Multi-robot Decentralized Exploration using Weighted Random

Selection

Abhijith N. Balan

and Asokan Thondiyath

Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India

Keywords:

Decentralized, Frontier Allocation, Frontier-based Exploration, Multi-robot System.

Abstract:

The exploration tasks using multi-robot systems require efﬁcient coordination and information sharing be-

tween robots. The map creation is usually done through allocating frontiers,i.e., the boundary between ex-

plored and unknown regions of the map, to each robot. This paper introduces an efﬁcient frontier allocation

method based on weighted random selection for a decentralized multi-robot system. The weights are calcu-

lated based on the size of the frontiers and the distance between a robot and the frontiers. In this strategy, each

robot identiﬁes the available frontiers in a shared map and select the goal for exploration through a random

selection. Even though a robot is randomly picking a frontier without coordination with other robots, a collec-

tive intelligence is developed at the swarm level. The proposed method is computationally efﬁcient and uses

minimal communication between the robots in a decentralized multi-robot system. As a result, the robots get

allocated to frontier according to the calculated weight. A comparison with the nearest frontier exploration

approach in computerized simulation demonstrates the efﬁciency of the proposed algorithm.

1 INTRODUCTION

Robots have come a long way from a mere ﬁctional

entity to a useful tool that can aid or even replace

human effort in many situations. They can carry

out tasks that are difﬁcult or impossible for a hu-

man to perform. Introduction of Multi-Robot Sys-

tem(MRS) extends the capabilities of robots by many

folds. These systems work by coordinating multiple

robots to perform a single task or multiple tasks at

the same time. These systems feature high levels of

scalability, resistance to failure, and parallelization of

tasks. Such systems are very efﬁcient in tasks such

as search and rescue in disasters, nuclear site explo-

ration, surveillance, environment monitoring, and lo-

cating the sources of hazardous materials.

MRS is mainly classiﬁed into two categories, cen-

tralized and decentralized systems. A centralized sys-

tem would have multiple robots being controlled and

managed by a central entity. The central entity man-

ages the system’s decision making, communication,

and data storage. Failure to this unit would disturb

the entire system and the tasks can no longer be com-

pleted. A Centralized system has global information

about all the robots and their tasks. Access to this in-

https://orcid.org/0000-0003-4649-2429

https://orcid.org/0000-0002-5474-3999

formation makes coordinating and controlling a cen-

tralized robot swarm easier and efﬁcient. A decentral-

ized system, however, lacks the central entity. In such

systems, the robot swarm as a whole takes care of

decision making through local communication alone.

Since the robots lack the information of the state of

the system(position of the individual robots and their

task status), they take decisions based on its limited

perception of the environment and its neighbors. This

makes developing strategies for information sharing

and exploration in the decentralized system difﬁcult.

Even though a decentralized system will not be as ef-

ﬁcient as a centralized one, it offers scalability and re-

dundancy, which plays a crucial role in robot swarms.

A robot swarm is an autonomous multi-robot sys-

tem that can only perform local sensing and commu-

nication, with no access to information about other

robot’s position or task status (Brambilla et al., 2013).

They are usually homogeneous, containing simple

robots and resembles a decentralized MRS.

Since the exploration and mapping process is par-

allelizable, multiple robots can carry it out faster and

more efﬁciently than a single advanced robot. A robot

system performs this task through coordination, infor-

mation sharing, and data fusion. Autonomous map-

ping and exploration can be done through exploring

frontiers which are the boundary between mapped ar-

Balan, A. and Thondiyath, A.

Multi-robot Decentralized Exploration using Weighted Random Selection.

DOI: 10.5220/0010608405230529

In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2021), pages 523-529

ISBN: 978-989-758-522-7

523

eas and unknown areas. A robot navigating to a fron-

tier can disclose new regions and expand the map.

In this paper, we focus on exploration using decen-

tralized swarm robot systems. The most challeng-

ing problem in such systems is the decision making.

Since global information is unavailable to an agent

in a swarm, the agent chooses a local goal based on

the information available. In a mapping process, it

will be the selection of most promising frontier to ex-

plore next. A good strategy for optimizing the local

goal can reduce redundant data collection and make

the process faster and efﬁcient.

This paper puts forward a decentralized algo-

rithm for frontier selection which is computationally

efﬁcient and uses minimal communication between

robots. This implicit frontier allocation algorithm al-

lows each robot to take a decision with the local in-

formation it possesses without the need of coordina-

tion with other robots. The algorithm works better

with multi-robot systems with large number of robots.

Comparing to other implicit exploration algorithm,

our method was 13.7% faster for a multi robot sys-

tem of 7 robots without adding any extra communica-

tion effort. The independence of frontier selection on

robot coordination enables the algorithm to scale eas-

ily as the number of robots increase and makes this

algorithm the right choice for robot swarms in explo-

ration and mapping.

2 PROBLEM STATEMENT

The following deﬁnes the problem addressed.

The task is to reduce the overall duration of ex-

ploration by a ﬂeet of autonomous robots while keep-

ing the communication between the robots minimal.

The robot swarm is considered homogeneous and

is equipped with exteroceptive sensors, which allow

them to create a map of the environment. The robots

also contain devices for communication which are

crucial for cooperative exploration. The range of

communication is assumed to be less than the map-

ping range and may not cover the whole robot ﬂeet,

i.e., all robots are not connected to each other always.

The environment is unknown, and all the robots may

not start at nearby locations. One robot might explore

a frontier even if another robot had already explored

it.

Due to the limited communication range, one

robot may not be able to communicate with all the

robots in the ﬂeet. A robot should make a decision

based on the local information available at that time.

The algorithm should be efﬁcient in computation so

that the system would not be affected as the number

of robots in the ﬂeets is increased.

To limit the scope of the problem, the details

on robot localization, mapping, and map sharing are

not discussed in this paper. However, they are dis-

cussed in the work by (Balan, 2019) in which com-

plete multi-robot SLAM(Simultaneous Localization

And Mapping) simulations are addressed in both cen-

tralized and decentralized systems.

After identifying all the available frontiers to a

robot, the robot has to be allocated to one frontier

for further exploration. The inability of one robot to

know the position and status of all other robots in the

ﬂeet results in selecting a sub-optimal frontier. This

problem is inherent in a decentralized multi-robot sys-

tem.

The following notations are used:

• R is the set of all robots participating in explo-

ration. R = {R

......R

}, where n is the

total number of robots in the system.

• F

is the set of all frontiers available to the R

= {F

......F

}, where m is the total

number of frontiers available to R

• α

is the assignment vector for R

(

1 if F

is assigned to R

0 if otherwise

(1)

Since this is a decentralized approach, the available

frontiers and the assignment vectors are different for

each robot. Now the problem has simpliﬁed to ﬁnding

the assignment vector α for each robot using F at any

time.

3 STATE OF THE ART

In this section, we discuss the previously proposed

methods in multi-robot exploration.

3.1 Nearest Frontier Exploration

Nearest frontier exploration was a popular frontier

based exploration strategy formulated by (Yamauchi,

1997). Yamauchi recognized frontiers as the bound-

ary between the known area and the unexplored area

of the map. Navigating a robot to a frontier will en-

able the robot to identify unexplored area that conse-

quently increases the size of the explored area. In a

system with more than one robot, each robot has to

be navigated to the nearest frontier and broadcast the

map information to all other robots. The robot can

stop exploration when there are no frontiers left to ex-

plore in the map. This ensures coordination between

ICINCO 2021 - 18th International Conference on Informatics in Control, Automation and Robotics

524

the robots. The area in the map which was already

covered would not be covered again by another robot.

This is an implicit method, i.e., after receiving the in-

formation from other robots, a robot does not need to

communicate with other robots for selecting the best

frontier to navigate. Nearest frontier exploration is

a distributed approach and requires less computation

for frontier selection. The algorithm for nearest fron-

tier exploration is given in Algorithm 1.

Algorithm 1: Nearest Frontier Exploration O(m).

Input: F

Output: Assignment vector α

of R

= 1 with j = argmin

∀F

∈F

distance to F

;

Nearest frontier exploration can be reduced to

O(1) using Breadth-First Search from robot position

and taking the ﬁrst frontier we encounter. However,

it is possible that multiple robots may get allocated to

the same frontier if their maps are similar (when they

are close to each other. This is common in a robot

swarm). This keeps the ﬂeet from utilizing its full po-

tential to explore the map.

3.2 MinPos

MinPos is a decentralized frontier allocation algo-

rithm for multi-robot exploration by (Bautin et al.,

2012). This algorithm strategically allocates best

frontiers to robots.each robot evaluates its relative

rank among the other robots in term of travel distance

to each frontier. Accordingly, robots are assigned to

the frontier for which it has the lowest rank. To eval-

uate this criterion, a wavefront propagation is com-

puted from each frontier giving an alternative to path

planning from robot to frontiers. This method not

only considers the distance to each frontier but also

the number of robots closer to that frontier. The algo-

rithm builds a local minimum free artiﬁcial potential

ﬁeld from each group of frontier using the wavefront

propagation algorithm. This also builds the cost ma-

trix, which is used to allocate frontiers. The algorithm

for MinPos is given in Algorithm 2.

MinPos is a decentralized frontier allocation algo-

rithm. It does not require any central unit to function

like in MOARSLAM (Morrison et al., 2016). How-

ever MinPos algorithm requires the communication

range to be large enough so that it covers the whole

map,i.e., all robots should be in constant communi-

cation with all other robots at all times. MinPos re-

quires information about all the robots participating in

the cooperative mapping and their task status for the

frontier allocation to work. This would not be avail-

able in a robot swarm always. Even though MinPos

Algorithm 2: MinPos O(nm).

Input: R , F

,C cost matrix

Output: Assignment vector α

of R

for F

∈ F

j =

∑

∀R

∈R ,k6=1,C

end

= 1 with j = argmin

∀F

∈F

requires global information, the decisions are taken

by individual robots. It is also interesting to note that

the algorithm complexity is O(mn),i.e., the algorithm

becomes slower to allocate frontiers as the number of

robots in the swarm increases. This can cause MRS

to scale poorly.

3.3 Centralized and Decentralized

A centralized exploration strategy will have a cen-

tral server which allocates frontiers to each robot for

the maximum efﬁciency. In MOARSLAM (Morri-

son et al., 2016), the server stores the map created

by autonomous robots. The server guide the robots

to each location for exploration and the robots trans-

mits the data back to the server. In (Simmons et al.,

2000), the robots perform maximum likelihood esti-

mation of the map using odometry and observations.

These maps are transmitted to a server which devel-

ops the global map. A similar strategy is implemented

in (Gil et al., 2010), in which the robot sends the

virtual descriptors along with the odometry informa-

tion to the server. The server adds this information

to the global map. All these strategies represent a

single point which is in constant bi-directional com-

munication with individual robots. The performance

of the system depends on the capabilities of this cen-

tral entity. There are also limitations on the extent

of area that can be mapped (communication range of

the central unit) and the number of robots that can

communicate to the central unit at a time (communi-

cation bandwidth limitation). This inhibits the scala-

bility of the system. To make the systems decentral-

ized, one will have to remove the central server and

make the robots capable of making decisions. Nearest

frontier exploration 3.1 and Minpos 3.2, which were

discussed before, are examples for this. Another ap-

proach was proposed in (Yan et al., 2011) in which a

trade based scheme is implemented in a decentralized

multi-robot system so that each robot bids on a fron-

tier based on the limited information available. This

method can reduce computation cost through parallel

computation but induces a communication overhead

of O(mn), which does not facilitate the scalability fac-

tor.

Multi-robot Decentralized Exploration using Weighted Random Selection

525

We need a frontier allocation method which

is O(m) so that the system becomes easily scal-

able (computation cost is independent of number of

robots) and should work in the absence of global in-

formation.

4 PROPOSED METHOD

We propose an approach to the frontier allocation

problem based on weighted random selection. In this

approach, we calculate a weight for each frontier en-

countered based on its size and distance and allocate a

frontier to a robot according to it. This is a distributed

frontier allocation method that will divide the robot

swarm according to the size of the discovered fron-

tier (number of frontier points) and the distance to it.

This algorithm is computationally efﬁcient and does

not require robot communication to select the frontier

for exploration. The algorithm performance is inde-

pendent of the number of robots in the ﬂeet, which

makes the system to be scaled easily to any number

of robots.

In the proposed method, each robot performs the fol-

lowing steps. (More details on the robot system mod-

ules and their interaction are given in the ﬂowchart

(Figure 1))

1. Each robot explores freely in the environment.

2. At robot rendezvous, the i

robot broadcasts its

local map data to other robots.

3. Also, the i

robot receives map data from other

robots.

4. The robot R

creates an updated map.

5. R

performs weighted random selection for fron-

tier selection.

6. The R

navigates to the selected frontier

Figure 1: A schematic diagram showing the robot systems

module and their interactions. Note that the communica-

tion is only required for the Map Fusion, and the frontier

selection is performed within the robot system.

Although this is an implicit method, i.e., robots

do not coordinate with each other at the frontier se-

lection stage, the ﬂeet as a whole takes a meaningful

decision by dividing and allocating itself to different

frontiers in the map. This technique is similar to the

swarm intelligence present int the nature (e.g. Ants

ﬁnding the shortest path to food and Termites build-

ing their nests). These systems also contain various

forms of decision making with the help of limited lo-

cal information. Our technique enables a single robot

following simple local rules to give rise to a collective

intelligence in the swarm level.

4.1 Map Sharing

The robots upon rendezvous, share their map data and

update their local map. After the map update, all the

robot’s local maps share a common set of features.

Since the communication range is assumed to be less

than the mapping range, the complete map of the en-

vironment has been developed using these common

map features. Each robot fuses the received maps to

its local map to obtain the updated map result. The

image-based map merging algorithm, as illustrated in

orner, 2016) has been used for map sharing.

4.2 Frontier Classiﬁcation

After the robot develops the updated map, the robot

searches the map for frontier points. Frontier points

in the occupancy grid map are found by searching for

the points in between the explored and unknown parts

of the map. After every frontier points are identiﬁed,

they are clustered into individual frontiers (set of fron-

tier points) using a clustering algorithm based on their

position. After ﬁnding the frontiers, the shortest dis-

tance Euclidean distance (D

) from the robot R

to the

frontier F

, and the frontier size (S

, the number of

frontier points in the frontier F

) are calculated for

each of the frontiers. These parameters will be used

to select the frontier for exploration.

4.3 Frontier Allocation

In this stage, the robot is left with the frontiers and

their parameters (distance and size). We create the

weight of each frontier from these to parameters as

(2)

The weight indicates the tangent of the angle cre-

ated by a frontier in robot’s vision. Refer Figure 2. An

imaginary frontier is considered at the frontier point

ICINCO 2021 - 18th International Conference on Informatics in Control, Automation and Robotics

526

Figure 2: Diagram showing how the weight corresponds to

the angle made by the frontier in robot vision.

closest to the robot, aligned perpendicular to the sens-

ing direction, with the same frontier size. This fron-

tier makes an angle α in the view of the robot. Note

that tangent of the angle α is the weight that frontier

has. A large frontier located far away from the robot

gets lower weight. So the weights indicate the angles

each frontier makes in the vision of the robot. Refer

Figure 3 for example of such frontier selection.

Figure 3: A schematic diagram showing the concept of

weight used in the proposed approach. Each color (ma-

genta, green, blue, black) indicate an available frontier, and

the red circle indicates the robot R

. Here note that the an-

gle made by the frontier colored green makes the biggest

angle in the robot’s view. This frontier will have the largest

weight among all frontiers. Image credits: (Virtual Labs,

IIIT Hyderabad).

Now we select a frontier (or calculate α

) from

the set of frontiers through weighted random selection

using the weights we calculated.

(

1 if F

= Random Choice(F

) according to W

0 if otherwise

(3)

Algorithm for the proposed method is given below

Algorithm 3: Proposed Method O(m).

Input: F

Output: Assignment vector α

of R

for F

∈ F

end

SelectedFrontier ←

Random Choice(F

) with weights W

= 1 where F

= SelectedFrontier

Through individual robots performing a weighted

random selection for choosing frontier, we can divide

a robot swarm to multiple frontiers according to its

proximity and size. It is interesting to note that this di-

vision is happening without explicit coordination be-

tween the robots.

5 EXPERIMENTS AND ANALYSIS

The experiments are done in a computer simulation

using Robot operating system. The main concentra-

tion is on the time taken for thoroughly exploring the

environment. The proposed approach is compared

with the other implicit frontier allocation algorithm

Nearest Frontier Exploration.

5.1 Simulation

For simulation stage simulator (Figure 5) is used with

cave map accommodating all the robots used for ex-

ploration. The robots have 180

◦

vision for mapping

with parameterized communication range. The map

is scaled appropriately (30m X 30m) to simulate a

decentralized decision-making process with all the

robots. The robot is approximately the size of a 0.4m

cube.

The Figure 4a shows the comparison between the

map exploration time and the number of robots for

both the methods, Nearest Frontier Exploration, and

the proposed method. As both algorithms are dis-

tributed, with implicit frontier allocation, they can be

compared. The results shown are the average of 10

iterations on each robot count in both the algorithms.

Figure 4b compares the map exploration time

while varying the local communication range. The

local communication range denotes the distance to

Multi-robot Decentralized Exploration using Weighted Random Selection

527

(a) The trend in exploration completion time with respect to

the number of robots. (Local communication range = 5m)

(b) The dependence of the algorithms to the local communi-

cation range. (For ﬁve robot ﬂeet)

Figure 4: Simulation result using the cave environment. The ﬁgure shows a comparison between Map completion time

and Number of robots(a) and Local communication range(b). The proposed algorithm is compared with Nearest Frontier

Exploration Algorithm.

Figure 5: Simulation of 3 robots (blue boxes) in Cave map

loaded in stage simulator.

which the communication between the robots is pos-

sible.

5.2 Analysis

From the simulation results, we can see that the pro-

posed method, on an average, is 13.7% faster com-

pared to the other algorithm on high robot count. Al-

though both algorithms have similar results in low

number of robots, the proposed method shows a clear

advantage as the number of robots increases. We

can also see that the algorithm works better when

the local communication range is small. This in-

dicates the advantage our algorithm has in covering

large areas compared to the robot size and its sens-

ing range. We could observe that the robots tend to

move together in Nearest Frontier Exploration when

they were spawned very close to each other. Having

similar maps and frontier distances were forcing the

robots to ﬂock together. However, in the proposed

method, the robot system was allocated across the

frontiers even when they were outside the communi-

cation range. A larger frontier was getting assigned

more robots even if the robots are outside each other’s

communication range. This shows that our method

developed a collective intelligence in choosing the

frontiers for exploration without the need for any

communication between the robots. This proves that

the proposed algorithm can work better in a swarm

system containing a large number of robots.

6 CONCLUSION

In this paper, we addressed the problem of explor-

ing an unknown environment with the decentralized

multi-robot system. An algorithm was proposed for

this problem, which allocates the frontiers to the

robots efﬁciently so that the exploration time can be

reduced. The algorithm addresses the limitations of

other previous approaches and attains good results

with reducing the computation load on the robots and

also using less communication between the robots.

The proposed algorithm uses the concept of

weighted random selection for assigning frontiers

to robots. In the discussed scenario, i.e., mapping

an unknown environment, the weights are calcu-

lated according to the size and proximity of frontiers.

Other scenarios might require different parameters for

weight calculation (e.g., likelihood of ﬁnding a tar-

ICINCO 2021 - 18th International Conference on Informatics in Control, Automation and Robotics

528

get in search and rescue operations), but the underly-

ing concept of weighted random selection remains the

same.

The performance of this algorithm is compared

with nearest frontier allocation through computer

simulation. The proposed algorithm appeared to be

signiﬁcantly more efﬁcient in robot systems with

higher number of robots. Furthermore, our algorithm

has lower complexity than other multi-robot explo-

ration algorithms and is independent of the number

of robots in the ﬂeet. These properties make our

approach a good option in robot swarms for explo-

rations.

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