=Paper=
{{Paper
|id=Vol-2054/paper11
|storemode=property
|title=RescueRobot: Modeling Complex Robots Behaviors in Emergency Situations
|pdfUrl=https://ceur-ws.org/Vol-2054/paper11.pdf
|volume=Vol-2054
|authors=Giuseppe Palestra,Andrea Pazienza,Stefano Ferilli,Berardina De Carolis,Floriana Esposito
|dblpUrl=https://dblp.org/rec/conf/aiia/PalestraPFCE17
}}
==RescueRobot: Modeling Complex Robots Behaviors in Emergency Situations==
https://ceur-ws.org/Vol-2054/paper11.pdf
RescueRobot: Simulating Complex Robots
Behaviors in Emergency Situations
Giuseppe Palestra, Andrea Pazienza, Stefano Ferilli, Berardina De Carolis, and
Floriana Esposito
Dipartimento di Informatica – Università degli Studi di Bari ”Aldo Moro”
name.surname @uniba.it
Abstract. This paper aims at studying complex behaviors of search and
rescue robots in emergency situations. We used as environment of the
simulation NetProLogo in order to: i) build a simulated scenario with
robots, humans beings, and emergency exits, ii) endow robots with rea-
soning rules, and iii) evaluate robots behavior on the basis of two search
strategies. Preliminary results show that one search strategy reaches bet-
ter performance. Next studies will cover more simulations and different
scenarios.
Keywords: complex systems, rescue robots, reasoning
1 Introduction
Natural calamities such as fire, floods, earthquakes, hurricane, and tsunamis can
cause loss of human lives, ecological disruption, and damage to infrastructures.
Disasters typically occur suddenly and, with the same speed of the emergency,
safety services should be put in place. However, the standard ability of the
affected community is often not sufficient to deal with emergencies and humans
rescues. External resources are then required for such chaotic and dangerous
circumstances. For these reasons, mobile robotics in the domain of Search And
Rescue (SAR) would be helpful [6]. The current challenges in SAR development
are: robot design, simultaneous localization and mapping in the disaster area,
and human beings detection. In a SAR task, a humanoid rescue robot would be
designed, in the upper part, with two arms used to pick up humans, whereas
the lower part would consist of two tracks for a better rough terrain mobility.
Typically, they are equipped with sensors such as thermal camera, ultrasonic and
microphone in order to detect human beings. The main purpose of the adoption
of SAR robots is to help and facilitate the hardest operations of saving people
from danger. Other benefits of SAR robots to these operations include reduced
personnel requirements, and access to otherwise unreachable areas.
SAR robots are developed with specific abilities such as searching, reconnais-
sance, and mapping, removing or shoring up rubble, delivery of supplies, medical
treatment, and casualties evacuation. Even with all these ideas coming about,
there are still some technical challenges that remain. In fact, it is a hard task
to plan how a SAR activity is conducted, by forecasting which difficulty robots
may face, and analyzing complex phenomena that may evolve during a rescue.
In this paper, we address such problems by modeling, analyzing and simulating
a SAR activity conducted by robots. In a complex environment, different robots
behaviors may emerge, depending on the reasoning processes held to accomplish
a SAR activity. Will the robots cooperate? Or will they be in competition?. For
these reasons, we developed:
1. the robot behavior to search for humans in danger and rescue them in a
safety place;
2. the collective behavior of robots to perform this task in an efficient way,
using the swarm robotics approach.
Swarm robotics is a recently developed approach to the coordination of multi-
robot systems which consist of large numbers of mostly simple physical robots [7].
SAR robotics has received much attention in recent years and it is under inves-
tigation all over the world [4]. Lee et al. [3] propose a humanoid rescue robot
characterized by two arms used for carriage of people in the upper part whereas
the lower part consists of two track for better rough terrain mobility. Koch et
al. [2] describe a SLAM approach applicable to multiple mobile robots.
The main research goal is that a desired collective behavior would emerge
from the interactions between the robots and interactions of robots with the
environment, as well as insects, ants and other fields in nature, where a swarm
behavior occurs. This study aims at modeling complex behaviors of SAR robots
depending on random setup of evacuations by running a simulation in emergency
situations.
This paper is organized as follows. Section 2 presents an overview of the
whole system. Section 3 describes the experimental setup and the preliminary
results. Finally, conclusions are drawn.
2 RescueRobot
In this paper, we present RescueRobot, a multi-agent modeling environment
for simulating complex systems. We developed a scenario in which robots (i.e.,
agents) have to search and rescue humans in the emergency situation of evacu-
ation from a building on fire. In order to model and simulate this scenario we
endowed the robots with reasoning rules. A reasoning rules system is a software
system that infers conclusions from available knowledge using logical techniques
such as deduction and induction. In order to simulate the complex robot be-
haviors in the proposed scenario we exploited NetLogo [8], and in particular the
NetProLogo extension1 , that permits to run Prolog [1] inside NetLogo.
NetLogo is a multi-agent programmable modeling environment that enables
exploration of emergent phenomena. It comes with an extensive models library
including models in a variety of domains, such as economics, biology, physics,
1
https://github.com/jgalanp/NetProLogo
chemistry, psychology, system dynamics. It is possible to use agents as turtles,
links, patches and the observer. NetProLogo (NetLogo + Prolog) is a NetLogo
extension that allows running Prolog code inside NetLogo in order to take ad-
vantage of Prolog features to provide NetLogo agents (turtles, links and patches,
or the observer) with reasoning capabilities.
Prolog is a logic programming language used in artificial intelligence field.
Prolog uses rules for the knowledge representation, and an inference engine to
derive conclusions [5].
In this setting, a robot can help just one human per time. Hence, robots have
to keep in safe humans by guiding them out of the emergency status. As general
rescue strategy, robots exploit their internal stored map of the building to bring
humans to the closest safety point. We then designed two search strategies:
1. search the nearest human first (NearS);
2. search humans on the basis of their gender and age first (ReasS).
In the former, robots act with no particular reasoning skills and just look for
humans to rescue them as they find them. In the latter, robots should reason
about what type of person safe first. Suppose that robots can recognize human
characteristics by using face analysis algorithms. Hence, robots can infer gender
and age rescue policies to determine a ranking based on the probability of hu-
mans to fend for themselves. The outcome of this reasoning, based on human
characteristics, is the following ordered list:
1. little girls;
2. little boys;
3. elder women;
4. adult women;
5. elder men;
6. adult men.
3 Results
Several simulations have been run using 10 robots and a different number of
humans: 10, 20, 30, 50, and 100. Both search strategies have been evaluated
according to the following metrics:
1. time elapsed;
2. number of total visited places;
3. number of saved humans;
4. number of robot single unit movements.
In the NetLogo graphical environment the robots are drawn in blue, the exits
are represented in white, humans beings in red and robots that brings a human
in green. In Fig. 1a and 1b are illustrated respectively two simulation with NearS
and ReasS search strategies after 750 steps, with 10 robot, 100 humans and 8
exits. In particular, in Fig. 1b inside the red circle, it is possible to see a robot
.. ..
(a) NearS (b) ReasS
Fig. 1: NearS and RearS search strategies. 750 steps, 10 robots, 100 humans, and
8 exits. Exits in white, robots in blue, humans in red, robot that brings a human
in green.
on the same patch of a human. The robot does not bring the human because of
the reasoning strategy.
In Table 1 results for NearS search strategy are provided. Moreover, in Table
2 results for ReasS search strategy are provided.
Table 1: Results of the simulation using the NearS strategy.
Time Elapsed Visited Saved Robot single
People to save
(sec.) Places Humans unit movements
10 8 759 100 % safe 177
20 34 1710 100 % safe 735
30 12 773 100 % safe 251
50 13 837 100 % safe 259
100 24 1520 100 % safe 549
From the results in tables 1 and 2, it is possible to note that when the number
of humans is ≤ 10, the ReasS strategy performs better in terms of the number of
visited places and number of robot single unit movements. On the other hand,
as long as the number of humans to save increases, the NearS approach works
better, assuring always that the whole number of humans rescues is accomplished
in very few time.
Table 2: Results of the simulation using the ReasS strategy.
Time Elapsed Visited Saved Robot single
People to save
(sec.) Places Humans unit movements
10 11 446 100% safe 162
20 180 2600 100% safe 3693
30 317 2620 100% safe 6814
50 >500 3000 40% safe >10000
100 >500 3000 20% safe >10000
4 Conclusion
In this paper we presented a simulation of complex robots behaviors in emergency
situations. We have tested two search strategies through several simulations and
preliminary results. The simulations show that with a little number of humans,
the ReasS approach outperform the NearS one, while, as long as the number of
humans to save increases, the NearS approach works better. Since the nearest
person strategy performs better than reasoning strategy in most of the tested
cases, it is the recommended strategy to be adopted in big emergency situations.
As future works,other simulations will be performed and other strategies will
be modeled using also a hybrid approach.
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