Suppressing Energy Consumption of Transportation Robots using

Mobile Agents

Ryosuke Shibuya

, Munehiro Takimoto

and Yasushi Kambayashi

Department of Information Sciences, Tokyo University of Science, Chiba, Japan

Department of Computer and Information Engineering, Nippon Institute of Technology, Saitama, Japan

Keywords:

Mobile Agent, Multi-robots, Energy Consumption, Intelligent Robot Control.

Abstract:

This paper presents an application for controlling multiple robots connected by communication networks.

Instead of making multiple robots pursue several tasks simultaneously, the framework makes mobile software

agents migrate from one robot to another to perform the tasks. Since mobile software agents can migrate to

arbitrary robots by wireless communication networks, they can ﬁnd the most suitably equipped and/or the most

suitably located robots to perform their task. We previously implemented an application of searching targets in

our framework, and showed that it could suppress energy consumption of the entire system. In this paper, we

propose a new mobile agent system that transports the found targets to a collection area. The approach is based

on passing the targets among robots through throwing, and therefore, suppresses energy consumption as the

same manner for searching targets. In order to show the effectiveness of our approach, we have implemented

two strategies on a simulator and conducted numerical experiments. As a result, we show that our approach

has more advantages than previous ones, and there is a remarkable difference between the two strategies in

terms of energy saving.

1 INTRODUCTION

In the last decade, robot systems have made rapid

progress not only in their behaviors but also in the

way they are controlled. In particular, a control sys-

tem based on multiple software agents can control

robots efﬁciently (Takimoto et al., 2007).

On the other hand, excessive interactions among

agents in the multi-agent system may cause problems

in the multiple robot environments. In order to miti-

gate the problems of excessive communication, mo-

bile agent methodologies have been developed for

distributed environments(Kambayashi and Takimoto,

2005). Mobile agent systems are especially useful in

an intermittently connected ad hoc network environ-

ment. In the minimal case, a mobile agent requires

that the connection is established only when it per-

forms migration (Binder et al., 2001).

The model of our system is a set of cooperative

multiple mobile agents executing tasks by controlling

a pool of multiple robots as shown by (Kambayashi

and Takimoto, 2005). A mobile agent can migrate to

the robot that is most conveniently located to a given

task, e.g. closest robot to a physical object such as a

soccer ball. Since the agent migration is much easier

than the robot motion, the agent migration contributes

to saving power consumption (Takimoto et al., 2007).

We have proposed our model in the previous pa-

per (Takimoto et al., 2007) and have also shown the

effectiveness of saving powerconsumptionand the ef-

ﬁciency of our system for searching targets (Nagata

et al., 2009; Abe et al., 2011). In this paper, we focus

our attention on not only searching targets process but

also transporting the found targets to a designated col-

lection area. The sequence of behaviors for searching

targets and transporting them is one of the most im-

portant tasks for autonomous robots. For example,

the sampling missions on Moon and Mars are well-

known. Also, it is important for transporting target

things from the stricken area where the environment

is too dangerous for human to work. Our approach

enables robots saving energy consumption by pass-

ing targets among robots through throwing them. A

software mobile agent controls a robot and makes it

throw an object, and then the agent migrates to an-

other robot that receives the target. Thus instead of

mobile robots, the software mobile agent has the re-

sponsibly to transport the target object to the desti-

nation, and it achieves suppressing the number of ro-

tations of robot-wheels. Furthermore, our transporta-

219

Shibuya R., Takimoto M. and Kambayashi Y..

Suppressing Energy Consumption of Transportation Robots using Mobile Agents.

DOI: 10.5220/0004240902190224

In Proceedings of the 5th International Conference on Agents and Artiﬁcial Intelligence (ICAART-2013), pages 219-224

ISBN: 978-989-8565-38-9

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

tion technique has a good property of holding the way

in which robots are scattered, where it is more prof-

itable for robots to be scattered in a work ﬁeld for

passing targets through throwing.

The structure of the balance of this paper is as fol-

lows. In the second section we describe the back-

ground. The third section describes an example of

intelligent robot systems in which robots search mul-

tiple target objects and transport them cooperatively.

In our robot control system, the mobility that the mo-

bile agent system provides is the key feature that sup-

presses energy consumption; therefore the details of

mobile agent based strategies are shown here. In the

fourth section, we demonstrate how the properties of

mobile agents can contribute to saving energy con-

sumption through numerical experiments based on a

simulator. Finally, we conclude in the ﬁfth section.

2 BACKGROUND

Multi-agent robotic systems are recently becoming

popular (Yasuda and Ohkura, 2011). In traditional

multi-agent systems, robots communicate with each

other to achieve cooperative behaviors. There are

three major advantages of multi-robot systems over

single robot systems (Stone and Veloso, 2000; Yasuda

and Ohkura, 2005). The ﬁrst is parallelism; a task can

be achieved by autonomous and asynchronous robots

in a system. The second is robustness; it is real-

ized through redundancy. The system can have more

robots than required for a certain task. The third is

scalability; a robot can be added to or removed from

the system easily.

Making multiple robots cooperatively carry and

push common objects has been intensively studied

and yet to established the standard way. Many re-

search projects have dealt with this topic but few of

them have demonstrated on physical multi-robot sys-

tems. One of the most demonstrated tasks involving

cooperative transport is the pushing objects by multi-

ple robot teams (Rus et al., 1995; Stilwell and Bay,

1993). This task is inherently easy to accomplish

when comparing to carrying tasks, because a carry-

ing task involves multiple robots’ gripping a common

object and navigating to a destination in a coordinated

fashion (Khatib et al., 1996; Wang et al., 2000).

One of the most famous transportation problems

is the box-pushing problem (Mataric et al., 1995).

The problem is deﬁned in (Gerkey and Mataric, 2002)

and consists of cooperatively moving a box, which

is relatively large when compared to the size of the

multi-robots, from an initial position to a destination

using robots that can only perform pushing move-

ments. Then the fundamental research problem is to

design the appropriate control mechanism in order to

achieve the desired global behavior by the multiple

robots’ cooperation. This problem is known as NP-

hard (Reif, 1979), and many research efforts are fo-

cused into planning. Our research objective is saving

energy by much rougher behaviors of multiple robots

but more intelligent behaviors through multiple mo-

bile software agents.

3 ROBOT CONTROLLER

AGENTS

The target recovery process consists of two tasks; one

is the task for searching targets, and the other is for

transporting them to collection areas. They are per-

formed sequentially and exclusively. In our system,

these tasks are allocated to different software mo-

bile agents. They are Searcher agent and Transporter

agent, respectively. In this section, we describe details

of each mobile agent, and show how they contribute

to suppressing energy consumption in a multi-robots

system.

3.1 Mobile Agents for Searching

Considering how to make multi-robots search a tar-

get. The simplest solution would be to make all robots

move in order to search for the target. It means, how-

ever, only one robot that ultimately ﬁnds the target

do the job, and the other robots waste their energy in

vain. In addition, even if one ﬁnds the target, the oth-

ers may not be able to know the fact because of being

out of the range where communications through wire-

less LAN are available. As the result, they may move

around until they are exhausted to move.

We have proposed the mobile agent approach for

searching targets and showed its effectiveness in the

previous paper (Abe et al., 2011). The Searcher agent

visits each robot through migration. When the agent

reaches a robot, it checks the camera on the robot in

order to ﬁnd whether it is close to the target or not,

where it would have to spin within 360 degrees to

check all around it if the robot has no special cam-

era such as an omnidirectional sensor. It repeats this

process until it reaches the robot closest to the target,

and ﬁnally, it drives that robot to capture the target.

The searching strategy suppresses rotating wheels,

and therefore, contributes to saving energy consump-

tion. We have also adopted the same approach to

the target recovery process. The only difference is

that the recovery agent has a vector value that points

to the collection area as an interior datum, which is

ICAART2013-InternationalConferenceonAgentsandArtificialIntelligence

220

subsequently used to guide the agent for transporting

the target. We assume that the Searcher agent know

where the collection area is. The simplest implemen-

tation would be to make Searcher agents born at their

own collection areas. Each Searcher agent has a vec-

tor to keep track of its collection area. In the search-

ing process, each Searcher agent’s vector value is dy-

namically modiﬁed so as to pointing to the destina-

tion. When the robot, which is driven by the Searcher

agent, physically moves, the vector value is adjusted

according to the physical motion. Also whenever the

Searcher agent migrates to another robot, the vector

value has to be modiﬁed with respect to the new po-

sition. The modiﬁcation is achieved by synthesizing

the vector value at the previous robot and the vector

value from the source to destination in the migration.

To achieve this synthesis, it is required that each robot

can locate each other.

3.2 Mobile Agents for Transporting

Once the Searcher agent ﬁnds the target object, the

agent has to transport the object to the collection area.

In this subsection we describe a new transporting al-

gorithm that saves energy consumption using a soft-

ware mobile agent. The basic idea is to make the robot

throw the target in the direction of the destination in-

stead of carrying it, where the direction of the desti-

nation is passed from Searcher agent as vector value.

In the simplest case, the collection area would corre-

spond to the location where the Searcher agent was

generated as mentioned above. Assuming such vector

value, all that robots should do is only to check the

directional criteria shared with the other robots. Such

criteria could be given by a simple device such as a

compass. The algorithm for the Transporter agent is

as follows: 1) Transporter makes the robot pick up the

target, and then, 2) throwsthe target to the destination.

3) If there is not any other robot in the direction of the

destination, Transporter agent makes the robot carry

it by itself.

In the second step, there are two strategies of

ThrowStraight and DirectPass as a manner where sev-

eral robots transport the target to the collection area

through cooperatively passing it.

3.2.1 ThrowStraight Strategy

The simplest strategy for passing a target is that the

agent makes the robot throw the target to the des-

tination as further as possible, and then, makes an-

other robot that is closest to the thrown target pick it

up. Once such a closest-to-the-target robot is found,

ThrowStraight agent migrates to that robot to receive

the target. This behavior makes the newly arrived

robot approach to the target in order to pick up and

throw the target again. The ThrowStraight agent can

transport the target to the destination by repeating

these steps.

On the other hand, if ThrowStraight agent cannot

ﬁnd any robot close to the target, the robot has to carry

the target by itself. ThrowStraight agent throws the

target to the destination regardless whether the closest

robot is found or not. After that, if such a closest-to-

the-target robot cannot be found, all what the agent

should do is to make the robot approach to the tar-

get along thrown path. Notice here that the behaviors

of throwing and approaching correspond to carrying

the target with it to the location where the target is

thrown.

robot 1

robot 2

mobile agent

(a) Throwing a target to the des-

tination.

(b) Calculating the difference be-

tween the directions of another

robot and the destination.

tween the directions of the previ-

ous robot and the target at the cur-

rent robot after a migration.

(d) Approaching to the target.

(e) Facing with the destination.

Figure 1: The migration steps of ThrowStraight.

These steps of the ThrowStraight strategy seem to

work well if the direction to the destination, i.e. a col-

lection area, is always known. Initially, the direction

information is given to the ThrowStraight agent by

Searcher agent. Notice here that though the Searcher

agent has vector value to the destination, the Throw-

Straight agent holds only the angle datum of the vec-

tor value. After that, the angle information is modi-

ﬁed according to physical motions of the robot, and

recalculated through every migration. The new angle

value after the migration can be gotten by calculat-

SuppressingEnergyConsumptionofTransportationRobotsusingMobileAgents

221

ing each angle of a triangle consisting of the current

robot, the next robot, and the target in order. Fig. 1

demonstrates the steps of the process, where a star,

multi-circles, a circle with a face, and circles with a

slit respectively represent a target, a destination, an

agent, and robots. The slits of circles show the di-

rection with which each robot faces. The steps are as

follows:

1. The agent makes the robot throw the target to the

destination as shown by Fig.1(a),

2. The agent calculates difference α between the di-

rections of the other robot and the destination as

shown by Fig.1(b).

3. The agent migrates to the next robot (robot 2).

4. The agent calculates difference β between the di-

rections of the previous robot (robot 1) and the

target as shown by Fig.1(c).

5. The agent makes the robot approach to the target

and picks it up as shown by Fig.1(d).

6. The agent makes the robot face to the destination

as shown by Fig.1(e), where the angle to be ori-

ented, θ is given by the calculation 180− (α+ β).

Notice that the thrown point in the ﬁrst step has to

be within the view range of the robot. Otherwise, the

target cannot be traced in the following process.

Summarizing a sequence of behaviors of Throw-

Straight agent, an agent performs the following steps

1-5 until ﬁnding the speciﬁc destination, and then the

agent makes the robot throw a target to the destination

once it ﬁnds the destination: 1) The ThrowStraight

agent drives the robot forward until ﬁnding out other

robots within the angle of 120 degrees in the view

range, 2) the agent makes the robot throw the target

to the destination, 3) the agent migrates to the closest

robot to the target, 4) the agent drives the robot, to

which the agent has migrated, toward the target and

makes the robot pick it up, and 5) the agent calculates

the modiﬁed direction and makes the robot orient to-

ward the destination.

3.2.2 DirectPass Strategy

More smart strategy, which we call DirectPass strat-

egy, is to directly pass a target to another robot closer

to a destination. In more detail, the thrower passes

the target to the receiver only if the receiver is within

120 degrees range of the direction from thrower to

the destination. In this strategy, the step for searching

the next robot in ThrowStraight strategy is not nec-

essary because the current robot throw the target to

the next robot, instead of throwing to the direction of

the destination. What DirectPass agent does is simply

migrating to the receiver.

(a)



(b)

Figure 2: Passing process of DirectPass agent.

Thus, DirectPass strategy seems to be simpler and

more direct than ThrowStraight strategy. In order to

keep the direction to the destination, however, the

agent has to have complex data such as a vector value,

though the ThrowStrainght agent needs to have only

the angle as an interior datum. The vector value has to

be not only modiﬁed according to the physical motion

of a robot, but also recalculated through migrations.

Fig. 2 shows the process of calculating new vector

value after a migration. The steps are as follows:

1. The agent calculates vector value v

from the

current robot to the next robot as shown by

Fig.2(a), where the initial vector value is passed

by Searcher agent.

2. The agent modiﬁes the interior datum v

with new

vector value given by calculating v

− v

3. The agent migrates to the next robot as shown in

Fig.2(b).

Summarizing a sequence of behaviors of Direct-

Pass agent, an agent performs the following steps 1-

5 until ﬁnding the speciﬁc destination, and then the

agent makes the robot throw a target to the destina-

tion once it ﬁnds the destination: 1) The DirectPass

agent drives the robot forward until ﬁnding out other

robots within angle of 120 degrees in the view range.

2) the agent makes the robot throw the target to the

receiver, 3) the agent calculates new vector value as

interior datum, 4) the agent migrates to the receiver

robot, and 5) the agent makes the robot pick up the

target and orient toward the destination.

4 EXPERIMENTAL RESULTS

In order to demonstrate the effectiveness of our sys-

tem, we have conducted numerical experiments on the

example of target transportation that we have just dis-

cussed in the previous section. We have implemented

the mobile agent system simulator as shown in Fig. 3.

The simulator is based on hierarchical mobile agent

(Kambayashi and Takimoto, 2005; Satoh, 2000), of

which migration manner is based on the strong migra-

tion model (Cugola et al., 1997). We have evaluated

our two strategies i.e. ThrowStraight and DirectPass,

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222

0.4

0.6

0.8

The ratio of

moving distance / linear distance

R=50

R=100

R=150

R=200

0.2

moving distance / linear distance

The number of robots

R=200

R=250

(a) Results for ThrowStraightAgent

0.4

0.6

0.8

The ratio of

moving distance / linear distance

R=50

R=100

R=150

R=200

0.2

moving distance / linear distance

The number of robots

R=200

R=250

(b) Results for DirectPassAgent

Figure 4: The inﬂuences of the number of robots to moving distance.

(a) A snapshot of a running

simulator with a single target.

(b) A snapshot of a running

simulator with multi robots and

multi targets.

Figure 3: A snapshot of a running simulator.

in terms of moving distance and how robots are lo-

cated after the target is collected on the simulator. We

assume that energy consumption of a robot is propor-

tional to the moving distance because most energy is

consumed for rotating wheels.

4.1 Energy Consumption

First, we have conducted several experiments on the

sequential process of searching and transporting of a

target. We have repeated a number of the same pro-

cesses with varying the number of targets and the ra-

dius of the view range of each robot. Figs.4(a) and (b)

show ratio of the total moving distance in the trans-

portation process to the direct distance from the start

point of the transportation to the destination. The ﬁve

line charts represent the results for different radiuses

of the view ranges: 50, 100, 150, 200 and 250, respec-

tively. Each chart consists of results for 5, 10, 15, 20

and 25 robots. As shown by the shapes of the charts,

the more the number of the robot and/or the radius

of the view range increased, the more the total mov-

ing distance decreased in both cases of ThrowStraight

and DirectPass strategies.

4.2 Arrangements of Robots

In both of our transporting approaches, the more

widely robots are scattered, the more advantages they

seem to have. In order to conﬁrm this observation,

we have evaluated the area size covered by the view

ranges of all robots during the experiments as a crite-

rion of the way in which robots are scattered. In the

experiments, we assumed several targets, and sequen-

tial executions of searches and transportations such as

(a search agent) → TA

(a transport agent) → SA

→ TA

→ SA

→ TA

→ · · · , TA

created after SA

Table 1 shows the ratio of the area size covered

by the view ranges in DirectPass strategy to that in

ThrowStraightAgent, for 5, 15 and 25 robots, for 10,

30 and 50 targets and for 50, 150 and 250 view dis-

tance respectively. Though DirectPass strategy are

more widely scattered than ThrowStraightAgent, the

difference between them is negligible. In other words,

the result shows that our approaches have little differ-

ence in inﬂuence on other tasks of the multi-robots.

5 CONCLUSIONS

We have implemented a multi-robot system that trans-

ports a target to a destination while suppressing en-

ergy consumption, by adding transport facility to the

previous multi-robot system for searching (Abe et al.,

2011). We have proposed two strategies Throw-

Straight and DirectPass as transport facility. Both

of them can suppress more energy consumption than

carrying directly to destination, but DirectPass has re-

markable advantages. DirectPass, however, requires

more hardware equipment than ThrowStraight. Di-

rectPass has to get both the directional angle and the

distance toward other robots through robot’s sensor

SuppressingEnergyConsumptionofTransportationRobotsusingMobileAgents

223

Table 1: Ratio sum of each robot’s view area: DirectPass / ThrowStraight.

target = 10 target = 30 target = 50

View distance The number of robots

5 15 25 5 15 25 5 15 25

R = 50 1.055 0.997 1.007 1.005 1.005 1.018 1.023 1.045 1.040

R = 150 1.056 1.041 1.016 1.064 1.094 1.049 1.094 1.114 1.094

R = 250 1.030 1.034 1.014 1.099 1.092 1.048 1.100 1.142 1.100

devices. ThrowStraight needs only the directional an-

gle. Thus, when implementing the multi-robots sys-

tem based on our approach, it would be necessary

to decide which strategy should be adopted based on

quality of the sensors equipped with the robots.

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