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COLLECTIVE ROBOTICS FOR RESOURCE DISTRIBUTION

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MICC Ph.D. DAY / DECEMBER 15, 2005 / Steven de Jong. 1. COLLECTIVE ROBOTICS FOR ... Our research track (1) Central agent. Grid world. Potential field. Reactive robots ... – PowerPoint PPT presentation

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Title: COLLECTIVE ROBOTICS FOR RESOURCE DISTRIBUTION


1
COLLECTIVE ROBOTICS FORRESOURCE DISTRIBUTION
  • Steven de Jong
  • steven.dejong_at_cs.unimaas.nl
  • MICC Ph.D. Day
  • 15 december 2005

2
Storage facilities
3
Resource distribution
  • EASY?
  • Bring objects from A to B
  • COMPLEX? Weyns
  • Objects can have priorities
  • Object offerings are often irregular
  • Environment can change
  • Facilities are very large
  • REQUIREMENTS
  • Adaptive, functional, robust scalable

4
Classical approaches
  • Classical approaches
  • Multi-Agent Planning De Weerdt
  • Contract Net FIPA
  • Problems Dorigo, Wolpert, Stone,
  • Have central control
  • and/or
  • Rely heavily on inter-agent messaging
  • Complexity explosion
  • Number of robots
  • Number of tasks

?
5
Collective systems in biology Dorigo
6
How do they do it? How do we?
  • Ant hills ? mechanisms ? storage facilities
  • Key mechanisms
  • Communication through environment
  • Pheromones, et cetera
  • Collective/externalized memory (configuration)

Please dont use electric cattle when microwave
is turned on.
7
Our research track (1)
  • Central agent
  • Grid world
  • Potential field
  • Reactive robots

8
Our research track (2)
  • Knowledge and memory distributed from central
    agent
  • To individual robots
  • To the environment (communication)
  • Key question
  • How can a group of co-operative agents obtain
    co-ordination in a robust way, in an
    ever-changing environment?
  • Approach
  • Collective intelligence, RL, sensor networks,

9
Summary
  • Problem
  • Systems with many robots either emergent or
    predefined
  • Wish to have adaptive and reliable large
    systems that perform difficult tasks predictably
  • Solution
  • Methodology required function of the group ?
    requirements for individuals and environment
  • Straightforward (e.g., reactive) specification
    for individuals

10
References (selection)
  • Danny Weyns et al Gradient field-based task
    assignment in an AGV transportation system
  • FIPA www.fipa.org
  • Marco Dorigo / IRIDIA iridia.ulb.ac.be/mdorigo/
    ACO/ACO.html
  • David Wolpert et al General Principles Of
    Learning-Based Multi-Agent Systems
  • Peter Stone et alMulti Robot Learning for
    Continuous Area Sweeping
  • Mathijs de Weerdt Multi-agent Planning An
    introduction to planning and coordination.
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