Content uploaded by Juan Calderon
Author content
All content in this area was uploaded by Juan Calderon on May 19, 2020
Content may be subject to copyright.
Robot Swarms Theory Applicable to Seek and
Rescue Operation
Jos´e Le´on1Gustavo A. Cardona3Andres Botello2and Juan M. Calder´on1,2
1Department of Electronic Engineering, Universidad Santo Tom´as, Colombia
joseleonl@usantotomas.edu.co
2Department of Electrical Engineering, University of South Florida, Tampa, FL,
USA
juancalderon@mail.usf.edu, abotello@mail.usf.edu
3Department of Electrical and Electronics Engineering, Universidad Nacional de
Colombia. Bogot´a, Colombia.
gacardonac@unal.edu.co
Abstract. An important application of cooperative robotics is search
and rescue of victims in disaster zones. The cooperation between robots
requires multiple factors that need to be taken into consideration such
as communication between agents, distributed control, power autonomy,
cooperation, navigation strategy, locomotion, among others. This work
focuses on navigation strategy with obstacles avoidance and victims lo-
calization. The strategy used for navigation is based on swarm theory
where each robot is an swarm agent. The calculation of attraction and
repulsion forces used to keep the swarm compact is used to avoid obsta-
cles and attract the swarm to the victim zones. Additionally, an agent
separation behavior is added, so the swarm can leave behind the agents,
who found victims so these can support the victims by transmitting their
location to a rescue team. Several experiments were performed to test
navigation, obstacle avoidance and victims search. The results show how
the swarm theory meets the requirements of navigation and search op-
erations of cooperative robots.
1 Introduction
Unfortunately, Throughout the history of mankind, we have experienced un-
countable natural disasters and terrorist attacks, which decimate cities and
towns. Evidently, these events end with the lives of many people, and that is the
main reason why numerous approaches exist to deal with the before, during and
aftermath of a disaster.
When a disaster occurs, the most critical steps that need to be followed in
order to save lives are exploration, search, localization and rescue of survivors.
Each step is accompanied by a set of challenges for rescue teams, because af-
fected areas are difficult to access and are extremely dangerous, for example
they might have uneven grounds, which are prone to landslides, or collapsed
buildings, among other possible scenarios. Fortunately, we have seen significant
2 Jos´e Le´on et. al
progress in the interaction between rescue workers and robotic platforms used
in rescue operations, as shown in the work presented by Casper in [1], which
deals with the events presented in the attacks of the World Trade Center. This
type of work has allowed the public to see the advantages, disadvantages and
applicability of robotics in disaster zones.
One of the recent theories applied to rescue robotics are bio-inspired systems,
which are based on animals, specifically insects like ants, termites and bees [2],[3].
These animals show applicable behaviors, such as localization, recognition and
are also able to explore large areas, when they are searching for an specific tar-
get. Ethology says these animals possess cooperative dynamics, which can be
explained in mathematical form. In fact, the implementation and theoretical de-
velopment of the behavior of swarm agents can open a new path to multi-robots
systems, which can provide different kinds of solutions with simple structures to
complex challenges. One of the possible approaches of this bio-inspired system,
which is the one being covered by this paper, is swarm theory applied to the
simulation of robots for rescue applications. These robots can keep a formation,
while searching for position targets and can also avoid obstacles.
The use of swarms of robots in exploration of disaster zones facilitates and
streamlines rescue tasks, thus making the recognition and mapping of affected
areas faster and safer for rescue workers, while providing more information for
search-and-rescue of possible survivors.
The other sections that complete this paper are Section 2 - Related Work,
Section 3 - Robotic Swarms, Section 4 - Experiment and Results, Section 5 -
Conclusions.
2 Related Work
Proper search and rescue planning should take into account all of the factors,
that can hamper decision-making and lower the performance of duties, such as
high stress levels and disorientation that normally affect rescue workers during
disaster events. This is critical to safeguard lives and facilitate prompt assistance
to those who require it. In disaster events, it is important to have access to a solid
contingency procedure, as explained in the work presented by Huder in [4], which
discusses the first hours and critical initial days that take place during disaster
responses. It is in the first hours and initial days when the robot action can be
crucial to save lives. It mentions the precautions and planning that individuals
must carry out beforehand and also gives different perspectives on how to manage
critical situations presented in diverse types of disasters.
As mentioned in previous section, a possible addition to current search and
rescue procedures is the implementation of robotic platforms to locate survivors.
As previously mentioned, a benchmark for these type of robotic platforms was
displayed during the 911 attacks, where Casper et. al in [1] presents the advan-
tages and disadvantages associated with the implementation of this technology
and the performance of each robot that deployed. It is also worth mentioning
that the robots used by Casper were not conventional, since they were struc-
Robot Swarms Theory Applicable to Search and Rescue Operation 3
turally designed to withstand though disaster conditions. In the work presented
by Ventura et. al. [5], the need to use heterogeneous robotics teams, such as
aerial robots that perform exploration and recognition of the disaster zone, was
discussed. In addition to that the author mentioned the need to include power-
ful robots able to remove debris and rubble, and small agile robots capable of
reaching people trapped under ruins.
Regardless of the robotic platform selected, it is clear that they must have a
certain level of adjustable autonomy, basic learning capabilities, and object han-
dling abilities, in addition to sufficient human interface options for rescue teams.
In this paper, we have outlined the achievements of several search and rescue
robots from around the world, in order to shine more light on how these platforms
work and to show the increasing level of autonomy that we have witnessed in the
last decade. In the contribution made by Seljanko et. al. in [6], They proposed a
low cost and low maintenance add-on search and rescue robotic system with high
reliability and robustness, which includes electronic devices such as microphones,
speakers, GPS and integrated camera, thus resembling a smart-phone.
Swarm robotics theory can be best implemented in search and localization
of victims in disaster zones, due to the teamwork or cooperation displayed by
the swarm, as presented by Tan in [7], who said that the main advantages of
using swarms robots is the exploration and recognition of larger areas in less
time, specially when compared to the single robot case, thus allowing greater
flexibility which is ideal for our application. It is also advantageous for rescue
teams to have a swarm of robots, because if some of the agents of the swarm
were to fail, the absence of these agents does not seriously affect the overall
success of the swarm, which clearly demonstrates the greater robustness of the
decentralized control system.
To work with robotic swarms, we must take into consideration factors such
as communication, information sharing between agents, the distributed control
algorithm, cooperation and navigation, and that is why will explore them in this
paper.
Several algorithms designed for the navigation of swarm robots have been
tested in the past, one of the more commonly used ones is the swarm intelligence,
which was presented by Couceiro et. al. in [8]. This paper presents a comparison
between the Particle Swarm Optimization (PSO) and an algorithm derived from
the PSO called Darwin Particle Swarm Optimization, which is based on the
theory of evolution. The results obtained from the PSO algorithm are generally
good, but sometimes fail to be trapped in the local optima and can not find
the global optimum, on the other hand, the DPSO is a variation, which can
perform better, because it divides the swarm into sub-swarms. Dividing the
swarm into several sub-swarms is beneficial, because the sub-swarms can share
the best solution with the other sub-swarms and if this new team solution is
better than the possible solution derived by the swarm as a whole, the sub-
swarms are allowed by the complete swarm to go to the new optimal solution,
thus avoiding being trapped in the local optima.
4 Jos´e Le´on et. al
3 Robot Swarms
In nature, there are several species that display swarm like behavior, such as
bacteria, insects, birds, fish and horses among others. As can be expected, these
groups are composed of individuals, who possess specific distinct capabilities
and behaviors, but when they all get together as one group, and operate as
such, they will behave differently in order to work as a group. One of the more
notable examples of this group behavior is a swarm of honey bees choosing
a place to build a new honeycomb, this phenomenon was presented by Seeley
et. al. in [9], whose paper showed that this swarm behavior, which takes place
during the spring and summer, is best exemplified when the colony outgrows
its honeycomb, and then proceeds to divide itself to find new grounds and then
gets back together as a swarm once it finds the correct location to build a bigger
hive. To be more specific, The new site selection begins with several hundred
scout bees, who leave the colony in search of potential new sites, once the scouts
find an appropriate candidate, they return to the hive in order to report with
a wiggle dance where the potential new place is located. Once the scouts select
the correct location from the pre-selected sites, they will steer the rest of the
swarm to the new location by chemical stimulation. After the selection step is
completed, the division process of the hive begins, and the old queen with nearly
half of the colony leaves the hive to build the new one, while a younger queen
stays with the other half of the colony in the old honeycomb.
It is also important to highlight that the ability of the swarm to navigate can
be affected by a set of critical factors, such as collisions between themselves, large
obstacles in route, pheromone communication errors, erroneous scout indications
and even poor sense of direction.
We can represent the swarm of bees with the help of graph theory with
the application of the concepts worked by Mesbahi in [10]. One can describe
the system by defining Nagents representing each member of the bee swarm,
or in the case of our application a robot, and a connection representing the
communication and transmission of information between the agents, either bees
or robots. As mentioned by Mesbahi, we first need to define the agents of the
system (V, E ), where Vis the set of nodes in the system and Estands for the
topology of the system, we also need to define N(i) as the neighborhood or
adjacent nodes to node i. Regarding the links between the agents, we can be
describe them as a dynamic system.
It is advantageous to apply graph theory to control swarm responses, because
it allows us to apply concepts such as finding the Laplacian, which represents
the system, and assuming that the whole system is a graph fully connected.
In addition to that, we can assume that the system has no noise, links are
bidirectional, or even digraphs with different hierarchies.
The Algorithm used to simulate the swarm behavior is based on the one
proposed by Passino in [11]. This algorithm is explained as follows; We also
want to specify to the reader that this algorithm is not focused on a spacial kind
of animal swarm, and therefore we are just referring to Nnumber of agents.
Robot Swarms Theory Applicable to Search and Rescue Operation 5
Agents: : Each agent is described as a point in the space with position and
velocity. Initially, the position and velocity are defined randomly. We assumed
that each agent can detect the position and velocity of the rest of the swarm
agents. The swarm agents interaction is represented by graphs (G, A) where
G={1,2, ..., N }is a set of nodes and A={(i, j) : i, j ∈G, i 6=j}represents
the communication and sensing topology between the ith agent and each of the
other jth swarm agents. We will now define the terminology and elements used
in our algorithm.
Interaction : The interaction of the swarm agents is defined by the attraction
and repulsion strategy. This strategy allows the agents to keep a comfortable
distance over the other swarm agents. The attraction parameter tries to main-
tain in close proximity every element of the swarm and thus gives the group a
mechanism to remain grouped together. The attraction parameter is defined by
(1)
−ka(xi−xj) (1)
where kais the attraction force, this mechanism can be local (having a restriction
by the sensing range) or global (Agents can move other agents from the group
regardless of how far they are).
The repulsion parameter allows the agent to keep distance from the other
members of the swarm. This avoids collision between agents when the swarm is
moving. The repulsion mechanism can respond in two different ways. First one,
when a comfortable distance is reached and its parameter is expressed by (2)
[−k(kxi−xjk − d)](xi−xj) (2)
where kxi−xjk=2
p(xi−xj)T(xi−xj), k > 0 is the repulsion magnitude and
dis the comfortable distance between ith agent and the jth agent. On the other
hand, if two agents are two close to each other, the parameter is represented by
(3).
krexp(−1
2kxi−xjk2
r2
s
)(xi−xj) (3)
Where kr>0, represents the repulsion magnitude and rs>0, is the repulsion
range.
Environment: This is the space where the agents can move and it is composed
of good and bad areas. These good and bad areas are analogous to places where
agents can find food and to areas that have predators respectively. For this
present work, the good areas are defined by the presence of survivors, who need to
be rescued and the bad areas are full of ruins, collapsed structures and obstacles
that impair the ability of the swarm to navigate freely.
The environment is defined by J(x), where x∈ <n. Its is assumed that J(x)
is continuous and that it has a finite slope in every direction. The agents navigate
to the negative gradient (4) of the J(x)
−∇J(x) = −∂J
∂x (4)
6 Jos´e Le´on et. al
As mentioned above, the obstacles and victims that must be found by the
agents are part of the environment. For the current work, both elements generate
forces of repulsion and attraction over the agents. In this way, the interaction
between agents with victims and obstacles is modeled using equation (3) with
some changes in the meaning of the variables.
Interaction between agents and obstacles: The obstacles produce a repulsion
force over the agents. This repulsion is modeled using (3) where kr>0, and rs
is related to the size of the obstacle.
Interaction between agents and victims : The victims exert an attraction force,
where kr<0 and rsis proportional to the victims density in the near area.
Left Behind: Every agent is exposed to different attraction and repulsion forces,
which help to keep the swarm cohesion and at same time pushes the swarm
towards the goal. The victims generate an attraction force over close agents and
this force can disturb the normal behavior of the swarm and reduce the velocity
of the agents in proximity to the victims. The krmagnitude was put in place
so that it can stop at least one of the closest agents near the victims. When
agents are navigating close to the victims, at least one agent must stop near
the victims. Once one or more agents stop beside to the victims, those agents
are left behind by the swarm. They are separated from the swarm, breaking the
communication and attraction forces that keep them into the swarm. The agents
left behind create a new smaller swarm close to the victims, which changes its
status to stationary with the victims and the starts to transmit the localization
of the newly found victims.
4 Experiment and Results
In order to perform experiments for swarm navigation, the model previously
explained was implemented using Matlab. Four different types of experiments
were developed to show and analyze the swarm behavior in search and rescue
operations.
Obstacle avoidance: The first version of this experiment is performed using 10
agents and small obstacle as shown in Fig.(1). This part of the experiment depicts
how the swarm goes around the obstacle in order to avoid it. This is possible,
because the obstacle is relatively small and the agents are able to tolerate the
obstacle between the attraction forces. The second version uses a bigger obstacle
as depicted in fig(2). In the second case, the agents avoid the obstacle by taking
a side path, this occurs because there is not enough space between the attraction
forces to allow broad obstacles to stay in between the agents.
Robot Swarms Theory Applicable to Search and Rescue Operation 7
Fig. 1: Navigation with a Small Obstacle
Fig. 2: Navigation with a Big Obstacle
Multiple Obstacles: This part of the experiment depicts how the swarm goes
around obstacles in order to avoid them. This is possible, because the obstacles
are relatively small and the agents are able to tolerate the obstacles between the
attraction forces, as depicted by fig.3. This case uses nine obstacles distributed
throughout the area between the start point and the goal point. It forces the
swarm to navigate through the obstacles. while avoiding them and moving to-
wards to goal point. Figure 3 shows the agents navigating and exploring the
zone. At same time, the path described by every agent is drawn and it depicts
the explored area.
Victim Localization: This test uses a flat terrain to show how the swarm localizes
victims. This process is accomplished with the use of the ”left behind” process.
Once an agent has localized a victim, it stops near the potential victim. At the
same time, the swarm stops, because of the attraction force between the agents.
In that moment the process called ”Left behind” detaches the agents found
victims. The detached agents create a new swarm surrounding the victims. The
original swarm restarts to move towards the target point and leaves behind the
8 Jos´e Le´on et. al
Fig. 3: Navigation with Several Obstacles
swarm agents that are in charge of the victims. Figure (4) shows how the agents
stop near the victims and surround them, while the swarm leaves them behind.
Fig. 4: Search of Victims Area
Navigation and Victim Localization: This is a complete case where the swarm
navigates through the area full of obstacles and some places with potential vic-
tims. This test is divided in two experiments. The first one uses 10 agents and
2 victim places as shown by fig. 5. The second one has 40 agents and 5 victim
places (fig. 6). Both cases depict how the swarm covered the area navigating
through obstacles and localizing victims at the same time. The performance dif-
ferences between these two cases are the covered area and the number of agents
surrounding the victims in independent clusters. With large numbers of agents,
the covered area is bigger, because the repulsion force pushes the agents harder
to keep the distance between them. These agents use more area, to find an equi-
librium point of repulsion and attraction forces. The number of agents around
Robot Swarms Theory Applicable to Search and Rescue Operation 9
victims is also bigger, because there are more agents in the swarm and therefore
rises the probability of finding victims for each agent.
Fig. 5: Victims Zone with multiples Obstacles
Fig. 6: Several Victim Areas with a Big Swarm
5 Conclusions
An algorithm for search and rescue operations has been proposed using swarm
theory. The algorithm is based on concepts of attraction and repulsion forces.
These concepts keep the swarm as compact as physically possible and fix a
minimum distance between them. It is possible to represent the swarm with
graph theory, where the agents are nodes,the attraction and repulsion forces
are links between the nodes. The repulsion force is used to avoid obstacles and
to keep a minimum distance between the agents. The attraction forces allow
the swarm to stay compact and to be attracted by the victims. Additionally, a
10 Jos´e Le´on et. al
concept called ”left behind” is introduced with the aim of making possible the
opportune separation of some agents from the swarm. Agents who find victims
are separated from the swarm and they are in charge of the global localization
of the victims so they can be rescued. Four different experiments were developed
with the objective of showing the performance of the swarm in several cases of
navigation. The experiments depicted show how the swarm navigates in areas
with multiple obstacles and simultaneously searches for victims. The algorithm
shows creation of new small swarms around victims, which can be used to support
and facilitate the localization of victims for rescue teams. The swarm algorithm
shows redundancy and robustness characteristics when it loses agents, but it
regroups with the ones that are left and keeps the navigation process. Future
work includes the use of algorithms based on graph theory to improve the relation
of the sub swarms and the global swarm. Additionally, the algorithm proposed
in the current work will be performed in a robotics simulator with the purpose
to check this ideas on aerial and ground robots.
References
1. J. Casper y R. R. Murphy, “Human-robot interactions during the robot-assisted
urban search and rescue response at the World Trade Center”, IEEE Trans. Syst.
Man Cybern. Part B Cybern., vol. 33, nm. 3, pp. 367-385, jun. 2003.
2. Quijano, N., & Passino, K. M. (2007, July). Honey bee social foraging algorithms
for resource allocation, part I: Algorithm and theory. In 2007 American Control
Conference (pp. 3383-3388). IEEE.
3. Quijano, N., & Passino, K. M. (2007, July). Honey bee social foraging algorithms
for resource allocation, Part II: Application. In 2007 American Control Conference
(pp. 3389-3394). IEEE.
4. R. C. Huder, Disaster Operations and Decision Making (1). Hoboken, US: Wiley,
2013.
5. R. Ventura y P. U. Lima, “Search and Rescue Robots: The Civil Protection Teams
of the Future”, en 2012 Third International Conference on Emerging Security Tech-
nologies (EST), 2012, pp. 12-19.
6. F. Seljanko, “Low-cost electronic equipment architecture proposal for urban search
and rescue robot”, en 2013 IEEE International Conference on Mechatronics and
Automation (ICMA), 2013, pp. 1245-1250.
7. Y. Tan y Z. Zheng, “Research Advance in Swarm Robotics”, Def. Technol., vol. 9,
nm. 1, pp. 18-39, mar. 2013.
8. M. S. Couceiro, R. P. Rocha, y N. M. F. Ferreira, “A novel multi-robot exploration
approach based on Particle Swarm Optimization algorithms”, en 2011 IEEE Inter-
national Symposium on Safety, Security, and Rescue Robotics, 2011, pp. 327-332.
9. Seeley, T. D., y Buhrman, S. C. (1999). Group decision making in swarms of honey
bees. Behavioral Ecology and Sociobiology, 45(1), 19-31.
10. Mesbahi, M., y Egerstedt, M. (2010). Graph theoretic methods in multiagent net-
works. Princeton University Press.
11. K. M. Passino, Biomimicry for Optimization, Control, and Automation. Springer
Science & Business Media, 2004.
