A System based on Swarm Intelligence and Ant Foraging Techniques

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A System based on Swarm Intelligence and Ant
Foraging Techniques

• By Kristin Eicher-Elmore

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What is Swarm Intelligence?

• Swarm Intelligence is a system in which more than
  one unsophisticated agents work together to
  create a solution to difficult tasks.

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Some definitions relevant to Swarm Intelligence

• Collective behavior The process of a group of
  agents working together to achieve a common goal.
• Reactive behavior The reaction of an agent to an
  outside stimulus such as a light.
• Emergent Phenomena The process where new
  behaviors develop dynamically during the process
  of solving a task.

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Why is using Swarm Intelligence Techniques
Important for Robotics Systems?

• Cost Effectiveness of
• Hardware and
• Software

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Cost Effectiveness of Hardware

• Simple agents have inexpensive hardware that can
  be easily replaced if an agent is damaged or lost
  in a hazardous environment.
• Inexpensive hardware leads to the ability to
  create large groups of agents that will be able
  to cover a large area.

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Cost Effectiveness of Software

• Using simple agents means that the Software must
  be kept relatively simple and uncomplicated.
  These systems generally will not have the memory
  space for complex algorithms. Thus, the reaction
  times will generally be quicker for fast reaction
  times.

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Purpose of the System

• To create a model for a system that will use
  features of the ant foraging techniques to find
  the shortest path to a goal for Search and Rescue
  applications.
• Military uses
• Fire and disaster rescue
• Police uses
• Any situation where there is danger and the
  need to get to a victim quickly.

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Ant Foraging Techniques

• Ant foraging techniques were chosen because of
  the ants ability to find the shortest path to a
  goal.

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Ant Foraging Technique Definitions

• Stigmergy Indirect communication used for
  communication between different insects such as
  ants. It is opposed to direct cues such as
  visual or auditory ones.
• Pheromones The chemical scent used by ants to
  communicate with one another in an indirect way.
• Mass recruitment The process by which ants are
  directed towards a food source through the use of
  pheromone trails.

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How does Mass Recruitment work to find the
shortest path?

• The first ant to find the food source and return
  to the nest leaves a pheromone trail for the
  other ants to follow.
• Another ant follows this trail since it has the
  freshest and strongest scent and leaves a scent
  trail reinforcing the path.
• The path is now established and it will be the
  shortest one because of the fact that the first
  one to return took the least time finding the
  food.

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Problems with adhering strictly to ant foraging
techniques

• Ants will meander around until they find a
  food source. When they return this path is
  usually the shortest but wandering will not work
  with a robot without ensuring that it has a good
  efficient search algorithm.

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The algorithm How this system ensures a good
solution

• The use of colored zones.
• Constant changing of search methods
• Constant search for food source through each
  search iteration
• Adequate obstacle avoidance
• Quick and Responsive RF Communication

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The use of colored zones

• Once the robot reaches this marker the search
  method is changed to a forward search and this
  ensures that the robot will keep moving on and to
  keep the boe-bot from doubling back if it is
  making a left or right wall hug search.
• This feature serves to force a progression
  towards the goal.

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Making progress with colored zones
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Constantly changing search methods

• Changing search methods from a forward to a right
  wall hug, and a left wall hug search make sure
  that the robot will not keep trying the same
  route over and over and wander aimlessly.
• These search methods are stored in memory to be
  communicated to the follower ants as a map.

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Robot changing search methods
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Constant search for the food

• The food is searched for prior to every step
  forward the robot makes. This ensures that the
  robot will not miss it.
• When the robot senses the food it will enter a
  separate search loop that does not involve the
  switching of search methods performed when in
  travel mode. This further ensures that the food
  will not be passed by.

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Food Search
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Quick Obstacle Avoidance

• If the robot becomes stuck in a corner it will
  make a sweep of the surrounding area to find the
  farthest path from the wall closest to the robot
  that is clear for both sensors.
• The robot also moves quickly through obstacles.

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Robot becoming Unstuck in a Corner
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Robot moving through Obstacle Course
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Quick and Responsive RF Communication

• Fast wireless communication means the follower
  robots can make a quick trip to the food goal.

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The Scout Robot Communicating to Follower Robots
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The System Algorithm Attempts to Find the
Shortest Path by

• Using Zones to mark progress so that scouts make
  quicker progress by not becoming stuck in one
  area.
• Using more than one search method so that the
  robot does not end up hugging one wall or
  traveling forward and going along every obstacle
  until the goal is reached.
• Sensing for the food at a constant rate so it
  isnt passed
• Obstacle Avoidance techniques that make sure the
  robots do not become stuck in a corner for too
  long.

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Emergent Behavior Nature vs. Boe-Bot
Similarities Differences
Sensing around obstacles No pheromone decay
A follower ant will scout its own way to a food source if it becomes lost Pheromone information is used as a guide rather than a strict trail
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Platform
All code is written in pBasic for a Board of
Education BS2pe chip using the Parallax Basic
Stamp Editor
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Hardware

• Parallax 433 Mhz Transceiver
• Ultrasonic Ping Sensors
• Photo-Resistors

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The Code contains two Controller Subsets

• Scout Search Loop
• Follower Search Loop
• Each robot contains the same code, but a flag
  indicates whether the robot starts out as a scout
  or a follower

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Observations and Results

• Obstacle Avoidance
• Getting out of corners
• Finding the Light
• XOR Error Checking and RF communication
• Maze size and Progression

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Obstacle Avoidance

• The code is successful at keeping the robot away
  from both walls and moving forward for forward
  search, and hugging the right and left walls for
  forward search. The robot is always successful
  at this.
• If the robot somehow gets very close to a wall on
  one side, the ultrasonic becomes blinded. During
  debugging it was found to record a large distance
  when it is in fact right up close to it. All of
  the sensors do this. So sometimes they get stuck
  running straight into a wall at a slight angle.

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Getting out of Corners

• Involves doing a sweep of the area and chooses
  the first direction that is away from
  obstructions on both sides of the robot in a
  direction away from the obstruction.
• On average only two tries are required to get out
  of a corner. At most three.
• The robot always chose the right direction.

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Finding the Light

• Very successful since sensors are checked a every
  step
• Once the robot senses it a separate sweep and
  search is made until the light source has been
  approached.
• Each robot has always found the light if close
  enough and situations were rare of a robot going
  by it when close unless another robot was
  blocking the light.
• Average distance when light found was five-seven
  inches away.

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XOR Error Checking and RF Communication

• XOR checksums are calculated at both ends and
  compared before a message is accepted as correct.
• The scout will send out a message three times
  with two seconds in between to ensure the correct
  message is received.
• However during debugging and testing
  communication never failed after the first
  attempt.

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Maze size and Progression

• If the maze walls are too far apart then when the
  robots go over a colored zone or marker, they
  dont realize they are making progress. They
  might double back and think it was new ground
  they were seeing when in fact it was the same
  marker it has already seen.
• There did not seem to be any way to solve this in
  code. The only solution seems to be keeping the
  walls from being too far apart.

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Live Demonstration
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Recorded Demo
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Challenges and Changes

• Communication and Error Checking
• Hardware Changes
• Mapping Technique Changes

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Communication and Error Checking

• At first there was more communication going on.
  Each robot, scout and follower transmitted and
  received. This was changed because of an eventual
  lack of memory space.
• Because both scouts and followers transmitted and
  received the XOR error checking scheme was more
  exact and involved the receiver sending error
  messages to the transmitter asking for another
  transmission. Again this was simplified due to
  little memory space.

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Hardware Challenges and Changes

• All code was simplified because major hardware
  changes were needed.
• The main challenge was a lack of memory space due
  to the needs of the transceiver.
• An extra chip a BS2 and a bread board were added.
• The extra chip made it necessary to consider
  building a battery pack that would hold five
  batteries since more voltage was needed. A power
  supply temporarily solved this problem.

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Hardware Challenges and Changes cont.

• One chip the BS2pe ran logic and movement, the
  BS2 ran the transceiver.
• The biggest problem that could not be resolved
  chip to chip communication. The BS2pe would not
  stop its program execution to notice the
  interrupt from the BS2 with the transceiver.
• The BS2pe ran at 6000 instructions per second and
  the BS2 ran at 4000 instructions per second. The
  BS2 ran at a speed too slow to interrupt the
  program execution of the BS2pe, so the code was
  simplified.

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Mapping Techniques

• At first actual directions were used instead of
  search techniques. This used too much memory
  space and because each robot moves differently
  due to differences in servo motors, search
  technique mapping was more efficient.

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Future Improvements

• Obstacle Avoidance and Colored Zones
• Finding the Goal (victim)
• Greater number of Agents and Scouts

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Obstacle Avoidance and Zones

• Obstacle Avoidance code could remain the same yet
  with more durable robots with better traction and
  the ability to deal with potholes, etc..
• Instead of contrasting markers used to keep track
  of progress, gps devices could be used that would
  keep track of where the robot is in relation to
  its starting point and the robot could actually
  see forward progression from the starting point.

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Finding the Victim

• Instead of using light sensors, a thermal
  infrared camera could be used to identify
  victims.

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Greater Number of Agents and Scouts

• A very large number of Scouts would be used to
  create better coverage of an area.
• Once the victim was found by the quickest agent,
  RF communication with more sophisticated error
  checking could be used to bring followers
  equipped with special equipment bringing
  temporary relief like oxygen and water until
  rescuers could reach the injured.

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Conclusion

• A successful swarm has these components
• Collective behavior
• Emergent behavior
• Reactive Behavior

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Collective Behavior in this System

• Each agent shares the goal of finding the food.
• When one Scout finds this food, a guide is sent
  to the rest of the ants so that they can find the
  food as well.
• All ants are cooperating together to find the
  food.

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Emergent Behavior

• Dynamic behaviors emerge during each run of the
  program.
• A follower might find an optimal solution better
  than the guide it received from the Scout because
  it does not follow the directions blindly but as
  a hint of the right moves to make to the goal
  sensing for the light as it goes.

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Emergent Behavior cont.

• A separate search for the light source with the
  proper obstacle avoidance and sweep methods for
  the light if it has been sensed can create
  differing behaviors in each ant even if they took
  the same route enabling that no mistakes of
  missing the light can be made.
• Changing search methods over time create possible
  changes in behavior that keep an ant from being
  stuck in a rut following one method.

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Reactive Behavior

• Apparent intelligence in insects comes from
  there reactions to their environment. Robots do
  can be made to react in similar ways with very
  simple sensors and hardware. Thus, swarm
  intelligence is an ideal way to create large and
  simple systems that can solve difficult problems
  with ease.