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2004'lEEE International Conference on Systems, Man and Cybernetics
Application of Swarm Intelligence to the Mine Detection Problem* Eric Chapman Department of Electrical Engineering, Rochester Institute of Technology New York, USA ejcl5
[email protected] Absiract
- This paper will highlight the importantpoints
of swarm intelligence, more specifically the Ant Algorithm, and illustrate how it can be applied to a real time mine detection problem. The algorithm wilI be applied using GroundScouts, a modular micro robot that was designed for cooperative robotics applications. An Adaptive Time Division Multiple Access ( A T D M ) communication scheme will also be presented that changes the behavior of the network according to the number of robots on the network. This maximizes the efficiency of die communication between tlie robots. The results will show that the real time implementation oj'tlie algoriilim is successful.
Keywords: Swarm intelligence, ant colonies, mine detection, and adaptive TDMA.
1 Introduction The behavior of swarms in nature has captivated researchers for many years [4,6,7,8,11]. A swarm is defined as a group of insects or other organisms that act collectively to achieve a common goal. The captivating feature of swarms is that each individual in the swarm is carrying out a specific set of tasks that allows the swaim to collectively accomplish a goal that could not otherwise be done by the individuals alone. Ant colonies are one of the best examples ofthis concept. Ant colonies illustrate collective behavior when searching for food. Scout ants are sent out to explon: the terrain. When food is found, the ants will recruit other ants for assistance so that collectively they can carry the food. Mimicking this behavior proves to be a very u:;eful optimization technique when applied to optimicdion problems that involve searching a large space, such ar the Traveling Salesman Problem [4] or searching for land mines[6,7,8].
Ferat Sahin Department of Electrical Engineering, Rochester Institute of Technology New York, USA
[email protected] the mines in the least amount of time. The Ant Colony based Algorithm [4] is a perfect technique to solve this problem since it utilizes the cooperative behavior of ants to solve the problem in a prompt manner. This paper will highlight the important points of swarm intelligence, more specifically the Ant Algorithm, and illustrate how it can be applied to a real time mine detection problem. Section II will describe the features of ant colonies and why they are useful. Section III will describe the mine detection problem. Section TV will outline the implementation of the algorithm including the tools used to mimic the ant behaviors as well as the flow of the software. Section V will outline the results of the implementation including the difficulties faced during the implementation stage. Section VI will conclude the paper.
2 Ant Colonies Ant colonies are an attractive topic for researchers of swarm intelligence since they are based on labor division and cooperation of the ant society [6]. This provides a solid backbone for behavioral studies involving the cooperation of multiple agents to achieve a common goal. This forms the basis for a multi-robot algorithm known as the Ant Algorithm. The Ant Algorithm was proposed by Dorigo in 1991 [4]. His paper outlined the behavior of ants that could be applied to a multi-robot system. Later studies [6,7,8] pinpointed the specific behavior of each individual ant and divided it into three different states. The states are outlined below in Figure 1. '
(-9lb Followin
The mine detection problem can be designed to be an optimized search problem. The search space is conprised of randomly dispersed mines. The goal is to disarm 2.11 of
Figure 1. Diagram of the different states that the ant is in
* 0-7803-8566-7/04/S20.00 Q 2004 lEFE
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Theforuging state is described as the state where the ant is searching for food or keeping predators away. In the case of keeping predators away, the ants are inspecting their territory to prevent predators from entering it. In either case the most important goal of foraging is that the ants cover the greatest amount of area in a minimal amount of time and effort. The ants divide the area up between them in order to search the space more efficiently. The ants will then need to get help when food is found. This is known as recruitment. In nature, ants recruit other ants using two different techniques, long range recruitment (LRR) and short range recruitment (SRR) [SI. In SRR, the ant releases a poisonous secretion that will attract other ants near by to assist. The ant is in the waiting state during SRR. If SRR does not work, then the ants employ LRR where the ant returns to the nest in an attempt to gather more ants to assist. It then walks back to the location releasing the poisonous secretion creating a trail for other ants to follow. TheLRR technique is not applied in this paper. The state of scenffohwing is described by an ant following the secretion given offby another ant.
3 Mine Detection Problem The mine detection problem is an optimization problem that is composed of mltiple mines randomly distributed throughout an area. The objective is to collectively disarm all of the mines in a minimal amount of time. The Ant Algorithmbas a great potential to solve this problem snccessfully. The ants are released into the minefield where they begin foraging. As the ants find mines, they begin releasing the secretions for other ants to follow. Experiments done in [6,7,8] showed excellent results in disarming the mines which proved that the algorithm is applicable to the practical mine detection problem. It is the goal of this paper to present a real implementation and see what difficulties are faced in doing so.
since the ants are now going to search for the next mine to disarm. Scent Found: This event occurs when an ant is foraging and picks up the scent left by another ant. The ant will then switch into the trail following state until the mine is found. If the ant follows the scent to its source and the mine is not there, the ant will then assume that the mine was already disarmed and switch back into the foraging state. The behavior of the ants in each state has been outlined along with the events that can occur that would cause the ant to make transitions from state to state. The next section will outline exactly how the overall algorithm was implemented using micro robots, known as the GroundScouts.
4 Implementation The implementation involved the development of software to control the GroundScouts along with the development of different techniques for mimicking the behavior of the ants in a colony. This included creating infrared beacons to act, as mines, developing a communication system to allow the ants to talk to one another, and the use of the communication system to act as a scent. The overall implementation is outlined below.
4.1
GroundScouts
GroundScouts are cooperative autonomous robots designed to he both versatile and easy to use. The overall design was centered on modularity, creating a robot that could be easily altered to fit the needs of almost any application. A picture of this robot is shown helow in Figure 2. The most recent generation of GroundScouts is divided into the following layers.
Three key events were observed that caused a transition from one state to another. The events and resulting transitions are outlined below. Mine Found: Regardless of what state the ant is currently in, if a mine is found the aut will automatically go to it and he in the waiting state. This is important since the overall goal of the algorithm is to disarm the mines.
.
Mine Disarmed: This event occurs when enough ants come to assist in the disarming of the mine. It causes a transition from waiting to foraging
Figure 2. Front view ofthe Ground Scouts Power Layer: The power layer contains the circuitry needed for both power of all the other layers, including the motor drivers for the
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modulation and demodulation of the signal. It was decided that TDMA was the most applicable since it is easy to implement and a “collision h e ’ ’ protocol.
locomotion layer. The batteries are stored oil the locomotion layer. Control Layer: The control layer is composed. of a Phillips 80C552 as the main controller for the entire robot. It is connected to all of the other layers via a hardware bus that runs up the Iback and the sides of the robot. This creates a mechanical and electrical means of connecting different layers of the robot together.
In TDMA, users are separated in time, giving each user its own time slot to transmit. Once each user has had a chance to transmit, the frame is reset. This creates a protocol that has no contention and a no collision network since users are not competing to transmit, they each wait their turn. AAer implementing this protocol and analyzing it carefully, some in-efficiencies in this type of network were observed. For example, the number of users on the network was fixed, creating a maximum number of users and also creating unused slots if all of the users were not present. This led to the development of an adaptive protocol that allowed the number of slots to change in accordance with the number of users on the network. This is referred to as Adaptive TDMA [2,5,9,10].
Ultrasonic Layer: The ultrasonic layer has three ultrasonic drivers located on it that can be arranged in two different configurations. They are evenly distributed meaning that the sensors are 120 degrees offset from one another 01: all three in the front 60 degrees offset from one another. This creates a versatile design for the robots ability to avoid obstacles. Infrared Layer: The infrared layer can he used for short range communication from robot to robot along with trail following, meaning that the robot can be programmed to follow an IR signal. e
The protocol works by creating a time slot at the beginning of each frame where users can request a transmit slot. The master grants the user a transmit slot. All of the other users on the network hear this and increment their transmit slot by one, creating a gap for the new user to enter. This also goes the other way. If no message is sent in a time slot then the rest of the users on the network decide that the time slot is no longer in use and they close it. The only contention is in the requesting time slot. This is handled by having the users generate arandom number and wait that many frames before requesting another slot.
Communication Layer: The communication layer is composed of a PIC microcontroller and a wireless transceiver that is capable of transmitting serial data at ranges up to 200 feet. The modulation scheme that the transceiver uses is Frequency ShiA Keying meaning that all of the users are sharing the same medium. This created the need for an applied Medium Access Control (MAC) protocol that was developed and described in the next section.
Some other features in the ATDMA protocol include a non-fixed master, which allows the main network to split into sub networks when a group of robots becomes too far away to hear the master, allowing communication to still he able to take place. The sub networks can then recombine when appropriate. The protocol also has error detection allowing retransmission of erroneous packets.
GPS Layer: The GPS layer was created to allow the robots to he sent off on autonomous missions and give them a way to get hack to the master station.
4.2
4.3
Adaptive TDMA
Many different MAC protocols were studied in an attempt to find one that was suitable for the application. The protocols analyzed included Frequency Division Multiple Access (FDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), and polling. FDMA separates the users in the frequency domain. This cannot he used with the current hardware since the transmit frequency of the transceivers cannot he changed. CDMA gives each user a unique code. The message will only make sense to the user that has the $:ode that the message was modulated with. The cui~ent hardware does not have the capability to implement a CDMA network since the user fas no control over the
Mines
The mines are composed of a beacon that constantly transmits an infrared signal that is modulated at 38 KHz in all directions. A picture of the beacon is shown helow in Figure 3. The signal can he sensed by the robot within seven feet radius. The robot is constantly searching for the signal. As Don as the signal is found, the robot knows that it is close to the mine.
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and either pmcess or ignore the message depending on the distance. This technique effectively utilizes the communication board to be used for scent following. This method does have a few problems that will be discussed in the results section of the paper. 4.5
hpk"ItatiOn of the Swarm Algorithm
Ant Colony Based
The algorithm was written based on the diagram shown helow in Figure 4. The boxes represent the three different states that the robot can be in, while the diamonds represent the transitions that occur.
Figure 3. Outside view (a) and inside view (h) of the mine Directionality is found by viewing the five sensors that surround the robot. Early attempts were made to find the sensor with the best signal, and assume that the mine is in that direction. This is proved to be difficult since the sensnrs are somewhat omni directional, creating a number of sensors having a good signal, making it difficult to really pin point the exact direction of the mine. It was concluded that finding the direction could be simplified by looking for the two sensors that have the worst signal. The robot could then move in the opposite direction, which would be directly toward the mine. The mine is disarmed using the GroundScouts communication module. A communication board was placed on the top of the mine as shown in Figure 3(a). When enough robots are surrounding the mine to disann it, a message is sent by the command center to the communication board telling it to disarm the mine. The PIC on the communication board will then toggle a pin that will turn the mine off. The robots will then shift hack into the foraging state since signal from the mine will not he available after the mine is disarmed. 4.4
Scent
The purpose of the scent is to provide the robot with a means of recruiting other robots to help assist in the disarming of the mine. Since the IR sensors were being used for mine detection, it was decided that the communication system would he used to create a trail for the robot to follow.
I
/
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Figure 4. Flow Chart of the Implemented Algorithm The algorithm requires that multiple robots be present around a mine for the mine to he disarmed [Z].This creates the need for two different messages to he sent from robot to robot. One message indicates to other robots that the mine was found, to mimic a scent. Another message tells the other robots that the robot timed nut. These two messages allow the other robots to know exactly how many robots are surrounding the mine. AU of the messages incorporate the distance property used in Section 4.4. This makes it so the robots at other mines do not hear the messages and get confused. One thing to note is that when a robot times out, it turns around completely and travels fifteen feet before it begins to forage again. This gets the robot far enougb away so that it does not instantly go back to the mine it was just at. Also the timeout count is reset when another robot arrives at the mine.
The algorithm was programmed such that each robot keeps track of its current X and Y coordinates (neglecting slippage). All of the robots start at the middle position of an imaginary box, pointing in the same direction. This creates a way to have the scent only travel a certain distance and also a way for the robot to follow the scent. The robot will calculate how far he is away from the other
For the experiment, five robots are used to disarm two mines. A mine must have four robots surrounding it in 5432
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order to be disarmed. The robots will start in between the mines at the same location. The mines are placed far enough apart such that the communication radius of a robot at mine 1 and a robot at mine 2 does not overlap. The hashed lines in Figure 5 represent the communication radii.
5
are picking up an object that is within 6 inches. Figure 7 shows a picture of a robot at the mine.
Results
The experiment was performed in a gymnasium so that the robots had plenty of room to work with. A picture of the starting point of the experiment is shown below in Figure 5.
. .
' P
Figure 7. Robot at the mine
Figure 5. Startingpoint ofthe experimental setup
The robots were turned on one at a time and allowed to move about 3 feet before the next robot was turned on. At the start of the algorithm, the robots are foraging. This is shown below on Figure 6.
As soon as the robot reaches the mine, it will begin sending out the recruitment signal to other robots. A few problems were encountered with this. Since the impleniantation of the internal coordinate system neglects slippage, over time the robots internal coordinates will begin to become off center. As a robot at a mine sends out the recruitment signal, other robots that are within the physical distance may not hear this signal since according to the coordinate system; they are outside of listening range. Another problem is that sometimes a robot would hear the signal, but would be going to the wrong location, since it is where the robot thinks the mine is. As soon as four robots are surrounding the mine, the mine can be turned off. The robots decide that a mine is turned off by checking their front infrared sensor. If no signal is detected, then the robots conclude that the mine has been disarmed, they then instantly switch into the foraging stage, which incorporates obstacle avoidance. This is shown in Figure 8.
Figure 6. Robots in the foraging state Figure 6 clearly shows the robots randomly searching for mines. The robots near the top of the figure are beginning to find the first mine. The algorithm used to find the mines using the infrared sensors will bring the robots towards the mine. The robot will then move until the back infrared sensors have no signal and the ultrasonic sensors
Figure 8. Robots having disarmed the mine and leaving. Some problems were encountered doing this. First of all, the robots have difficulty seeing each other using the
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ultrasonic sensors since their physical structure has a limited amount of surface area to bounce off of. No solution has been found for this occurrence yet. They are now hack in the foraging state where they are searching for more mines. The robots never fully made it to disarm the second mine. The reason for this is that the area the robots could forage in was much greater than the area that the mines were in, causing the robots to move very far away from the mines, unable to retum hack.
[2] A. G. Burr, T. C. Tozer and S.J. Baines, “Capacity of an Adaptive TDMA Cellular System: Comparison with Conventional Access Schemes,” 5“ IEEE Conference on Personal, Indoor, and Mobile Radio Communications, Vol. 1, Sept. 1994, pp. 242 - 246. [3] A. M. Chou, “Slot Allocation Strategies for TDMA Protocols in Multihop Packet Radio Networks,” Eleventh Annual Joint Conference of the IEEE Computer and Communications Society, Vol. 2, May 1992, pp. 710 - 716.
The implementation of the algorithm proved b be successful but there were some problems that were faced during this implementation. They are outlined as follows.
[4] A. Colomi, M. Dorigo, V. Maniezzo, “Distributed optimization by ant colonies,” Proceedings of fhe First European conference on Artificial Life, 1991: pp. 134 142.
The internal coordinate system became very inaccurate as time progressed. This caused scent following problems.
[SI A. Kanzaki, T. Uemukai, T. Hara, S. Nishio, “Dynamic TDMA Slot Assignment in Ad Hoc Networks,” International Conference on Advanced Informofion Networking and Applications, Mar. 2003, pp. 330 - 335.
Robots that were right next to each other could not hear one another because the transmit power was full, causing the received signal to be saturated so it was impossible for a robot to know how many other robots were around the mine. This also caused a lot of problems with the ATDMA protocol. The network became unstable and it was concluded that a standard TDMA protocol would be more efficient for this application. This was implemented and proved to be much better for the application.
[6] V. Kumar, F. Sahin, “A Swarm Intelligence based approach to the Mine Detection Problem,” IEEE International Conference on Systems, Man and Cybernetics. Vol. 3, Oct. 2002. [7] V. Kumar, F. Sahin, “Foraging in Ant Colonies Applied to the Mine Detection Problem,” IEEE International Workshop on Soft Computing in Industrial Applications, Binghampton, New York, June 23-25,2003, pp. 61 - 66.
The algorithm that brought the robots next to the mine using the infrared sensors worked but not as efficiently as desired. The robots would sometimes go directly next to the mine, miss it and have to tum around and try again.
[SI
V. Kumar, F. Sahin, “Cognitive Maps in Swarm Robots for the Mine Detection Problem,” IEEE International Conference on Systems, Man and Cybernetics, Vol. 4, pp. 3364-3369, Oct. 2003.
6 Conclusions
191 G. Papadimitriou, A. Pomportsis, “Self-Adaptive TDMA Protocols for WDM Star Networks: A LeamingAutomoto-Based Approach,” IEEE Phofonics Technology Letters, Vol. 1 1 , Oct. 1999, pp. 1322 - 1324.
This paper presented a real time implementation of an ant colony based s w a m algorithm to the mine detection problem. In addition, an adaptive communication network that maximizes the efficiency of the network has also been implemented. It was shown that the algorithm can be effectively implemented with very &w problems. Future efforts would show that the algorithm can be flawlessly implemented with the use of a GPS module on all of the robots.
[IO] D. Stevens, M. Ammar, “Evaluation of Slot Allocation Strategies for TDMA Protocols in Packet Radio Networks,” IEEE Military Communications Conference, Vol. 2, Sept. 1990, pp. 835 - 839. [ I l l D. Yingting, H. Yan, J. Jingping, “Multi-Robot Cooperation Method Based on the Ant Algorithm,” Proceedings of the 2003 IEEE, 2426, April 2003, pp. 14 18.
References [l] F. Ali, P. Appani, J. Hammond, V. Mehta, “Distributed and Adaptive TDMA Algorithms for Multiple-Hop Mobile Networks,” IEEE Military Communications Conference, Vol. 1, Oct. 2002, pp. 546 551.
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