AuRA: Principles and Practice in Review

Paper by:

Ronald C. Arkin and Tucker Balch

Present By:

Jirakhom Ruttanavakul

Introduction

AuRA : Autonomous Robot ArchitectureAuRA : 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

Plan RecognitionUser Profile

Spatial Learning

Opportunism

On-lineAdaptation

User Intentions

Spatial Goals

Mission Alterations

Teleautonomy

Mission Planner

Spatial Reasoner

Plan Sequencer

REPRESENTATION

Schema Controller

Motor Perceptual

Actuation Sensing

HierarchicalComponent

ReactiveCompnenet

Structure of AuRA (Cont.)

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.

Mission Planner

Spatial Reasoner

Plan Sequencer

Structure of AuRA (Cont.)

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)

Schema Controller

Motor Perceptual

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.

Thank You

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