EasyChair Preprint№ 1806

Disaster Response Simulation by Drones Using
Group Decision Making Model BRT
Naoya Horio, Hiroaki Aritomi, Masao Kubo and Hiroshi Sato

EasyChair preprints are intended for rapid
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October 31, 2019

Disaster response simulation by drones using group decision making model BRT
Naoya Horio1†, Hiroaki Aritomi2, Masao Kubo3, and Hiroshi Sato4
1

Department of Computer Science, National Defense Academy, Kanagawa, Japan(Tel : 046-841-3810; E-mail: em58032@nda.ac.jp)
2
Department of Computer Science, National Defense Academy, Kanagawa, Japan(Tel : 046-841-3810; E-mail: masaok@nda.ac.jp)
3
Department of Computer Science, National Defense Academy, Kanagawa, Japan(Tel : 046-841-3810; E-mail: masaok@nda.ac.jp)
4
Department of Computer Science, National Defense Academy, Kanagawa, Japan(Tel : 046-841-3810; E-mail: hsato@nda.ac.jp)
Abstract: In recent years, the use of drones in harsh environments has attracted attention. There is an expectation that ahuman's complex work can be substituted by integrating the limited capability of drones by forming a swarm by making
use of their high mobility. To achieve this system we discuss corrective decision making of swarm. When instructions
from a human operator cannot be obtained, the drone swarm needs to judge the situation on the site and take appropriate
coordinated action. In this paper, it is shown that disaster response can be dealt by drones that perform tasks
continuously while changing the three priorities of patrol, firefighting, and supply depending on the energy status of thedrone. The conventional BRT model lacks a part to estimate the evaluation of options, and it has not been easy to use
for practical problems as discussed here.
Keywords: a collection of drones, best of n, distributed decision making

1. INTRODUCTION
In recent years, the use of drones in harsh environments
has attracted attention. There is an expectation that ahuman's complex work can be substituted by integrating
the limited capability of drones by forming a swarm bymaking use of their high mobility. Generally speaking,to act as a swarm is strong against failure of a unit while
it can keep enough powers and resilience to solve
various tasks. To achieve this system, we discuss
corrective decision making of swarm. When instructions
from a human operator cannot be obtained, the drone
swarm needs to judge the situation on the site and takeappropriate coordinated action.
In this paper, we use a simulation of disaster scene asa place where drones act. The drones in this simulation
are required to switch various subtasks that surpass
individual drone's ability, such as fire and rescuer
detection, firefighting, injured rescue, and informationtransfer, to calm down the situation. Here, it is necessary
to properly assign each drone to one of the subtasks. Inaddition, if the drone's energy is not sufficient for theworking time, the drone needs to supply its own energy
simultaneously.
So far, this theme is considered to be one of the roleassignment problems, and so much researches havebeen reported, for example, Fixed response theory [1],[2], and so on. These approaches are methods ofassigning tasks to agents. They can allocate agents
sufficiently and accurately, if the relationships among
tasks are known in advance. On the other hand, theseapproaches divide the agents into too small groups, sothey may lose the above merits of the swarm generating,which they are working near each other geographically.
Therefore, in this paper, we think about a method to
response to disasters by achieving the same subtask

while always a large number of agents form a group. We
use BRT model as a decision-making model of drone forthis swarm system.
BRT [3] is a method for the distributed agents choose
the best of n options, and it is possible for the agents
agree to one of the best choices out of three or more
options, even if there is no leader. We extend this modeland apply this to the drones responding to disasters andshow drones can use BRT to select appropriate actions
from the three subtasks of patrol, firefighting, andsupply, and can response to disasters.
So far, researches on BRT have been conducted, suchas when communication between agents is limited
locally [4], and an example to the El Farol Bar problem[5]. These models lack a part to estimate the evaluationof options, and it has not been easy to use for practicalproblems as discussed here. In the following, we showthat the swarm of drones can calm a disaster situation
down. They choose tasks together continuously whilechanging the three priorities of patrol, firefighting, andsupply depending on the energy status of the drone.
In the next chapter, we describe a model of cities
assumed as a disaster scene and drones using Unity. In
the chapter 3, we propose a BRT model that determines
the situation at the disaster scene and selects appropriategroup behavior. In the chapter 4, by computer
experiments, we confirm the drones are performing
patrols and firefighting while properly supplying themso that their energy does not reach zero, and drones canperform tasks continuously while switching priorities
appropriately.

† Naoya Horio is the presenter of this paper.

energy are charged each turn.

Fig.1 Future image of disaster response by drones.

2. DRONES SIMULATION USINGVIRTUAL ENVIRONMENT
We create a simulation of disaster scene using
Unity[unity.com], one of game engines. The following
describes the modeling of each object and task.
2.1 Disaster response by drones
Fig.1 shows about future disaster scene coping bydrones. Drones patrol in the town and discover disasters
and emergency patients without human instruction. And
if possible, each drone will respond to the disaster. If thedrone’s energy is low, the drone will go to supply itself.
The residents of the city can live with peace of mind
more than ever.

2.5 Modeling of the fireFig.2(C) shows an example of fire occurrence. Fireoccurs with a certain probability and does not spread. Afire cannot be extinguished unless it is found. One fire
has 500 points, and when one drone approaches a fire toa certain distance, perform firefighting activities andreduce the object's points by about 1-1.2 each step. The
fire goes out when it reaches 0 points. The amount of
the fire reduced in the whole drone group is taken as
FHP.
2.6 Modeling of Patrol
Patrol task occurs in the area that requires the mostpatrol. Necessary patrol amount is made to appear ineach district and indicated by the patrol gauge (red). As
each area is approached, 1-1.2 points decrease in everystep. When the gauge reaches 0, it occurs in anotherarea. Fig.2(D) shows an example of a gauge.

3. PROPOSED METHOD
3.1 BRT [3]
BRT can handle more than two-choice problem of thegroup decision-making model.
𝑛(𝐴𝑖 (𝑡))(1)≥ 𝜃𝑖 + 𝜏 ∙ 𝑐𝑖 (𝑡) ∙ (𝑡 − 𝑡𝑖,𝑙𝑎𝑠𝑡 (𝑡))𝑁
Each agent has a personal attribute bias value θ i. τ is theincrease in the number of supporters, ci(t) is a functionthat is 0 when a suitable option can be found, and t(i,last)(t) is the time to keep selecting the same option. Theleft side is the proportion of agents who have the sameaction choice as their own, and changes the current
option when the right side exceeds the left side. It is
known that this formula causes the population to trial
and error and transition to a suitable state.

Fig.2 Modeling of the simulation.

2.2 Modeling of drones
Fig.2(A) shows the modeling of drones in thissimulation. one drone has 10000 energy and is displayedin real time on the HP gauge. DHP(Drones HealthPoint) is the total energy of a drone. Each drone
consumes 30 energy each turn. In addition, drones areequipped with a fire radar, and the closer the distance tofire, the higher the possibility of detection.
2.3 Modeling of the field
As shown in Fig.2(B), The field is divided into nine
blocks of A to I, and drones patrol each section.
2.4 Modeling of the Charging station
At the charging station, as each drone approaches, 1000

Fig.3 Three tasks in the simulation.

Table 1 Switching c by three tasks.
(X and Y are constant)
Current
When a fire hasbehavior
been found(1)Supply
c=energy/10000(2)Patrolc=X×6000/energy(3)Firefighting c=6000/energy

When a fire has
not been foundc=energy/10000
c=6000/energy
c=Y×6000/energy

3.2 Proposed method
Three tasks in the simulation is as shown in Fig.3. In the
proposed method, we focus on the fact that theimportance of three tasks is not equivalent and decide c.
Specifically, the following points were considered.
In order to drones perform the task continuously, do(1)supply so that the energy of drones does not become
zero. When a fire has not been found, (2)patrol isprioritized. When a fire has been found, (3)firefighting
is prioritized.
Based on the above, we decided c as shown in Table 1
so that each task can be switched appropriately.
3.3 Best action selection by proposed method
The problems with conventional research are as
follows.
・ Dealing with sudden fires is delayed. And we want
the drone to prioritize firefighting, but the drone cannot.
・ Supply of energy cannot be made in time, and thetask cannot be performed.
Based on the above, we examined the following
solution.
・ By setting the denominator of the evaluation function
c to energy, the evaluation function c becomes larger asenergy becomes smaller, and it becomes easy to change
the action selection to the supply.
・ While patrolling, If they find a fire, multiply Patrolby X. It was possible to switch the action to firefighting.
・ When they selected fire extinguishing activity when
there was no fire by mistake, they promoted the switchof action selection to the patrol by multiplying Y times.
The evaluation function c was determined as shown in
Table 1 reflecting the above.

4. EXPERIMENTIn this chapter, we use the proposed method to check
whether the drone group can properly perform the threetasks. First, it was tested whether basic operation could
be performed. The number of agents N = 50, theincrease in the number of supporters τ = 0.003, and one
simulation is 3000 steps . A fire occurs at 1/1500 per
step at random locations on the field.

Fig. 4．Transition of DHP
As shown in Fig. 4, it can be seen that the appropriate
refueling operation is selected so that DHP does not
become 0.

Fig. 5．Transition of workload of patrol
As shown in the Fig. 5, PHP is rising and it turns outthat patrol is being performed properly.

Fig. 6．Transition of firefithing task
As shown in the Fig. 6, FHP is rising and it turns outthat it corresponds to a sudden fire.

Intelligence, Vol.2, HAMT, 339-344, 2018
[5] Phung, N., Kubo, M., Sato, S. “El Farol Bar problem
by Agreement algorithm based on trial and error
behavior at the macro level”, The 22nd Asia PacificSymposium on Intelligent and Evolutionary Systems,
2018

Fig. 7．Switch action when a fire occurs
Finally, we show the cases where priority tasks haveoccurred in Fig.7. Drone group chooses patrol at first,
but switches to firefighting activity when they detectfire. It turned out that it can respond to a sudden
situation from this thing.

5. CONCLUSION
In this paper, we focus on the fact that the importance ofthe three tasks is not equivalent. By changing theevaluation function according to the drone energy anddrone selection, it was found that an agent with threeaction options can perform task while switching theaction options appropriately in the virtual space withreality. In this method, the importance of each task mustbe determined in advance, and drones cannot change theimportance. In the future, the importance of each task
should be learned by drones.

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