin Warwick. Task Example. We will look at a relatively simple example and see how a robot might solve a specific problem Try to spot the assumptions we/humans make on the robot’s capabilities

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Robot Intelligence

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  1. Robot Intelligence Kevin Warwick

  2. Task Example • We will look at a relatively simple example and see how a robot might solve a specific problem • Try to spot the assumptions we/humans make on the robot’s capabilities • In the problem the robot will start at one point and must find it’s way to the goal

  3. Topological Representation

  4. Topological Representation • The space can be partitioned using for example: • Binary State Partitioning. • But does the robot have or can it find out such a map?

  5. A connectivity graph of this BSP is:

  6. Both together…

  7. Valid Routes • In order to get from the start position (region 2) to the goal (region 14) the robot must follow one of the routes from 2 to 14 in the graph • 2  3  4  5  6  10  13  12  14 • 2  3  4  9  10  13  12  14 • 2  3  4  8  7  11  12  14 • 2  1  7  11  12  14 • 2  1  7  8  4  5  6  10  13  12  14 • 2  1  7  8  4 9  10  13  12  14

  8. Real-time Solutions in Unmodelled Environments • If the robot did not know the structure of the environment and could not build a planned trajectory, the goal may still be reached • Consider the goal having a beacon to indicate to the robot where the goal is

  9. Real-time Solutions in Unmodelled Environments • Now consider a wall following routine • The robot moves forward following the left hand wall until • it reaches an obstacle (a wall in front) • It turns right – possibly repeatedly • it no longer detects the presence of a wall to the left • It turns left • Can the robot reach the goal?

  10. Problem Revisited

  11. Real-time Solutions in Unmodelled Environments • In this case the robot will be able to reach the goal if the robot initially moves to the right • If the robot moves to the left then it will circle the top-left island and would require some additional reasoning to break out of that cycle • e.g. Odometry would indicate that the pose of the robot has repeated a number of times

  12. Robot Architectures • Example: seven dwarf • Pseudocode … • if left_sensor_reading • turn right • elseif right_sensor_reading • turn left • elseif (right_sensor_reading) and (left_sensor_reading) • reverse • else • move forward

  13. Robot Architectures • Add in wall following … • if left_sensor_reading • if left_sensor_reading < min • turn right_slightly • if left_sensor_reading > max • turn left_slightly • else • move forward • elseif right_sensor_reading • if right_sensor_reading < min • turn left_slightly • if right_sensor_reading > max • turn right_slightly • else • move forward • elseif (right_sensor_reading) and (left_sensor_reading) • reverse • else • move forward • Even introducing modest extensions can lead to increased complexity in the algorithm if it is developed in an ad hoc way • Need to find some more formalised way of developing behaviours

  14. Robot Architectures • Two main classes • Centralised • Reactive • Three main types: • High Level Control (centralised) • Treat the robot as an abstract entity • Apply classical AI techniques to define complex tasks • Top down (Computer Science) approach • Functional (centralised) • Classical horizontal connection between perception and action • sense – model – plan – act paradigm

  15. Robot Architectures • Reactive (distributed) • Bottom up (Cybernetics) approach • Subsumption • most widely known • Motor Schema • Ego-behaviour • Biological Analogues • Artificial Neural Networks • Genetic Algorithms • Hybrid systems • Combinations of any/all of the above

  16. Centralised Controller (functional)

  17. Distributed Controller (reactive)

  18. Functional Architectures I: Hierarchical • Decompose the control process by function • Low level processes provide simple functions that are grouped together to provide higher level functions • e.g. • Lower level processes • position sensing • motor output • forward kinematics • inverse kinematics • dynamic model of the robot • low level vision processing (e.g. edge detection) • … • Note: while these are “low level” some of them can be computationally challenging, especially kinematics, dynamics and vision • Higher level processes • Mission planning • Map building • Reasoning about tasks • Sequencing tasks

  19. Functional Architectures II: Blackboard • Blackboard-based architectures rely on a common pool of information (the blackboard) that is shared by independent computational processes • Example: Ground Surveillance Robot (GSR) • designed to navigate from one known location to another known location over unknown natural terrain • to complete the task the GSR has to build a terrain map • GSR uses the blackboard to represent and pass information from one software module to another • there is a loose coupling between modules • Disadvantages • requires the information to be consistent in its representation • cannot use relative positions as defined in one subsystem in another • can lead to bottlenecks in processing • asynchronous nature of blackboards can lead to problems due to timing skews • difficulties arise if more than one module can change data in the pool … which set of data is the right one? • similar problem to file sharing violations

  20. Example Blackboard System

  21. Basic Reactive • Consider instead a very basic reactive robot: • Higher level goal – move towards a desired end position (infra-red?) • Lower level behaviour – move forwards • Lower level behaviour – avoid obstacles

  22. Basic Reactive • Lower level behaviours – example • Kohonen network to map robot positional state/situation (object to left/right/front – distance) • Fuzzy Automata – Physical realisation in each state – e.g. left wheel forward fast, right wheel reverse slowly • Needs reward/punishment • Probabilities randomised then updated based on success/failure of moving forward and avoiding obstacles

  23. Next Presentation • Architectures

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