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sensors these sensors uses interfaces which are specific to
the type of information they are interested in, e.g. a state
change in the component. The information provided by these
sensors on the diagnosis port can afterward be used by
a monitor. The monitor allows to view the sensing result
and thus show which faults are present in the system. This
contrasts with the method we propose in this paper as we
use the information provided by the observer to calculate
a diagnosis. Additionally, our observers allow checking for
properties which need to hold between different components,
e.g. the movement measured by the wheel encoder and by
the IMU.
Another method to observe a robotic system was proposed
in [12]. Each module in the system is accompanied with
a detection module which checks if the module works as
expected. This check is performed with the help of a residual
calculation. If the residual is not zero a fault is detected. All
the detected faults are gathered in a fault signature and used
for fault identification. This identification is performed with
the help of an incidence matrix. The matrix describes in a
static manner which fault causes which observations. This
contrasts with our approach as we do not assume that we
can simply enumerate all possible observation and faults in
a matrix. To react to a fault, the method presented in [12]
reacts on the high-level which uses defined recovery actions,
which are chosen per the severity of the fault. This is like
our approach which use a simple rule engine to perform a
reaction but delegates the fault handling to more complex
reasoning whereas the rule engine allows a fast reaction.
A method which uses a rule system for observations was
presented in [13]. The system defines safety rules which
are checked during runtime. To define this rules a domain
specific languages is used which allows defining conditions
for the rules and which actions to trigger if a condition holds.
The rules use information which is provided on different
topics to define a safety rule. The actions are afterward
executed on the robotic hardware and can be defined in the
framework separately. The main difference to our system is
that we separate the detection and the reaction to a fault. This
allows us to use several observations to determine which
component is faulty and afterward react depending on the
faulty component.
As we have briefly outlined above our method is based
on the method presented in [3]. The method presented in [3]
also uses observers to detect a fault and a diagnosis engine to
identify the fault component. Additionally, a planning system
is used to repair if a fault is detected. Instead of using a
planning system to find a proper repair we use a simple rule
engine to allow the robot a fast reaction but also restricts
the possible repairs which can be performed. To allow a
fast reaction and a proper repair one can combine both
methods and first react with the rule engine and afterward
trigger a planning step for a proper repair. The other dif-
ference between the method presented in this paper and the
method presented in [3] is the underlying implementation.
The underlying implementation presented in this paper use
plugin-based observers which are more efficient than the implementation of the observers used in [3].
VIII. CONCLUSION AND FUTURE WORK
Autonomous robots perform tasks in a (partly) unknown
environment. This is done by using several complex software
and hardware components. These components need to proper
function and properly interact with each other to allow
the robot to achieve its task. Due to the complexity of
the components and the (partly), unknown environment one
cannot expect that the robot will perform its task without
a fault. Instead one needs to address the problem of fault
occurrence in the robotic system.
In this paper, we presented a model based approach which
allows that the robot detects and identifies a fault. This
is achieved by observing the communication between the
components and checking this communication for specific
properties. These properties are derived from the system
and specify the proper function of the system. If a property
indicates a fault a diagnosis engine is used to determine the
minimal set of components which is faulty. Using the result
of this diagnosis engine a simple rule engine can be used
to allow the robot to react to a fault. This reaction can be
used to repair the fault or to bring the robot in a safe state
to perform a more complex repair action.
The current approach uses static properties of the system
to determine if a fault has occurred. It is left for future work
to extend this approach to also consider dynamic changes of
the properties. This would allow to detect a malfunction in
the dynamic behavior of the system as well as to determine
a malfunction of a component which changes its static
behavior per a defined system state.
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14
Proceedings of the OAGM&ARW Joint Workshop
Vision, Automation and Robotics
- Titel
- Proceedings of the OAGM&ARW Joint Workshop
- Untertitel
- Vision, Automation and Robotics
- Autoren
- Peter M. Roth
- Markus Vincze
- Wilfried Kubinger
- Andreas MĂĽller
- Bernhard Blaschitz
- Svorad Stolc
- Verlag
- Verlag der Technischen Universität Graz
- Ort
- Wien
- Datum
- 2017
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-85125-524-9
- Abmessungen
- 21.0 x 29.7 cm
- Seiten
- 188
- Schlagwörter
- Tagungsband
- Kategorien
- International
- Tagungsbände