1. ©2006 CSUC Institute for Research in Intelligent Systems AAROM: A System based on Swarm Intelligence and Ant Foraging Techniques April 10, 2008 Kristin Eicher-Elmore

2. ©2006 CSUC Institute for Research in Intelligent Systems 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.

3. ©2006 CSUC Institute for Research in Intelligent Systems 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.

4. ©2006 CSUC Institute for Research in Intelligent Systems Why is using Swarm Intelligence Techniques Important for Robotics Systems? Cost Effectiveness of: Hardware and Software

5. ©2006 CSUC Institute for Research in Intelligent Systems 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.

6. ©2006 CSUC Institute for Research in Intelligent Systems 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.

7. ©2006 CSUC Institute for Research in Intelligent Systems 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.

8. ©2006 CSUC Institute for Research in Intelligent Systems Ant Foraging Techniques Ant foraging techniques were chosen because of the ant’s ability to find the shortest path to a goal.

9. ©2006 CSUC Institute for Research in Intelligent Systems 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.

10. ©2006 CSUC Institute for Research in Intelligent Systems 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.

11. ©2006 CSUC Institute for Research in Intelligent Systems 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.

12. ©2006 CSUC Institute for Research in Intelligent Systems The algorithm: How this system ensures a good solution ● The use of colored zones. ● Constant changing of action methods ● Constant search for food source through each search iteration ● Adequate obstacle avoidance ● Quick and Responsive RF Communication

13. ©2006 CSUC Institute for Research in Intelligent Systems 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.

14. ©2006 CSUC Institute for Research in Intelligent Systems Making Progress with Colored Zones

15. ©2006 CSUC Institute for Research in Intelligent Systems Constantly Changing Action Methods ● Changing action 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 action methods are stored in memory to be communicated to the follower ants as route information.

16. ©2006 CSUC Institute for Research in Intelligent Systems Robot Changing Action Methods

17. ©2006 CSUC Institute for Research in Intelligent Systems Constant Search for the Target ● 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 action methods performed when in travel mode. This further ensures that the food will not be passed by.

18. ©2006 CSUC Institute for Research in Intelligent Systems Food Search

19. ©2006 CSUC Institute for Research in Intelligent Systems 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.

20. ©2006 CSUC Institute for Research in Intelligent Systems Robot becoming Unstuck in a Corner

21. ©2006 CSUC Institute for Research in Intelligent Systems Robot Moving through Obstacle Course

22. ©2006 CSUC Institute for Research in Intelligent Systems Quick and Responsive RF Communication ● Fast wireless communication means the follower robots can make a quick trip to the food goal.

23. ©2006 CSUC Institute for Research in Intelligent Systems The Scout Robot Communicating to Follower Robots

24. ©2006 CSUC Institute for Research in Intelligent Systems 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 action 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 isn’t passed ● Obstacle Avoidance techniques that make sure the robots do not become stuck in a corner for too long.

25. ©2006 CSUC Institute for Research in Intelligent Systems SimilaritiesDifferences 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 Nature vs. Boe-Bot

26. ©2006 CSUC Institute for Research in Intelligent Systems Platform All code is written in pBasic for a Board of Education BS2pe chip using the Parallax Basic Stamp Editor

27. ©2006 CSUC Institute for Research in Intelligent Systems Hardware ● Parallax 433 Mhz Transceiver ● Ultrasonic Ping Sensors ● Photo-Resistors

28. ©2006 CSUC Institute for Research in Intelligent Systems

29. The Solution 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

30. ©2006 CSUC Institute for Research in Intelligent Systems

38. Observations and Results ● Obstacle Avoidance ● Getting out of corners ● Finding the Light ● XOR Error Checking and RF communication ● Maze size and Progression

39. ©2006 CSUC Institute for Research in Intelligent Systems 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.

40. ©2006 CSUC Institute for Research in Intelligent Systems 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.

41. ©2006 CSUC Institute for Research in Intelligent Systems 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.

42. ©2006 CSUC Institute for Research in Intelligent Systems 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.

43. ©2006 CSUC Institute for Research in Intelligent Systems Maze size and Progression ● If the maze walls are too far apart then when the robots go over a colored zone or marker, they don’t 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.

44. ©2006 CSUC Institute for Research in Intelligent Systems Live Demonstration

45. ©2006 CSUC Institute for Research in Intelligent Systems Recorded Demo

46. ©2006 CSUC Institute for Research in Intelligent Systems Challenges and Changes ● Communication and Error Checking ● Hardware Changes ● Mapping Technique Changes

47. ©2006 CSUC Institute for Research in Intelligent Systems 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.

48. ©2006 CSUC Institute for Research in Intelligent Systems 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.

49. ©2006 CSUC Institute for Research in Intelligent Systems 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.

50. ©2006 CSUC Institute for Research in Intelligent Systems Routing Techniques ● At first actual directions were used instead of action methods. This used too much memory space and because each robot moves differently due to differences in servo motors, action method routing was more efficient.

51. ©2006 CSUC Institute for Research in Intelligent Systems Future Improvements ● Obstacle Avoidance and Colored Zones ● Finding the Goal (victim) ● Greater number of Agents and Scouts

52. ©2006 CSUC Institute for Research in Intelligent Systems 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.

53. ©2006 CSUC Institute for Research in Intelligent Systems Finding the Victim ● Instead of using light sensors, a thermal infrared camera could be used to identify victims.

54. ©2006 CSUC Institute for Research in Intelligent Systems 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.

55. ©2006 CSUC Institute for Research in Intelligent Systems Summary A successful swarm has these components: ● Collective behavior ● Dynamic behavior ● Reactive Behavior

56. ©2006 CSUC Institute for Research in Intelligent Systems Collective Behavior ● 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.

57. ©2006 CSUC Institute for Research in Intelligent Systems Dynamic 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.

58. ©2006 CSUC Institute for Research in Intelligent Systems Dynamic 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 action methods over time create possible changes in behavior that keep an ant from being stuck in a rut following one method.

59. ©2006 CSUC Institute for Research in Intelligent Systems Reactive Behavior Apparent success in insect behavior comes from there reactions to their environment. Robots 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.