SHAGE: A Framework for SHAGE: A Framework for Self- -managed Robot - - PDF document

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SHAGE: A Framework for SHAGE: A Framework for Self- -managed Robot - - PDF document

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SHAGE: A Framework for SHAGE: A Framework for Self- -managed Robot managed Robot Self Software Software Sooyong Park <sypark@sogang.ac.kr> Software Systems Engineering Lab. Sogang University May. 21~22, 2006 ICSE 2006 Workshop on

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May 21~22, 2006 SEAMS, Shanghai, China

SHAGE: A Framework for SHAGE: A Framework for Self Self-

  • managed Robot

managed Robot Software Software

Sooyong Park

<sypark@sogang.ac.kr>

Software Systems Engineering Lab. Sogang University

  • May. 21~22, 2006

ICSE 2006 Workshop on Software Engineering for Adaptive and Self-Managing Systems(SEAMS), Shanghai, China

2 May 21~22, 2006 SEAMS, Shanghai, China

Outline Outline

  • Introduction
  • An Approach
  • SHAGE Framework
  • Demonstration
  • Research Issues
  • Conclusions
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3 May 21~22, 2006 SEAMS, Shanghai, China

Home Service Robots for the Elderly Home Service Robots for the Elderly

  • Home service robots(HSR) provide useful services

such as

– Face recognition – Navigation – Bringing an object – Conversation – …

  • CIR(the Center for Intelligent Robotics) at KIST(the

Korea Institute of Science and Technology) has been developed various home service robots.

– T-Rot(High-end), Infotainment robot, H-Robot(Healthcare robot)

4 May 21~22, 2006 SEAMS, Shanghai, China

Overall S/W Architecture Overall S/W Architecture1)

1) of HSR

  • f HSR

Deliberative Layer Reactive Layer Task Manager Action Components Action Components Action Components Action Components

Behavior Control Component Behavior Control Component Behavior Control Component

…

Functional Unit Component Functional Unit Component Functional Unit Component Functional Unit Component Functional Unit Component Functional Unit Component

Sequencing Layer

SHAGE Framework

1) E. Gat, “On three-layer architectures,” in Artificial Intelligence and Mobile Robots (D. Kortenkamp, R. P. Bonnasso, and R. Murphy, eds.), MIT/AAAI, 1997.

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5 May 21~22, 2006 SEAMS, Shanghai, China

Research Motivation Research Motivation

Malfunction new Environment

Self-Healing, Adaptive, and Growing Technologies for Intelligent Robots Self-Healing, Adaptive, and Growing Technologies for Intelligent Robots

User’s new needs Limited Resources 6 May 21~22, 2006 SEAMS, Shanghai, China

Example Example – – (1) (1)

  • Need for safety: an old man says “move carefully!”

– During a party: a lot of unrecorded moving objects

PathPlanner MapBuilder Localizer Montion Controller AutoMove LaserProxy VisionProxy SonarProxy SkinProxy

Quality requirement:

Don’t care execution time, but avoid all possible collisions

Quality requirement:

Don’t care execution time, but avoid all possible collisions

Reconfigured Architecture:

ready to use various sensors and to make a map by using the sensors.

Reconfigured Architecture:

ready to use various sensors and to make a map by using the sensors.

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7 May 21~22, 2006 SEAMS, Shanghai, China

Example Example – – (2) (2)

  • Need for agility: an old man says “move quickly!”

– The old man is home alone: no unrecorded object

PathPlanner MapBuilder Localizer Montion Controller AutoMove LaserProxy

Quality requirement: Short response time, Short execution time, optimize use of batteries. Quality requirement: Short response time, Short execution time, optimize use of batteries.

Reconfigured Architecture:

ready to use one sensor(laser). It cannot avoid (unrecorded) moving objects

Reconfigured Architecture:

ready to use one sensor(laser). It cannot avoid (unrecorded) moving objects 8 May 21~22, 2006 SEAMS, Shanghai, China

Our Approach Our Approach

Monitoring Monitoring Decision Decision Making Making Reconfigu Reconfigu-

  • ration

ration Brokering Brokering Learning Learning

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9 May 21~22, 2006 SEAMS, Shanghai, China

SHAGE Framework SHAGE Framework

  • SHAGE(Self-Healing, Adaptive, and Growing SoftwarE)

Framework integrates following technologies

– Monitoring – Brokering: Ontology(authoring relations between environmental information and architectural information) – Decision & Learning: Case-Based Decision Theory – Reconfiguration: Slot-based architectural style

  • This framework provides a test bed for self-managed

software.

10 May 21~22, 2006 SEAMS, Shanghai, China

SHAGE Overall Architecture SHAGE Overall Architecture

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11 May 21~22, 2006 SEAMS, Shanghai, China

Architecture/Component Broker Architecture/Component Broker

<i1, s1> …

A set of Inclination-Situation Pairs

<i1, s2> <in, sm>

Current Situation (given by the Task Manager)

…

A set of architecture configurations (Abstract Level Architectures)

Candidate Set (reduced by the Architecture Broker) 12 May 21~22, 2006 SEAMS, Shanghai, China

Architecture/Component Broker Architecture/Component Broker

  • Role

– Searching abstract-level architecture configurations related to the current situation.

  • Technology

– Ontological descriptions.

  • Current Status

– It can only search in a small set of configurations related to the navigation subsystem. – Rule-based search: it cannot relax rules.

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13 May 21~22, 2006 SEAMS, Shanghai, China

Decision Maker & Learner Decision Maker & Learner

…

A set of architecture configurations (Abstract Level Architectures)

Candidate Set (reduced by the Architecture Broker) Selected Concrete Component (Selected by the Decision Maker) Selected Architecture Configuration (Selected by the Decision Maker) 14 May 21~22, 2006 SEAMS, Shanghai, China

Decision Maker & Learner Decision Maker & Learner

  • Role

– Selecting exactly one configuration and one component for each slot from the candidate set retrieved by the architecture broker.

  • Technology

– Case-Based Decision Theory

  • Current Status

– It only carries out in limited scope. – Limited search space: only in the navigation subsystem. – Limited learning time: few scenarios.

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15 May 21~22, 2006 SEAMS, Shanghai, China

Reconfigurator Reconfigurator

Concrete Level Abstract Level

Slot: MotionControl Slot: Coordinator Slot: Localizer Slot: PathPlanner Slot: MapBuilder Slot: MotionControl Slot: Coordinator Slot: SLAM Slot: PathPlanner Slot: MotionControl Slot: Coordinator Slot: Localizer Slot: PathPlanner Slot: MapBuilder

A B C D E

Slot: MotionControl Slot: Coordinator Slot: SLAM Slot: PathPlanner

A.1 B F G

C1 C1 C1 C1 C1 C2 C2

After Reconfiguration After Reconfiguration Before Reconfiguration Before Reconfiguration 16 May 21~22, 2006 SEAMS, Shanghai, China

Reconfigurator Reconfigurator

  • Role

– Reconfiguring the current software architecture dynamically.

  • Technology

– Slot-based two-level software architectural style.

  • Current Status

– It has reconfigured only the navigation subsystem.

  • All configurations for the subsystem were verified in the demonstration.

– It can manage components distributed in SBCs(Single Board Computers) by RMI. – It supports components implemented in Java and C++(through JNI).

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17 May 21~22, 2006 SEAMS, Shanghai, China

Demonstration Demonstration

18 May 21~22, 2006 SEAMS, Shanghai, China

Research Issues Research Issues

  • Internal monitoring
  • Ontology construction
  • Learning speed
  • Run-time measurement and validation
  • Componentization
  • Domain Knowledge
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19 May 21~22, 2006 SEAMS, Shanghai, China

Conclusions Conclusions

  • SHAGE Framework has been developed to provide ‘self-managing

capabilities’ to robot software.

  • The framework integrated ontology, decision theory, and dynamic

architecture and comprises

– Monitor – Architecture/Component Broker – Decision Maker & Learner – Reconfigurator

  • In the experiment, we showed a simple scenario.

– The robot managed its architecture to adapt its changing requirements.

  • The second phase(three years) of our research just launched to

improve key technologies.