Comparing Effectiveness of Bioinspired Approaches to Search and Rescue Scenarios

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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. Shaeffer and Cao- ESE 313 4/28/2011

10. 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

11. 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

12. 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

13. 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

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

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

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

17. 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

18. 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

19. 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