SWITCH! Dynamic Roles Exchange Among

Cooperative Robots

⋆

Carlos E. Ag

uero, Vicente Matell

an, Jos

e M. Ca

nas and V

ıctor M. G

omez

Universidad Rey Juan Carlos

Tulip

an s/n, 28933, M

ostoles (Madrid) - Spain

Abstract. In this article we propose a dynamic role exchange algorithm for mo-

bile robots. The heuristics used to alternate among the different roles is based

on utility functions. We have utilize the Sony Aibo ERS-7 for testing our design

and implementation. The RoboCup 4-legged competition environment has been

chosen for proving our work.

1 Introduction

RoboCup is a very challenging and motivating robotic competition where under a sim-

ple soccer match, are hidden lots of research in several areas.

In this paper we will focus on the 4-legged league [1]. Teams in this league are

made by four Aibo robots [2]. In this competition, communication among robots is

allowed (since 2002 edition held in Fukuoka, Japan). Cooperative behaviors can beneﬁt

from communications in many ways: Improving accuracy to locate objects by adding a

global shared model [3], to allocate dynamically player roles [4].

Dynamic multi-robot coordination had already been explored by other teams. Early

work on cooperation and coordinated positioning in RoboCup was proposed in [5].

Cooperation among heterogeneous robots has been explored in [6]. In [7] the ball posi-

tion is determined by a probabilistic integration of all ball perceptions coming from the

players. [8] suggested a potential ﬁeld approach for managing robots constructed with

shared information. Newcastle university team [9], ﬁnalist in Osaka 2005, shares ball

location helping robots to ﬁnd the ball quickly.

In this paper we present our work in this area focusing on task allocation. Dynamic

task allocation allows a team to divide its main objective in a couple of sub-objectives

more specialized and adapted to the location of each of the teammates and the strategy

of the team. Section 2, 3 and 4 details our contribution to role allocation problem and

strategy decisions for roles in RoboCup. Section 5 analyzes experiments and results

we have got. Finally, section 6 summarizes our conclusions using coordination in robot

teams and using our algorithms.

⋆

This research has been partially sponsored by grants DPI2004-07993-C03-01, DPI2004-

07993-C03-02 and S-0505/DPI/0176 corresponding to Ditpa and Robocity 2030 projects re-

spectively

E. Agüero C., Matellán V., M. Canas J. and M. Gómez V. (2006).

SWITCH! Dynamic Roles Exchange Among Cooperative Robots.

In Proceedings of the 2nd International Workshop on Multi-Agent Robotic Systems, pages 99-105

DOI: 10.5220/0001224900990105

 SciTePress

2 Roles Speciﬁcation

Several teams [10], [9] attending RoboCup events in the last years have used a set of

roles assigned to each robot instead of using a single role for all players of the team.

The use of a collection of roles allows a team to organize more easily all responsibilities

involved in the task to resolve.

The roles can be assigned to each robot in an statical or in a dynamical way. Dy-

namic role allocation beneﬁts, from example, from opportunistic situations like fast ball

changes along the ﬁeld, penalized players

, or failures in some robot.

We have considered four main roles in the RoboCup domain: Goal-keeper, Striker,

Defender, and Supporter. Goal-keeper is the only role assigned statically. The main

reason to have a single player to the Goal-keeper role is that the rules do not allow

that players enter in its own goal area (like in the hand ball). The rest of roles can be

exchanged among robots according to game conditions.

Next, we are going to describe the objectives of each role and the advantages we

have obtained using dynamic role allocation:

– Goal-keeper: Its goal is to protect its own goal from shots by the other team players.

Also, it should rest in its own area.

– Striker: It tries to get the ball and to carry it, or to kick it, towards the net. When

the other team have got the ball, it tries to recover it actively (going after the ball).

None of the other roles are devoted to get the ball. This approach has one implicit

advantage: It avoids pushing among players of our team, that is explicitly penalized

in the rules. If a team does not have a ball booking mechanism [11] or similar, all

robots will ﬁnish bumping into each other, and its players will be penalized.

– Defender: Its goal is to intercept the ball if an opponent kicks it to its net. Fur-

thermore, it should stand in the way of the opponent and should try to hide the net

preventing the opponent to kick the ball.

Another implicit consequence of the Defender role is that one robot of the team

always remains in a position near its own net. This fact is very useful taking into

account that the ball quickly moves from side to side. We have always one robot

covering its defending half of the ﬁeld.

– Supporter: The function of this role is to assist the striker in its path, and to cover

the maximum amount of ﬁeld in case the ball will be kicked in the wrong way.

The main contributions of this role are to recover the ball if the kicks made by the

striker do not go in the good direction, and also to maintain a good position for

future passing kicks.

3 Utility and Heuristic Functions

In this section we are going to introduce some concepts needed to explain how the roles

are dynamically assigned. Our role allocation algorithm will be based in heuristic func-

tions. Those functions evaluates some parameters like the ball distance, localization,

Penalized players are removed from the ﬁeld for 30”

100

etc. and obtains a value for each role. We will call this value utility. Utilities will be cal-

culated periodically and roles will be assigned to robots in a priorized way according to

the values obtained.

A general deﬁnition for utility is “value to estimate the cost of executing an action”.

In our approach, utility is employed to evaluate the degree of adaptation of one role to

one robot in a particular game conditions.

In our proposal utilities will be individually computed by each robot as the weighted

sum of several factors: Distances to ball, nets, etc. In order to describe those factors, we

will use the formalism described in [4]:

– Let I

, ..., I

be the set of n robots.

– Let J

, ..., J

be the set of n priorized roles and w

, ..., w

their relative weights.

– Let U

the nonnegative utility of robot I

for role J

, 1 ≤ i, j ≤ n.

1. Update Utilities

2. Look for robot with larger utility for Striker role

3. Look for robot with larger utility for Defender role

4. Look for robot with larger utility for Supporter role

Update Utilities step computes a matrix with all combinations among robots and

roles according to the heuristic described below. The next steps selects the more suitable

robot for the Striker, Defender and Supporter roles in this order.

The heuristic functions used to allocate the roles developed are the following:

– U

i,Striker

= D

,Ball

+ (θ

−Net

− θ

Ball−Net

) + REC

– U

i,Def ender

= D

,Own

net

+ REC

– U

i,Supporter

= D

,Opp

net

+ REC

,Ball

, D

,Own

net

and D

,Opp net

are the distances to the ball, own net, and

opponent net. θ

−Net

is the difference in orientation between the local orientation of

the robot and the orientation needed for focusing the net. θ

Ball−N et

is the angle between

the ball and the opponent net. REC (Role Exchange Cost) is also added to prevent

excessive roles exchanges if only small changes have happened in the environment.

This factor provides some kind of hysteresis to the system.

Next are the equations to select the appropriate robot for each role. In this paper we

suppose that the order in roles assignment is Striker,Defender and Supporter.

1. Utility

striker

= min(U

i,striker

) , ∀i ∈ (1..n)

2. Utility

def ender

= min(U

i,def ender

) , ∀i ∈ (1..n) ∧ i 6= Robot

striker

3. Utility

supporter

= min(U

i,supporter

) , ∀i ∈ (1..n) ∧ i 6= Robot

striker

, Robot

def ender

Every robot updates its utilities periodically, and broadcasts this information to its

teammates. We will refer to this information as coordination information. The informa-

tion sent is its own location, and an estimation of its distance to the ball. Coordination

information is sent at 5Hz.

When one robot receives coordination information it updates data associated to the

corresponding robot in its global model. This global model stores position of the team-

mates and, combined with the position of the ball is used to calculate utilities functions.

101

4 Robot Positioning

In the previous section we have explained our mechanism for selecting roles according

to the game situations. Other interesting issue is what should a robot do if it is running

Defender or Supporter Role (Striker always goes for the ball). In our proposal, Defender

and Supporter go to a speciﬁc position. In this section we describe how the players

calculate their “ideal position” according to the roles they have been assigned.

Fig.1. Detailed points of interest calculating ideal posititions for roles.

4.1 Defender Positioning

Defender should stay in a position near its own net in order to defend its goal. We have

developed an approach that places Defender between the ball and the middle of its own

net. If an opponent hits the ball and the ball travels in the net direction, the defender

must be in its trajectory.

angle = arctan(ownN et

− ball

, ownN et

− ball

)

Ax = −cos(angle) ∗ areaCorner

Ay =

F ieldLength

− sin(angle) ∗ areaCorner

ball2aproxArea = distance(A, Ball)

Illegal defense must be taken into account when the team is defending. This penalty

is applied when a defending robot goes into its own area. In order to prevent our De-

fender from entering in its own area, we have approximated the area penalty to a semi-

circumference.

The Defender ideal position will be the center of the segment delimited by the ball

and point A (see ﬁgure 1 left). This position (DEF

,DEF

) is calculated using the next

equations:

102

DEF

= −cos(ang) ∗ (areaCorn +

ball2Area

)

DEF

F ldLgth

− sin(ang) ∗ (areaCorn +

ball2Area

)

4.2 Supporter Positioning

Ideal position is the middle point of the quadrant delimited by the ball and the fairest

corner of the opponent side (as shown in ﬁgure 1 right) where we can see this quadrant).

This can be calculated as:

lateral

= Max(dist(ball

, lat

lef t

), dist(ball

, lat

right

))

goalLine

F ieldLength

+ ball

SUP P ORT

= ball

lateral

SUP P ORT

= ball

−

goalLine

This position guarantees that Supporter will never stand in the way of striker and

the opponent net. Its goal is to support the striker and, in case a kick does not go in the

right direction, to reduce the time to go for the ball.

5 Experiments

TeamChaos [12] code is composed by the source code of the program that runs into the

robots and a suite of tools. One of this tools is the team simulator. We have tested what

are the differences if our team uses Switch coordination or not.

5.1 Playing with Switch Coordination in Simulator

Using dynamic role allocation, experiments show that the game is more dynamic. The

main reason is that we give the biggest priority to take the ball. In simulator we have

seen that always the better placed robot wants to go for the ball. If we drag the ball to

different positions on the ﬁeld, role reallocation acts quickly and robots change their

positions. In consequence, the ball is take it more quickly than experiments without

Switch coordination.

In order to test the real advantages of the dynamic role allocation, we have made six

matches in our simulator, three of them with coordination and the others without switch.

During the simulation there are not any opponents and each match has 10 minutes.

103

Table 1. Results of matches without opponents.

Match # Goles with Switch Goles w/out Switch

1st 3 2

2nd 4 3

3th 4 3

Every time a player scores, the ball is taken to the middle of the ﬁeld. In table 1 we can

see the results of the simulations.

Non cooperative team has a clear problem: All players go to the ball. There are not

any advantage playing with one player or playing with three players. In this case, more

players involve more obstructs between them.

Coordinated team offers a very more ordered play with the three attacker players

spreaded on the ﬁeld. With less robots standing in the way of others, there are more

time to carry the ball inside the net and if the pushing rule would be applied they would

have been removed from the ﬁeld.

Although there is not a very large improvement in goals, the overall behavior of the

team is much more organized. When the code of our Striker will be more tuned, we

hope the results with and without coordination will drastically emerge.

5.2 Playing with Switch Coordination with Real Robots

We have also tested Switch coordination in real robots. We have used a team of three

Sony AIBO ERS-7 using the framework of the TeamChaos RoboCup team [12]. First,

we have tested the operation of the assigning protocol for the striker role.

We have hold down the three robots in boxes. We want to show that only the robot

with best utility and only one goes for the ball. If we put the ball in front of one of the

robots, only the closest robot goes for it. If we move the ball and put it close to another

player, this one takes the role of striker and it moves its legs for catching the ball. Note

that for this experiment, defender and supporter roles are running a still behavior. You

can see a movie for this experiment in http://gsyc.es/

caguero/Switch-Pre.avi.

6 Conclusions

We have developed a basic coordination mechanism among members of a multi-robot

team. Localization and local ball estimation are the elements shared. Combining peri-

odically the information received with local information, each robot updates a global

model of the environment. Using coordination we have also got a very good way to

identify the rest of the team members. We can not do previously because all teammates

look alike.

Using the global map, an strategy layer uses the shared information to allocate roles.

We have used a simulator to choose several heuristic functions and factors. We have

observed the behavior in opportunistic situations dragging the ball and releasing it in

104

other point. Robots quickly exchanges their roles according to the new scenario. How-

ever, when doing little changes in the environment, robots are able to cushion them

maintaining their roles and improving the dynamism of the system.

Finally, ideal positions for each role are one option chosen by our team that has

worked very well in our tests. Movement of the Defender robot is very smooth without

big jumps between positions, preventing this critical player from moving constantly.

Supporter robot is less critical so it moves more than Defender covering the attack-

ing ﬁeld. Tests have shown that if a kick fails, Supporter goes for the ball quicker than

previously implementations without coordination. Before using cooperation, the three

players were closer all together and now they are expanded in a more intelligent way

around the ﬁeld.

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