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

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

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

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

Structure of AuRA (Cont.) 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.

Structure of AuRA (Cont.) 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)

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

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.

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.

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

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

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

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

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

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

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