Comparing Effectiveness of Bioinspired Approaches to Search and Rescue Scenarios Emily Shaeffer and Shena Cao 4/28/2011Shaeffer and Cao- ESE 313

4/28/2011Shaeffer and Cao- ESE 313 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

4/28/2011Shaeffer and Cao- ESE 313 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

4/28/2011Shaeffer and Cao- ESE 313 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

4/28/2011Shaeffer and Cao- ESE 313 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

4/28/2011Shaeffer and Cao- ESE 313 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

4/28/2011Shaeffer and Cao- ESE 313 Create Basic Obstacle Grid o 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

4/28/2011Shaeffer and Cao- ESE 313 Detection time-found all danger zones on map % Humans saved in time Behavior judged relative to 3 algorithms alone C3.5 Refutability

4/28/2011Shaeffer and Cao- ESE 313

4/28/2011Shaeffer and Cao- ESE 313 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

4/28/2011Shaeffer and Cao- ESE 313 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

4/28/2011Shaeffer and Cao- ESE 313 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

4/28/2011Shaeffer and Cao- ESE 313 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

4/28/2011Shaeffer and Cao- ESE 313 Need more time to work though code so we can test our different scenarios Conclusions

2/28/2011Shaeffer and Cao- ESE 313 Questions?

2/28/2011Shaeffer and Cao- ESE 313 Supplementary Slides

2/28/2011Shaeffer and Cao- ESE 313 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

2/28/2011Shaeffer and Cao- ESE 313 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

2/28/2011Shaeffer and Cao- ESE 313 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