AUTONOMOUS MONITORING AND REACTION TO FAILURES

IN A TOPOLOGICAL NAVIGATION SYSTEM

V. Egido, R. Barber, M. J. L. Boada, M. A. Salichs

Robotics Lab. Carlos III University.Madrid, Spain

Keywords: Robot navigation; Monitoring; Supervision; Robot skills.

Abstract: In this paper a system for simultaneous navigation and monitoring with autonomous reaction to failures is

going to be presented. This system is part of a complete navigation system called AURON (Autonomous

Robot Navigation). The AURON System autonomy is based on the interaction of four main components:

the autonomous generation of an environment representation, the planning of a sequence of actions and

perceptions which guide the robot from an initial event to a final one, the navigation that converts sequences

in real movements and supervises all the process, and the relocalization that allows to place the robot again

in the representation. This system has been implemented in a mobile robot control architecture called AD.

AD is a two level architecture: deliberative and automatic. The paper is focused in one deliberative skill, the

navigation skill.

1 INTRODUCTION

Autonomous movement implies not only being able

to perform a priori established movements without

human help, but also to react to unexpected

situations, with the same ability. Reacting to

unexpected events is a skill related to intelligence.

To supervise, will then require to monitor the

system evolution and to choose the most suitable

action over the events that halt the plan execution.

As for the monitoring techniques, there is no

generally accepted definition of execution

monitoring (Fichtner, 2003). Giacomo et al in

(Giacomo, 1998) defined it as ‘the robot’s process of

observing the world for discrepancies between the

actual world and the robot’s internal representation

of it, and recovering from such discrepancies’.

The recovering techniques that Giacomo

describes are widely dependant on the navigation

system architecture being used. The purely reactive

systems, like the one develop by Brooks (Brooks,

1986), do not have and cannot contain recovering

techniques because its basis are the reaction to

events without any kind of deliberative capacity over

themselves.

On the other hand, within the architectures which

consider those techniques because they contain a

deliberative character, the works (Stuck, 1995),

(Alami, 1998), (Fernandez, 1998) and (Fichtner,

2003) are found. All of them have a level over the

purely reactive one, which allows supervising the

navigation process, specifying which types of

situations are found, trying to correct them,

recovering the system control and, if it is possible,

accomplishing the required task.

In this work, a navigation system which monitors

what is happening and reacts to changes, is

presented. This system is called AURON and has

been implemented in the hybrid architecture AD

(Barber, 2001).

2 THE AURON NAVIGATION

SYSTEM

AURON (Autonomous Robot Navigation) is

considered a complete navigation system formed by

the interaction of four main components. As it is

shown in figure 1, the components are the explorer,

the planner, the navigator and the relocalizator.

383

Egido V., Barber R., J. L. Boada M. and A. Salichs M. (2005).

AUTONOMOUS MONITORING AND REACTION TO FAILURES IN A TOPOLOGICAL NAVIGATION SYSTEM.

In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Robotics and Automation, pages 383-386

DOI: 10.5220/0001156603830386

 SciTePress

Figure 1: General structure of the AURON System

Each component has its own mission and

interacts with the others giving autonomy to the

robot. The explorer is in charge of generating

without human intervention the representation of the

environment navigating through unknown places

and obtaining information. The planner gives the

sequence of actions and detected events to go from

one place to another once the robot has its own

representation. The navigator converts the sequence

obtained by the planner in real movements and

supervises all the process and finally, the

relocalizator allows placing the robot again in the

representation of the environment once it is lost.

The environment representation is an essential part

of the system as it is used by all the other components.

This representation is called the Navigation Chart. The

Navigation Chart is a fundamentally topological

representation of the environment, which tries to

imitate the human navigation. It differs from other

representations (Beccari, 1997) in considering a

directed graph where arcs and nodes are equally

relevant. In this graph arcs are not unions without

information as in many other models and nodes have

parameters that allow dynamic planning mechanisms

different from previous developments.

The Navigation Chart is not formed by a

succession of places of the environment as in

(Remolina, 2004) but by a succession of elementary

skills (Egido, 2003). It is represented by a simple

directed parameterised graph G(v,e) formed by

nodes v and edges e. Nodes are events regarding

sensorial perceptions (be in front of a door) and

edges represent sensorimotor skills which lead the

robot to the next event (move towards a door). A

new situation is being described on the Navigation

Chart when detecting an event. The robot finds itself

in the specific situation or place in which a specific

sensorial event is sensed. An example of this

Navigation Chart and the importance of its edges and

nodes is shown in figure 2.

Figure 2: Example of Navigation Chart

3 SUPERVISION

Once the sensorimotor and sensorial event skills to

be detected are obtained, the skill carries out the

supervision of what is taking place while those skills

are activated or deactivated. To perform this

supervision, the information related to nodes and

edges is used. In this case, time and distance are

parameters which allow supervising the process in a

higher level, but also in some situations specific

elements associated with enabled skills and events

will be supervised.

Therefore, a two level supervision will be

considered: A general level which implies the

distance and time monitoring, which can be applied

to all skills, and a lower level that represents a

specific monitoring for specific events. This paper

will focus in the general monitoring level, which can

be applied to the skills used.

3.1 Distance monitoring

While the skills are enabled and disabled, the

process will be supervised comparing the distances

stored in the graph’s edges with the real travelled

distances. When there is a difference in percentage

between the distance travelled by the robot and the

one stored greater than a established value (for the

environment where the experiment test were carried

out, it has been empirically established that the

distance travelled will not overcome in a 10% the

one stored), the supervisor compares the actual

sensorimotor skill and the one after the node

detection having in mind two fundamental cases:

• The undetected event is not a decisive event

for the plan execution.

• The undetected event sets a change in the

execution plan and therefore it is relevant.

Exploration

Navigation

RelocalizationPlanning

Navigation Chart

Exploration

Navigation

RelocalizationPlanning

Navigation Chart

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In figure 4 it is shown how the plan could

continue if the event “Right door detected” failed to

go from node A to node B, hiding the edge that

implies a different action: “Cross door”.

Corridor

travelling

Corridor

travelling

Cross door

Left door

detected

Right door

detected

Right door

detected

Right door

detected

Hidden edge

Corridor

travelling

Corridor

travelling

Cross door

Left door

detected

Right door

detected

Right door

detected

Right door

detected

Hidden edge

Figure 4: Planning with detection problems

In the second case, the sensorimotor skill that

was being carried out and the next one are different.

The event detection is totally necessary to continue

with the plan. In figure 5 it would execute the plan

that goes from node A to node C. In this case, the

navigator hides the different outgoing edges of the

failed node and communicates to the main sequencer

the new situation carrying out a new plan.

This new plan, if possible, will give a new

subgraph with the edges and nodes sequence that

allows reaching the final node in the new situation.

Corridor

travelling

Corridor

travelling

Cross door

Left door

detected

Right door

detected

Right door

detected

Right door

detected

Hidden edge

Corridor

travelling

Right door

detected

ROBOT

LOST !

Corridor

travelling

Corridor

travelling

Cross door

Left door

detected

Right door

detected

Right door

detected

Right door

detected

Hidden edge

Corridor

travelling

Right door

detected

ROBOT

LOST !

Figure 5: Planning with detection problems

To finish this supervision method monitoring

distances, a last situation is considered. The robot

could not detect the new node from the alternative

plan. (Situation of 3 consecutive failures). In this

case, it cannot be assured that the robot is in the

place set by the Navigation Chart. If the robot is in

this situation it will try a new plan, but if it fails, the

navigator will indicate by an event that the robot is

definitely lost and that there are not enough probes

to consider that the robot is in the node set by the

Navigation Chart. In figure 5 it can be seen how

three consecutive event failures imply an event in

which the navigation skill shows that the robot is

lost.

3.2 Time monitoring

In all the cases described above, failures in events

are monitored, but the environment changes could

equally affect the sensorimotor skills. If the distance

travelled is not overcame and however the time is

overcame without detecting an expected event, then

this means that a sensorimotor skill execution

problem is found and it is halted in a Navigation

Chart node.

The Navigation Chart characteristics that are

being used, in most cases, make the Navigation

Chart a graph that contains associated symmetries.

An example of this is the fact that travelling the

corridor in one way has its equivalent on travelling

the corridor in the other way. A 180º robot turn

allows travelling the corridor in the other way and

finds an event that will allow localization, as it is

shown in figure 6.

1 8

C.T.

R.D.D.

E.C.

L.D.D.

180º

symmetry

E.C.

L.D.D.

E.C. End of Corridor C.T. Corridor Travelling.

R.D.D. Right Door Detected L.D.D. Left Door Detected

1 8

C.T.

R.D.D.

E.C.

L.D.D.

180º

symmetry

E.C.

L.D.D.

E.C. End of Corridor C.T. Corridor Travelling.

R.D.D. Right Door Detected L.D.D. Left Door Detected

Figure 6: Graph symmetries to apply the time supervisory

4 EXPERIMENTAL RESULTS

The experiments described in this section have been

tested in a B21 robot by RWI. All the system has

been implemented in C++ language, using the

system specified by CORBA which provides

interoperability between objects in a heterogeneous,

distributed environment.

To test the navigation system, a mission was

requested to the robot. The robot started at the

beginning of the corridor and it should access to C12

lab, as it is shown in figure 7.

Figure 7: Experimental environment

C12

C13

C2 C3 C4 C5 C8C7C6 C9

C10 C11

C12

C13

C2 C3 C4 C5 C8C7C6 C9

C10 C11

AUTONOMOUS MONITORING AND REACTION TO FAILURES IN A TOPOLOGICAL NAVIGATION SYSTEM

385

As an experiment for the monitoring and

supervision system the C12 lab door was

momentarily closed when the robot was supposed to

detect it. The second problem appeared in the

corridor, the corridor was blocked not letting the

robot continue its plan, as it is shown in figure 7.

These were the succession of steps that the

system applied without any human intervention.

While monitoring distance and time, the distance

stored in the Navigation Chart indicated that there

was a problem and the robot changed the first plan.

In figure 8, it is shown the navigation interface that

represents the Navigation Chart on the left and the

plan obtained from the planner on the right. A

failure in the node that implies detecting the door

has happened.

Figure 8: Navigation Chart when an error was detected

The navigation skill notified the problem and the

planner generated a new plan solving the problem.

The navigation skill took the new plan and began the

sequence of movements, monitoring distance and

time again. The new plan is shown in figure 9.

Figure 9: New plan generated and new failure detected

Then, because of the obstacle in the middle of

the corridor, the time stored in the Navigation Chart

indicated that there was a problem and the robot

changed the plan again. In this case, the robot turned

180º placing itself in the symmetrical edge and the

robot set a new plan. As the door of lab C12 was

opened during the explained process then it could

finally go into the lab without any problem.

ACKNOWLEDGMENT

The authors gratefully acknowledge the funds

provided by the Spanish Government through the

MCYT projects TAP1999-214 and DPI2002-00188.

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