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Comparing Effectiveness of Bioinspired Approaches to Search and Rescue Scenarios

This study explores the effectiveness of combining ant, bee, and cockroach swarming behaviors in a functional search and rescue algorithm. It investigates the potential use of ant colony optimization, bees algorithm, and cockroach swarming in different search and rescue scenarios. The aim is to improve navigation and efficiency in locating areas requiring immediate attention.

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Comparing Effectiveness of Bioinspired Approaches to Search and Rescue Scenarios

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  1. Comparing Effectiveness of Bioinspired Approaches to Search and Rescue Scenarios Emily Shaeffer and Shena Cao Shaeffer and Cao- ESE 313 2/28/2011

  2. Hypothesis: A functional search and rescue algorithm can be found combining ant, bee, and cockroach swarming behaviors • Ant Colony Optimization Algorithm • Possible use: Search area, find optimal route back to base camp • Bees Algorithm • Possible use: Locate areas demanding imminent attention • Cockroach Swarming  • Possible use: Dispersion and continued searching C3.4 The Idea Shaeffer and Cao- ESE 313 2/28/2011

  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 C3.1 Desired Behavior or Capability: Swarming for Improved Search and Rescue References: Cao, Y. U., Fukunaga, A. S., & Kahng, A. B. (1997). Cooperative mobile robotics: Antecedents and directions. Autonomous Robots, 4(1), 7-27. Trivedi, Bijal P. (2001). Search-and-Rescue Robots Tested at New York Disaster Site. http://news.nationalgeographic.com/news/2001/09/0914_TVdisasterrobot.html Shaeffer and Cao- ESE 313 2/28/2011

  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 2/28/2011

  5. Ant colony optimization algorithm • Ants go any direction, pheromone trail strength indicates shortest path • Artificial bee colony • Bees scout new sources, return, dance based on amount of nectar at site • Cockroach Swarming • Chase-swarming behavior, dispersing behavior, ruthless behavior C3.3 Desirability of Bioinspiration: 3 Different Insect Inspired Algorithms Shaeffer and Cao- ESE 313 2/28/2011

  6. Hypothesis: A functional search and rescue algorithm can be found combining ant, bee, and cockroach swarming behaviors • Ant Colony Optimization Algorithm • Possible use: Search area, find optimal route back to base camp • Bees Algorithm • Possible use: Locate areas demanding imminent attention • Cockroach Swarming  • Possible use: Dispersion and continued searching C3.4 The Idea Shaeffer and Cao- ESE 313 2/28/2011

  7. Create Basic Obstacle Grid • Maze • Problem Areas-Various Degrees • Base Camp •  Test refutability parameters C3.6 Necessary Means Shaeffer and Cao- ESE 313 2/28/2011

  8. Speed of Response (minimize detection time) • Order of Response (high danger zones first) • Comparative behaviors with three original algorithms • Consider Alternative Algorithms C3.5 Refutability Shaeffer and Cao- ESE 313 2/28/2011

  9. 1) Randomly disperse from base, find food • 2) Randomly retract back to base, leave • pheromone trail • 3) Step proportionate evaporation of • pheromonetrail • 4) Probabilistic following of pheromone • trail • 5) Positive feedback leads to • optimization Ant Colony Optimization Details Shaeffer and Cao- ESE 313 2/28/2011

  10. 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

  11. 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

  12. Incorporate: 1) Degree of rubble (indicates scale of damage-     assume proportionate to degree of emergency) 2) Randomly dispersed rubble 3) Maze setting (roads, buildings, etc.-urban setting) 4) Disaster clearance rate 5) Simple 2D structure Obstacle Grid Details Shaeffer and Cao- ESE 313 2/28/2011

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