aura: principles and practice in review paper by: ronald c. arkin and tucker balch present by:...
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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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