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AuRA: Principles and Practice in Review

AuRA: Principles and Practice in Review. Paper by: Ronald C. Arkin and Tucker Balch Present By: Jirakhom Ruttanavakul. Introduction. AuRA : Au tonomous R obot A rchitecture AuRA : was developed in mid-1980’s (as a hybrid approach to robotic navigation) Arose from

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AuRA: Principles and Practice in Review

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  1. AuRA: Principles and Practice in Review Paper by: Ronald C. Arkin and Tucker Balch Present By: Jirakhom Ruttanavakul

  2. Introduction • AuRA : Autonomous Robot Architecture • AuRA : was developed in mid-1980’s (as a hybrid approach to robotic navigation) • Arose from • A deliberative or hierarchical planner • A reactive controller

  3. Guideline • The structure of AuRA • The strengths of AuRA • The origin of AuRA Theory • The example of AuRA-Based System • Conclusion

  4. Structure of AuRA Learning User Input REPRESENTATION Plan Recognition User Profile User Intentions Mission Planner Hierarchical Component Spatial Reasoner Spatial Learning Spatial Goals Plan Sequencer Mission Alterations Opportunism Schema Controller Reactive Compnenet On-line Adaptation Teleautonomy Motor Perceptual Actuation Sensing

  5. Mission Planner Structure of AuRA (Cont.) Spatial Reasoner Plan Sequencer • Mission Planner: Establishing high-level goals and constraints within which it must operate. • Spatial Reasoner: Using the knowledge in long-term memory to construct a sequence of path legs. • Plan Sequencer: Translating each path legs into a set of motor behaviors (schemas) • The schemas will be sent to the robot. • The deliberative system will stop and reactive system will start.

  6. Schema Controller Structure of AuRA (Cont.) Motor Perceptual • Schema Manage: Controlling and Monitoring the behavioral processes at run-time. • Motor Schema associated with Perceptual schema: Providing the stimulus required for that particular behavior. • Homeostatic Control System: Maintaining balance and system equilibrium. • Hierarchical Component will be reactivated, only if a failure is detected (lack of progress, velocity of zero, and timeout)

  7. The Strengths of AuRA • Modularity: Components can be replaced with others in straightforward manner • Flexibility: It provides for introducing adaptation and learning methods. • Generalizability: • Hybridization: Gat’s Atlantis Architecture, 3T

  8. The Origin of AuRA Theory • Aura : influenced by a wide range of ethological, neuroscientific, and psychological study • Schema Theory: a theory of intelligence which represents motor and perceptual control at a level of abstraction higher than that of neural networks. The AuRA employs at the reactive control level, encoded using an analog of the potential fields method • Justification for hybridization of reactive and deliberative control: found in studies by psychologists • Homeostatic Control System: developed using models of the mammalian endocrine system as inspiration.

  9. The Example of AuRA-Based System • Trash-Collecting Robots : Built by a Group of Georgia Tech Student in 1994 • Objective : Searching for trash, Picking it up, & Carrying it to the wastebaskets • The trash : consisting of Styrofoam coffee cups, wads of paper, and soda cans. • The environment : consisting of obstacles such as tables and chairs.

  10. Robots Hardware and Sensing • Power System & Computer Equipment • Sensors : including bumper switches for collision detection • Color Video Camera : The key factor of the robots’ success in their task • A custom-built gripper : attatched to the front of the robots • Infrared Sensor : mounted in the gripper

  11. Low-level Behaviors for the Robots • The lowest level : motor schemas • Schema Controller instantiates and runs schemas as directed by the Plan Sequencer. • A set of schemas is active at a time • Each motor schemas : Computing a vector which indicates a desired direction of motion. • The vector is combine to generate the overall movement vector • The overall movement : sent to the robot’s actuator

  12. The Example of Schemas • Detect-red-blob : using vision to find the location of the goal (red is trash, blue for wastebaskets, green for robots). • Detect-obstacles : using bumper switches to detects and tracks obstacles • Move-to-goal : generating a vector towards the goal found by detect-red-blob • Avoid-static-obstacles : generating a vector away from any detected obstacles • Detect-IR-beam-broken: used as the trigger to close the gripper around the object

  13. A Plan for Robots • The plan, coded by humans, is a sequence of behavioral assemblages and perceptual triggers which causes the transition between them, expressed as a Finite State Acceptor (FSA) • States : identified with circles • Perceptual Triggers : directed arcs between states • When the condition on arcs is met, the state will be changed

  14. Cooperation in Robots • No communication devices with the robots • Simply paint the robots to green color. • The robot move away from the green in wander-for-trash state

  15. Conclusions • The AuRA is a hybrid architecture which combine deliberative planner, based on traditional AI techniques and reactive controller, based on schema theory.

  16. Thank You Questions & Comments

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