STRATEGIES FOR ROUTE PLANNING ON CATASTROPHE

ENVIRONMENTS

Coordinating Agents on a Fire Fighting Scenario

Pedro Abreu

FEUP/DEI/LIACC, Rua Dr. Roberto Frias, s/n 4200-465, Porto, Portugal

Pedro Mendes

EZ4U, Rua Guerra Junqueiro, 417, Porto, Portugal

Keywords:

Multi-Agent System, Agent Collaboration, Simulated Environment.

Abstract:

The concept of multi-agent systems (MAS) appeared when computer science researchers had the need to

solve problems involving the simulation of real environments with several intervenients (agents). Solving

these requires a coordination process between agents and in some cases negotiation. Such is the case of a

catastrophe scenario with the need intervention to minimize the consequences, like for instance a ﬁre. In this

particular case the agents (ﬁremen) must have a good coordination process to achieve as fast as they can their

ﬁre ﬁghting position. The main goal of this project is to create an optimal strategy to calculate the best path

to the ﬁre ﬁghting position. Tests were conducted on an existing simulator platform Pyrosim. Three factors

have an important role: wind (intensity and direction), ground topology and vegetation variety. At the end the

results were quite satisfactory, mainly in what concerns the agents main objective. The A* algorithm proved

to be feasible for this particular problem, and the coordination process between agents was implemented

successfully. In the future this project may have its agents ported to the BDI concept.

1 INTRODUCTION

The Multi Agent System (MAS) concept appeared

in the early 80s although most of the relevant re-

search trends only began being explored in the 90s

(Wooldridge, 2002). At that time, several research

publications (magazines, journals and books) started

to attract researches all around the world and due to

that growth, the European Union created AgentLink

(Union, 2008) which is the premier Co-ordination

Action for Agent Based Computing. Most of these

systems ﬁnd their application in domains where it is

relevant to simulate a real environment with several

intervenients with well known responses to stimu-

lus and to solve complex constraint based problems

with hundreds or thousands of variables, in which

case, hopefully by making every agent ﬁght for him-

self, a solution close to the optimal one will emerge

in a short time. In this research project, the focus

is on the systems described ﬁrst. By using the Py-

rosim platform(Thunderheadeng, 2008), which simu-

lates the emergence of natural ﬁres in a forest area,

a MAS is proposed in order to coordinate a set of

agents that personify typical ﬁreﬁghters against a ﬁre

with a single focus. The Pyrosim platform supports

variable weather conditions, types of vegetation and

other aspects that will be discussed in the next sec-

tions. In a catastrophe situation, like a big dimension

ﬁre, the main goal is to conﬁne it, so to reduce the

damage as much as possible in the forest area. The

steps involved in tackling these problems are many.

First, one must detect the exact position where the

ﬁre is consuming the green area, the second is to de-

termine which is the optimal path to reach the most

effective ﬁre ﬁghting position and ﬁnally how should

the ﬁreﬁghters position themselves so to minimize the

ﬁre spreading process and actually conﬁne it. This re-

search project presents a strategy to effectively tackle

these goals. The remainder of this paper is organized

as follows. Section 2 describes the state of the art re-

garding MAS concepts and research topics. Section 3

shows some of the most relevant features of this par-

ticular approach in terms of applied algorithms and

undertaken strategies. Section 4 exposes results ob-

tained and ﬁnally in section 5 conclusions are pre-

sented and future work trends are discussed.

277

Abreu P. and Mendes P. (2009).

STRATEGIES FOR ROUTE PLANNING ON CATASTROPHE ENVIRONMENTS - Coordinating Agents on a Fire Fighting Scenario.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Artiﬁcial Intelligence and Decision Support Systems, pages

277-280

DOI: 10.5220/0001852102770280

 SciTePress

2 STATE OF THE ART

In the past few years, MAS proved to be a good ap-

proach to solve in cost-effective time a large set of

several types of distinct problems. In short terms, a

MAS is nothing more than a computer system where

typically there are several computing entities (agents)

with autonomous or semi-autonomous behaviors in-

teracting simultaneously in a speciﬁc environment.

The environment may directly related to real world,

and in such case the agents (usually the program that

a robot runs) base their behavior on sensor reading;

or it may interact with a fully digital environment

(simulators; industrial control engines etc.), (Lesser,

1999). The architecture of these is in most cases dis-

tributed as typically there is no need to centralize the

processing of each individual agent and thus over-

load the computing capacity of the station at hand.

Multi-Agent systems usually involve coordinating the

agents’ actions. This can be achieved following two

approaches. In one of them each agent tries to op-

timize its well being which is something that suits

well on competitive situations, as in most cases agents

may have contradictory objectives. In the other ap-

proach, a global goal is the pursued by a team of

agents and thus the coordination involved is quite dif-

ferent specially in what concerns the information to

be shared (Jennings, 2000). Such shared informa-

tion may be distributed among all the agents or there

may be hierarchical topologies so to promote agents

to leader agents. Coordinated agent positioning is one

of most sonant research topics concerning MAS. The

concepts involved point to strategies regarding decen-

tralized algorithms to make a group of agents with

a logical presence in a 2D or 3D space to position

themselves efﬁciently in order to effectively achieve

a common goal. One example of work conducted in

this area is presented by Scheutz (Scheutz and Bauer,

2006), where he presents a simulator of a biological

swarm where agents jointly achieve tasks using lo-

cal rules rather than global centralized or distributed

strategies. Each agent has a coverable area and the

main goal is to maximize the global coverable area

by the swarm by making the agents form a geometri-

cal polygon autonomously. Nowadays, there are sev-

eral real world systems that are simulated using MAS.

Many of them are already associated with worldwide

competitions while others are related to speciﬁc sce-

narios like particular ecosystems. Some of the MAS

that best ﬁt the ﬁrst case are the ones involved in

the Robocup competitions. The soccer competition

was the ﬁrst to be introduced in 1997 on a worldwide

scale. In the last few years other competition with

MAS emerged. The RoboCup Rescue was probably

the most relevant one. Proposed by Kitano (Kitano

et al., 1999), the idea behind this new competition is

to simulate how do rescuing entities (Fire Brigades,

Police Forces, Medical Teams) act after a large scale

disaster like tsunamis, earthquakes, tornados, ﬁres.

The simulator creates an environment representing a

virtual city with a set of rescuing objectives (save peo-

ple and property) that must be addressed within a lim-

ited time frame. It supports six categories of agents

with different missions. There is a type of agent for

each rescuing force and also a leader agent for each

of these forces. The common agents coordinate them-

selves by communicating with their respective leader.

The leaders can also communicate among themselves

to plan large scale actions.

3 APPROACH

3.1 The Pyrosim Platform

The massive destruction of our planet’s forests is one

of nowadays most critical issues for nearly every gov-

ernment on the globe. Following that perspective it is

important to study this phenomenon in order to draw

better and more precise ﬁght plans. The Pyrosim plat-

form simulates ﬁre in a forest area with several pos-

sible focuses. Fire emerges as a result of a combi-

nation of multiple nature factors. The spread of the

ﬁre is also dependant on several factors that combine

themselves on many different ways. The most rel-

evant factors that are supported in Pyrosim simula-

tor are: wind, ground topology and vegetation vari-

ety. The Wind has two sub variables that should be

taken into account, namely its intensity and direction:

Intensity. It is relevant to state that the ﬁre propaga-

tion increases with the wind intensity; however, if it

has extremely high values, the wind may transform

itself in a ﬁre extinction factor. Direction. In a sit-

uation analysis the direction of the wind is often the

direction of the spread of the ﬁre and thus it is also

a relevant factor. In Pyrosim the terrain can be plain

or heterogeneous with several valleys and hills. This

topology not only because that inﬂuence the positions

that the ﬁreman (agents) must assume to ﬁght the ﬁre

but also because it has an effect on the ﬁre propaga-

tion speed. In the case of a climbing ﬁre it tends to

spread faster as the air overheats and moves up cre-

ating optimal conditions for this effect. On the oppo-

site situation the propagation of the ﬁre has a reverse

behavior. In Pyrosim the terrain can be plain or het-

erogeneous with several valleys and hills. This topol-

ogy not only because that inﬂuence the positions that

the ﬁreman (agents) must assume to ﬁght the ﬁre but

also because it has an effect on the ﬁre propagation

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speed. In the case of a climbing ﬁre it tends to spread

faster as the air overheats and moves up creating op-

timal conditions for this effect. In this speciﬁc simu-

lator there are four types of vegetation: trees, sticks,

ground plants and humus. Each randomly generated

terrain by this platform has different ignition temper-

atures concerning this factor.

3.2 Multi-Agent System Architecture

In our approach there are two types of agents, namely

leader and worker. The classes that control them are

FireBirde and FireFoz respectively. They are both

extensions of the AgentSkeleton class because each

agent must have a physical representation on the map

and so it can virtually die. If this happens before his

death the leader passes his global information to the

healthiest worker which is later promoted to leader.

Every 20 ms the leader collects data on its work-

ers. The agent’s perceptions and their knowledge of

the world are stored instances in the class ﬂoatmap.

These are basically bidimensional matrixes represent-

ing an area surrounding the agent in what concerns a

given factor of the world. In this research work the

used matrixes were the ones related to presence of

ﬁre at short and medium distance. The matrixes that

represent the presence of ﬁre in the global map and

the terrain medium height were also used. These last

are less accurate than the previous ones because they

are 32*32 and the map is 128*128 which means that

each positions in the global map represents an aver-

age value of the four aggregates. These large scale

matrixes intend to simulate an area pocket map.

3.3 Multi-Agent Communication

System

When launched on the platform, the worker agents

send their position (POS) to the leader. The leader

agent acknowledges the worker as a team member,

estimates a good position to ﬁght the ﬁre (the used

method is described in the next subsection) and sends

it the coordinates of such position (GOTO). After re-

ceiving the new coordinates, the worker agent con-

sults his global ﬁremap and calculates the best possi-

ble path to achieve these new coordinates. After get-

ting to the assigned point, the agent notiﬁes the leader

(FORM) and the leader answers back with a request to

enter the team formation. The worker agent also no-

tify the leader of the impossibility to reach the desire

destination (e.g the designated point is inhabitable).

3.4 Heuristical Route Algorithm

To store the paths that must be ran, the agents use

graphs that are implemented the classes Graph, Edge

and Vertex. The graph is based on the world map ma-

trix, which is formed by a matrix of 32*32. By de-

fault it is assumed that the agents are able to go from

a given point to every neighbor in both ways assum-

ing 8 connectivity. This graph is generated in an ex-

tremely efﬁcient way as from the top left corner, three

threads are launched to quickly explore the map con-

sidering the south, southeast and east directions. After

the generation process which assumes the same cost

in both ways for each edge, a heuristic calculus is per-

formed to reevaluate these variables. The ﬁrst step is

to recalculate all the distances using 3D coordinates

and then for each edge the algorithm calculates the

percentage difference of height between the two con-

necting points and adds a cost to one of the ways in

case of a climb or a bonus in case of a descent (this

is only applied if the considered slope is bigger than

20%). If the slope is larger than 70%, the route is

considered to be impossible. The map has enough

resolution so that this algorithm does not fail in the

presence of valleys. The route calculus is based on

the well known A* (Russel and Norving, 2003) star

algorithm by providing it the ﬁreman’s current posi-

tion and ﬁnal position which is estimated according

to what is described in the next paragraph. At this

point it is relevant to state that each worker agent has

a speciﬁc memory region to store the graph and pro-

cess the A* algorithm, allowing a faster path recogni-

tion than what would be achievable by simply using a

collections data structure provided by the Java Virtual

Machine.To estimate a good position to ﬁght the ﬁre,

the leader agent consults his world ﬁre map and stores

the coordinates where the temperature is higher (ﬁre

point). After that to estimate a good ﬁre ﬁghting posi-

tion, the leader consults the current wind conditions

and determines a position where the worker agents

may center their formation. The formation will face

the ﬁre against the wind. It may slightly rotate if such

rotation allows the agents to assume a higher ground

position.

3.5 Fire Fighting

After the agent reaches the designated position it asks

the leader where it should position itself to start ﬁght-

ing the ﬁre. The leader that knows where every agent

is and also knows the current wind direction, assigns

each of them a base position on a line that is perpen-

dicular to the wind direction and also that has its 0

axis point aligned with the front of the ﬁre.

STRATEGIES FOR ROUTE PLANNING ON CATASTROPHE ENVIRONMENTS - Coordinating Agents on a Fire

Fighting Scenario

279

4 RESULTS

The obtained results were quite satisfactory, mainly

in what concerns the agent’s main objective, to extin-

guish the ﬁre. Using the proposed strategy the agents

are able to reach their designated ﬁre ﬁghting posi-

tion really fast because the A* star algorithm seems

to be correctly applied and the processing has been

tuned up by using a memory region for the Pathﬁnd-

ing of each single agent. The tests were conducted

with the default conditions, having the ﬁre a single

focus (point of ignition) and having the teams from

ﬁve to ﬁfteen members plus the leader agent.

5 CONCLUSIONS AND FUTURE

WORK

The proposed strategy proved to be efﬁcient in the

terms assumed in the previous section. Even small

ﬁve member teams are able to extinguish the ﬁre,

assuming that it ignites when all the agents are al-

ready in the map. The Pyrosim proved to be a mature

platform mainly in what concerns ﬁre generation and

spreading and robustness in the communication pro-

tocol between the agents. The heuristics used in the

A* algorithm proved to be close to optimal because

the paths walked by the agents were apparently good

choices. Some tests were conducted having worker

agents use the A* and others the simulated anneal-

ing algorithm (Russel and Norving, 2003). In all the

tests the A* agents got to the destination faster than

the simulated annealing ones (considering the same

departure point). From the visual analysis on Py-

roviz, one could state that the second method disre-

gards some interesting paths that seem to be bad on a

short term perspective and that are chosen by the A*

algorithm. As for future work trends, there are several

possibilities. At this point the agents are only able to

ﬁght ﬁres with a single focus. However it would be

interesting to increase the number of focuses of the

ﬁre. The following ﬁgure (Figure 1) which comprises

data about landscapes with of 1960 square kilometer

of area, having the line segment between the agent’s

starting point to the ﬁre focus about 1300 meters, con-

ﬁrms that the best suited method for this approach is

A*.

Figure 1: Results Obtained by different Methods.

Other methods were tried on landscapes with dis-

tinct levels of sinuosity. On the left side of the ﬁgure,

(green background), the values represent tests con-

ducted having Pyrosim conﬁgured for plain scenar-

ios while on the right (brown background) mountain

ones. On plain scenarios, A* might not be the best

approach since there is no real gain with the process-

ing overhead. In this case one might even say that

the best solution is to use greedy algorithms. Counter

measuring this last observation, A* is clearly the best

method for mountain scenarios, which in fact corre-

spond to the places where there is more need for a

good planning in ﬁnding the correct path. In a real

ﬁre ﬁghting situation, the ﬁremen have emotions that

affect their judgment and performance. To add these

additional variables to our approach, an integration

with the work presented by Sarmento in (Sarmento

et al., 2004) would probably be a good contribution.

The current version of Pyrosim also supports indoor

spaces. Fighting ﬁre in these spaces is quite differ-

ent from ﬁghting in outdoors. Indoors have different

types of materials and a ﬂammable conditions, spaces

and accesses tend to small sized, smokes may be toxic

and the propagation may differ a lot from room to

room. To create a good ﬁre extinction strategy that

keeps every ﬁreman alive is a challenge by itself.

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