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 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

5. 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.

6. 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)

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

15. 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

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

17. Thank You
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