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Comparing Effectiveness of Bioinspired Approaches to Search and Rescue Scenarios. Emily Shaeffer and Shena Cao. Shaeffer and Cao- ESE 313. 4/28/2011. Combine: The Ant Colony Optimization (ACO) convergence mechanism Bees Colony task division-forager, scout, packers

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comparing effectiveness of bioinspired approaches to search and rescue scenarios

Comparing Effectiveness of Bioinspired Approaches to Search and Rescue Scenarios

Emily Shaeffer and Shena Cao

Shaeffer and Cao- ESE 313

4/28/2011

c3 4 hypothesis

Combine:

    • The Ant Colony Optimization (ACO) convergence mechanism
    • Bees Colony task division-forager, scout, packers
    • Cockroach Swarm Optimization automatic swarming
  • =
    • Efficient navigation in 2D discrete environment between home base and target "danger" locations, faster than these algorithms alone

C3.4 Hypothesis

Shaeffer and Cao- ESE 313

4/28/2011

c3 1 desired behavior or capability swarming for improved search and rescue

What is Swarming?

      • Large groups to accomplish large tasks
      • Algorithms for ants, bees, cockroaches
  • Use of Swarming for Search and Rescue
      • “Foraging Task”- Can be performed by robots independently, multiple improve performance
      • Sept 11- robots found nothing, swarming robots could have covered more ground
      • Focus on searching and mapping, not rubble removal or extraction
  • Why Swarming
      • Collective intelligence for non-intelligent robots

C3.1 Desired Behavior or Capability: Swarming for Improved Search and Rescue

Shaeffer and Cao- ESE 313

4/28/2011

c3 2 present unavailability where robots are lacking

Current Technology

      • Separate algorithms modeling the behavior of each type of insect
      • Using just the cooperative collaboration model of ants, improved navigating
      • Ability to change between tasks increases efficiency
  • Missing Technology
      • A combination of all three techniques for most efficient possible navigation in different scenarios

C3.2 Present Unavailability: Where Robots are Lacking

Shaeffer and Cao- ESE 313

4/28/2011

c3 3 desirability of bioinspiration 3 different insect inspired algorithms

Ant colony optimization algorithm

      • Ants go any direction, pheromone trail strength indicates shortest path
        • Used Pure ACO
  • Artificial bee colony
      • Higher efficiency by task division using foragers, scouts, and packers
        • BeeSensor Routing
  • Cockroach Swarming
      • Chase-swarming behavior, dispersing behavior, ruthless behavior

C3.3 Desirability of Bioinspiration: 3 Different Insect Inspired Algorithms

Shaeffer and Cao- ESE 313

4/28/2011

c3 4 hypothesis1

Combine:

    • The Ant Colony Optimization (ACO) convergence mechanism
    • Bees Colony task division-forager, scout, packers
    • Cockroach Swarm Optimization automatic swarming
  • =
    • Efficient navigation in 2D discrete environment between home base and target "danger" locations, faster than these algorithms alone

C3.4 Hypothesis

Shaeffer and Cao- ESE 313

4/28/2011

c3 6 necessary means

Create Basic Obstacle Grid

    • GridWorld
      • 2D environment
      • Bounded
      • Discrete
      • Provided: 
        • Actor class-random movements which interact with other actors
        • Flower objects that decay over time (humans or pheromone trail)
        • Station rocks that can interact (change colors-might mark what has been found)
  •  Test refutability parameters

C3.6 Necessary Means

Shaeffer and Cao- ESE 313

4/28/2011

c3 5 refutability

Detection time-found all danger zones on map

  • % Humans saved in time
  • Behavior judged relative to 3 algorithms alone

C3.5 Refutability

Shaeffer and Cao- ESE 313

4/28/2011

results grid implementation

Created grid implementations in which all actors could interact with each other

  • Each test scenario contained at least one victim, obstacles, and different combinations of other actors
  • Have scenarios for only ants, only bees, and only cockroaches

Results: Grid Implementation

Shaeffer and Cao- ESE 313

4/28/2011

detailed implementation

Cockroach Swarm Optimization

    • Set visibility range (90 degree angle in forward direction)
    • Find local best (calculate individuals proximity to object and find closest)
    • Move randomly towards local best
    • Local best reaches target, marks it and moves to next target
    • If clustered, individuals interact and increases probability of dispersion (from 0.1 to 0.5)
  • Values yet to be optimized
  • Have yet to implement other algorithms
    • Vision: using the pure ACO concept on the path of bee colony algorithm

Detailed Implementation

Shaeffer and Cao- ESE 313

4/28/2011

predicted results

Cockroach Swarm Optimization

    • Performs well for dispersing and moving between target sites
    • Speed?
  • ACO
    • Good speed
    • Search?
  • BeeSensor
    • Good combining factor
  • Therefore we still believe that our final implementation will surpass these algorithms individually

Predicted Results

Shaeffer and Cao- ESE 313

4/28/2011

next steps

Understanding

    • More thorough understanding of weaknesses in literature
      • Understanding of implications of weaknesses in literature
      • Further defining what optimization is and what the literature considered optimization
    • More mathematical analysis to better predict what our results would be even if the code is not working

Next Steps

Shaeffer and Cao- ESE 313

4/28/2011

conclusions

Need more time to work though code so we can test our different scenarios

Conclusions

Shaeffer and Cao- ESE 313

4/28/2011

questions

Questions?

Shaeffer and Cao- ESE 313

2/28/2011

supplementary slides

Supplementary Slides

Shaeffer and Cao- ESE 313

2/28/2011

ant colony optimization details

1) Randomly disperse from base, find food

2) Randomly retract back to base, leave

pheromone trail

3) Step proportionate evaporation of

pheromone trail

4) Probabilistic following of pheromone

trail

5) Positive feedback leads to

optimization

Ant Colony Optimization Details

Shaeffer and Cao- ESE 313

2/28/2011

artificial bee colony details

1) Start with base

2) Each bee finds neighboring source, respond

    with “wiggle dance” based on nectar amount

3) Onlookers evaluate response, change

sources accordingly

4) Best sources found

5) Positive Feedback Effect

Artificial Bee Colony Details

Shaeffer and Cao- ESE 313

2/28/2011

cockroach swarming details

1) Chase-Swarming behavior

    Each individual X(i) will chase individual P(i) within its visual scope 

    or global individual Pg

2) Dispersing behavior

    At intervals of certain time, each individual may disperse randomly

            X ′(i) = X (i) + rand(1, D),i = 1,2,..., N      

3) Ruthless behavior

    Current best replaces an individual selected at random

            X (k)=Pg    

Cockroach Swarming Details

Reference: Chen ZH, Tang HY (2010) 2nd International Conference on Computer Engineering and Technology. 6, 652-5

Shaeffer and Cao- ESE 313

2/28/2011