1. Emily Shaeffer and Shena Cao
Comparing Effectiveness of Bioinspired Approaches to Search and Rescue Scenarios
Hi I’m Shena and this is Emily and we are doing our final project on the use of Bioinspired swarming behavior to navigate through search and rescue scenarios
Emily Shaeffer and Shena Cao
Shaeffer and Cao- ESE 313
2/28/2011

2. C3.4 The Idea
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
We will first give you an idea of what we are planning to do, talk about the need for a good search and rescue algorithm, and talk about a few specific algorithms we plan to work off of. To start, we hypothesize that a well functioning search and rescue navigation algorithm can be found combining components of ant colony, bee colony, and cockroach swarming algorithms. Some benefits of each of these algorithms in a search and rescue setting are finding an optimal route back to a base camp with the ant colony optimization algorithm, locate areas demanding the most or most imminent attention using a bee swarming algorithm with their dancing, and a grouping dispersion system based on the cockroach swarming behavior which could be useful if a robot gets trapped or breaks down.
Now Emily will tell you a bit more on the motivations for our hypothesis as well as more about the actual algorithms.
Shaeffer and Cao- ESE 313
2/28/2011

3. 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
Examples of swarming: ants lifting or deconstructing large objects (youtube video of ants eating a gecko), bees maximizing amount of honey they bring back to the hive, cockroaches following each other to foodDefinition of foraging: splitting up to cover large areas to find food (or another desired target)September 11: had 48 hours in which they needed to find victims but robots were unable to locate anyone withing the first 48 hours, swarm behavior could help in a divide and conquer sense, helping robots to communicate which areas had already been traversed (like cockroaches ruthless behavior)
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.
Shaeffer and Cao- ESE 313
2/28/2011

4. 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
There are many swarming algorithms available and they have been available for decades. Some of the most widely used ones is the ant colony optimization, but also bee and to a lesser extent the cockroach swarming. There are various other algorithms for the behavior of other insects and biological organisms.
Shaeffer and Cao- ESE 313
2/28/2011

5. C3.3 Desirability of Bioinspiration: 3 Different Insect Inspired Algorithms
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
Ants: N is nest, F is food1) Randomly disperse from base, find food2) Randomly retract back to base, leave pheromone trail3) Step proportionate evaporation of pheromone trail4) Probabilistic following of pheromone trail5) Positive feedback leads to optimizationBees: 1) Start with base2) Each bee finds neighboring source, respond with “wiggle dance” based on nectar amount3) Onlookers evaluate response, change sources accordingly4) Best sources found5) Positive Feedback EffectCockroaches: chase (used like buddy system, will follow each other to food), disperse (splitting up to traverse more area, better for searching large grids), ruthless (if not enough resources one will leave, good for taking advantage of dispersion when resources are low and chasing when high, like wisdom to know which is better)Sent at 1:34 PM on Tuesday 
Shaeffer and Cao- ESE 313
2/28/2011

6. C3.4 The Idea
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
Just a recap on what I said in the beginning, our idea is to combine the three algorithms, to make a better functional algorithm specific for search and rescue scenarios. We propose to use the shortest path optimization portion of the ant colony optimization algorithm based oh pheromone trails and pheromone evaporation to locate the shortest path between a danger site the robot locates and a base camp where rubble clearing robots, paramedics, people monitoring are located. We propose to use a bee colony optimization to judge the allocation of resources. If there are a limited amount of rubble clearing resources or paramedics, we want these to be directed to the areas where they are most needed, not say a small pile of stones that just happened to be there. Lastly, we wanted to use the cockroach swarming behavior to get good coverage while having the robots follow each other to a certain extent so that they maintain visual contact with other robots incase complications arise.
Shaeffer and Cao- ESE 313
2/28/2011

7. C3.6 Necessary Means Create Basic Obstacle Grid Maze
Problem Areas-Various Degrees
Base Camp
 Test refutability parameters
So how are we going to do this? The key to implementing and evaluating our resulting algorithm is to create a basic obstacle grid. This will have a maze to simulate urban environments such as builidings, streets, etc which robots have to navigate through, have areas with varing characteristics and various degrees of danger and to be able to locate and keep contact with a base camp.
To evaluate, we will test our refutability parameters
Shaeffer and Cao- ESE 313
2/28/2011

8. C3.5 Refutability Speed of Response (minimize detection time)
Order of Response (high danger zones first)
Comparative behaviors with three original algorithms
Consider Alternative Algorithms
So the areas that we are testing would be the speed of response, key is first 48hrs after a disaster. We also need to worry about order of response so that we can target the most important areas first and try to rescue the most amount of people, and finally, to evalute the effectiveness of our algorithm, we will compare it to the individual algorithms, the ant, bee and cockroach as well as additional swarming algorithms such as fish, bacteria, particle etc and see how it fares in comparison with the goal being to out perform the others.
Thank you, we would now like to open it up to questions.
Shaeffer and Cao- ESE 313
2/28/2011

9. 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
4) Probabilistic following of pheromone
trail
5) Positive feedback leads to
optimization 3
Shaeffer and Cao- ESE 313
2/28/2011

10. 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 3
Shaeffer and Cao- ESE 313
2/28/2011

11. 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    
     3
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. Obstacle Grid Details 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
     3
Shaeffer and Cao- ESE 313
2/28/2011