![](bg4.png)
Action guidelines:
0 :→ A
1 :→ A
′
→ B
2 :→ A
′
→ B
′
→ tR1||5, 6, 7, 8 → tR2
′
→ tR3
′
→ D
3 :→ A
′
→ B
′
→ tR1
′
→ tR2||5, 6, 7, 8, 2 → tR2
′
→ D
′
→ tR4||4, 5, 6, 7, 8 → v
l,2
4 :→ A
′
→ B
′
→ tR1
′
→ tR2
′
→ tR3||5, 6, 7, 8, 2, 3→ D
′
→ tR4
′
→ v
′
l,2
→ tR4||5, 6, 7, 8→ D
′
→ v
l,1
5 :→ A
′
→ B
′
→ tR1
′
→ tR2
′
→ tR3
′
→ D
′
→ tR4
′
→ v
l,2
→ tR4
′
→ D
′
→ v
′
l,1
→ D
′
→ tR3||8 → tR2
′
→ tR1
′
→ B
′
→ tR5||6, 7, 8 → v
l,3
6 :→ A
′
→ B
′
→ tR1
′
→ tR2
′
→ tR3
′
→ D
′
→ tR4
′
→ v
′
l,2
→ tR4
′
→ D
′
→ v
′
l,1
→ D
′
→ tR3
′
→ tR2||8, 5 → tR1
′
→ B
′
→ tR5
′
→ v
′
l,3
→ tR5||7, 8 → B
′
→ A
′
→ tR6||7, 8 → C
7 :→ A
′
→ B
′
→ tR1
′
→ tR2
′
→ tR3
′
→ D
′
→ tR4
′
→ v
′
l,2
→ tR4
′
→ D
′
→ v
′
l,1
→ D
′
→ tR3
′
→ tR2
′
→ tR1||8, 5, 6 → B
′
→ tR5
′
→ v
′
l,3
→ tR5
′
→ B
′
→ A
′
→ tR1
′
→ C
′
→ tR7||8 → E
8 :→ A
′
→ B
′
→ tR1
′
→ tR2
′
→ tR3
′
→ D
′
→ tR4
′
→ v
′
l,2
→ tR4
′
→ D
′
→ v
′
l,1
→ D
′
→ tR3
′
→ tR2
′
→ tR1
′
→ B
′
→ tR5
′
→ v
′
l,3
→ tR5
′
→ B
′
→ A
′
→ tR6
′
→ C
′
→ tR7
′
→ E
′
→ v
l,4
Figure 3: Resulting action guidelines from the navigation tree in figure 4.
0 1
3 1 1
0
10
v v
v
l,1
l,2
l,3
v
l,4
A
B C
D E
Figure 4: Example navigation tree.
straint is never violated. It also does no harm because,
if the robot for that node is already there, then this
command has no effect.
We also have to deal with temporary relay posi-
tions, which we name tR
x
. While crossing edges with
a weight, a defined number of robots is needed. They
are placed on thetR
x
nodes and have have to stay there
until the whole group reaches the next non-tR
x
node.
After that the temporary robots can also go forward.
The robot farthest away from the position of the group
has to move first, then the second farthest, and so on.
Up to this point we have moved all robots from
the beginning in one group. In most cases this is
not necessary. Thus, we propose an optimizing step
which can be done after calculating the action guide-
lines for the robot group. This optimization uses the
fact that a robot which passes a node v
i
in the di-
rection of the leafs and back without getting at least
one ”||” command in between is not necessary. So all
the commands between two appearances of v
i
can be
removed. However, this remove operation has some
side effects: As the waiting commands ”||” are always
related to the whole robot group, a robot whose action
guideline was partly removed has to be removed from
the waiting lists of the other robots. A resulting plan
transformation can be seen in figure 3.
4 CONCLUSIONS
In this paper we address the problem of transforming
a constrained global multi-robot plan into plans for
each single robot in that multi-robot system. There-
fore, we introduce navigation trees to determine the
minimal number or robots for a general global plan.
This results not only in the minimal number of robots
but also in a visiting sequence for the target positions.
Although the combined approaches, building a global
plan with our planning approach together with the
automatic translation to robot action guidelines are
tested on real robots (on a MRS with up to 6 robots)
an exhaustive evaluation of the performance is nec-
essary. Especially the online optimization via inter-
changing guidelines is a focus of future work.
REFERENCES
Alami, R. and da Costa Bothelho, S. (2002). Plan-based
multi-robot cooperation. In Beetz, M., Hertzberg, J.,
Ghallab, M., and Pollack, M., editors, Advances in
Plan-Based Control of Robotic Agents, volume 2466
of Lecture Notes in Computer Science, pages 65–95.
Springer Berlin / Heidelberg. 10.1007/3-540-37724-
7 1.
Alami, R., Fleury, S., Herrb, M., Ingrand, F., and Robert, F.
(1998). Multi-robot cooperation in the martha project.
Robotics Automation Magazine, IEEE, 5(1):36–47.
Br¨uggemann, B. and Schulz, D. (2010). Coordinated navi-
gation of multi-robot systems with binary constraints.
In 2010 IEEE/RSJ International Conference on Intel-
ligent Robots and Systems. IEEE Computer Society.
Burgard, W., Moors, M., Fox, D., Simmons, R., and Thrun,
S. (2000). Collaborative multi-robot exploration. vol-
ume 1, pages 476–481 vol.1.
Kaminka, G., Erusalimchik, D., and Kraus, S. (2010).
Adaptive multi-robot coordination: A game-theoretic
perspective. In Robotics and Automation (ICRA),
2010 IEEE International Conference on, pages 328–
334. IEEE.
Parker, L. E. (2008). Distributed intelligence: Overview
of the field and its application in multi-robot systems.
Journal of Physical Agents, 2(1).
Ryan, M. (2010). Constraint-based multi-robot path plan-
ning. In IEEE International Conference on Robotics
and Automation.
Sanchez, G. and Latombe, J.-C. (2002). Using a prm
planner to compare centralized and decoupled plan-
ning for multi-robot systems. In Robotics and Au-
tomation, 2002. Proceedings. ICRA ’02. IEEE Inter-
national Conference on, volume 2, pages 2112–2119
vol.2.
ICINCO2012-9thInternationalConferenceonInformaticsinControl,AutomationandRobotics
422