Plan Rehan O'Grady

Meeting 15th June 2006

'Swarm Control' - Theme for phd thesis

Thesis

Swarm Control

• How does a human being issue commands to a swarm
• How does a robot or a robot swarm issue comamnds to another swarm (Swarmanoid)

Possible Research Areas

• Basic control of swarm. Human (Eye bots in swarmanoid?) can see object to be retrieved. Need to direct the swarm to the object, then to the target location to which the object needs to be transported.
• Command is broadcast to swarm - swarm needs to decide how to interpret. Command type: '20 robots go over there and grab that object, 30 go and see what is over that hill. Rest of you stay where you are.

Tie in with work to date

• Functional self-assembly is a form of swarm decision making. This ties into HSRI (human swarm robot interaction) because an essential part of designing control systems for robots is determining to what level the swarm should behave autonomously
• Work to date provides examples of behaviours that can be instigated by the control mechanisms designed over the course of the Phd. e.g. Command is : 'Get into groups of size three and head over that hill'

Shorter Term

• Finish two sbot hill hill rotation experiment and write it up for a paper.
• Pattern generation experiment with Anders. Write up as a paper for a low key conference. I can use this simple controller as a starting point for investigating swarm control. Anders can use this controller as a starting point to investigate swarm fault detection.

Meeting 2nd December 2005

Long Term - Phd Thesis

Theory

Explore aspects of functional self assembly.

1. Decision Process
   • Is self-assembly necessary?
2. Structure
   • What is appropriate size for connected groups?
   • What is appropriate shape for connected groups?
3. Behaviour
   • What to do when connected (cooperative movement, transport, navigation, etc)?
4. Timing
   • When should swarm assemble?
   • When should swarm disassemble?

Practice

1. Parametrised group size selection mechanism (2,3,4,..)
   • Initially in a non-adaptive way
   • Maybe in an adaptive way
2. Parametrised Shape distinction mechanism
   • Initially binary parameter - linear / blob (non-linear)
   • Initially non-adaptive
   • Maybe adaptive
   • Maybe further parameterisable - control over what 'blob' looks like
3. Application of above two mechanisms to hill passing problem
   • Comparison of adaptive linear approach and blob approach

Short Term

Timescale

Conference paper writtern by end March 2006

Goal

Conduct new experiment which builds on previous functional self-assembly experiment. Publish new results in conference (probably Ants2006). Combine new conference paper with ecal2005 paper and publish results in journal.

Experiment

Environment

• Similar to previous ecal2005 functional self assembly experiment.
• Except use three different hills to prove adaptive rotation mechanism. | / \

Task

• Two robots must navigate over hill towards target.
• When they encounter hill, they use adaptive rotation (adaptive to orientation of hill) to avoid toppling.

Methods

• Reuse existing functional self assembly controller. (Published work)
• Develop adaptive (to hill orientation) control. (New work)

Trials

• 2 robots: 20 trials on each hill type. = 60 total.
• 3 robots (maybe): some trials needed to show blob behaviour doesn't change with different hill orientations.

Misc Ideas

• Spend 1 or 2 weeks helping Lausanne disassemble / repair robots
  • I get to learn more about robotic hardware
  • Lausanne gets work done faster with extra pair of hands.
  • Iridia gets skill transfer on hardware disassembly / repair.