=Paper=
{{Paper
|id=None
|storemode=property
|title=Supervised Task Performance of an Autonomous UAV Swarm, Supporting and Implementing Fire-Fighting Procedures
|pdfUrl=https://ceur-ws.org/Vol-918/111110446.pdf
|volume=Vol-918
|dblpUrl=https://dblp.org/rec/conf/at/HornauerFLS12
}}
==Supervised Task Performance of an Autonomous UAV Swarm, Supporting and Implementing Fire-Fighting Procedures==
https://ceur-ws.org/Vol-918/111110446.pdf
Supervised Task Performance of an Autonomous
UAV Swarm, Supporting and Implementing
Fire-Fighting Procedures?
Sascha Hornauer1 , Florian Frische2 , Andreas Lüdtke2 , and Jürgen Sauer1
1
Carl-von-Ossietzky University of Oldenburg, Germany
2
OFFIS – Institute for Informatics, Oldenburg, Germany
Keywords: Unmanned Aerial Vehicles, Swarm Behaviour, Human Computer
Interaction, Level of Autonomy, Level of Automation, Operator Workload.
1 Introduction
Currently, it is not well understood, how and to what extent a swarm of agents,
performing a task, benefits from supervision and guidance by a human operator.
At the same time it is considered vital to keep a human operator in the loop
to make informed decisions and improve the behaviour of agents in situations
which can not be anticipated beforehand. There are investigations into human
agent interaction, which mainly focus on aspects as the design of a graphical user
interface or the optimisation of the behaviour of the agents [1]. In similar work,
the way to exercise control is often chosen arbitrarily, focused on the research
question at hand. We present a framework for the comparative evaluation of ways
of interaction, varying in autonomy and automation according to established
taxonomies [2], which was developed and tested as part of a diploma thesis. As a
result we seek to devise general principles when designing ways of interaction, to
anticipate repercussions in the task performance of a controlled swarm of agents.
2 Research Topic
How ways of interactions can be developed and their influence on task perfor-
mance has been researched by studying agents which fly models of unmanned
aerial vehicles (UAVs) in a flight simulator, to observe their interaction with a
model of an operator while working on a task. A framework was implemented to
simulate both the UAVs and the operator to develop various ways of interaction
and assess their impact on the performance in a fire fighting scenario.
3 Methodology
The agents were designed to exhibit a self-organising swarm behaviour to supply
a high degree of autonomy, while in some scenarios the modelled UAVs were
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AT2012, 15-16 October 2012, Dubrovnik, Croatia. Copyright held by the authors.
allowed to detach from the swarm and fly more independently. The behaviour
was implemented according to a concept, investigated by C.W. Reynolds, where
UAVs choose their heading and airspeed depending on vectors which direction
and length are calculated based on their neighbours’ positions [3]. The objective
for each UAV was to extinguish every single fire known before deployment or
hidden ones which were revealed during the flight.
4 Results
A conducted evaluation, using the framework, showed it is possible to trace
aspects of the task performance back to design decisions for ways of interactions.
In most of the classes of the conducted experiments, the explored area, the
time and distance needed, and the number of times where the operator had to
interact, varied significantly. Waypoint based approaches lead to detours and
consequently to a slower task performance, however, the distance flown could
be anticipated beforehand because of a very small variation between individual
UAVs. In contrast, another approach relied heavily on the operator to select
the order of fires to be extinguished and so favoured experienced operators.
In small groups of agents this approach lead to good results because of the
parallel task execution and direct flight routes. Furthermore, in order to scale
the applicability to a greater number of agents another approach was developed
where the operator only agreed or revised a planned order by the system. This
approach could be shown to perform for the most part indistinguishable, while
it needed only one affirmation for each planned order of fires for each UAV, thus
reducing the total amount of interactions.
5 Conclusion
The requirements of the scenario and the experience of the operator have to be
taken into account while designing a way of interaction. The developed frame-
work can be used to devise and evaluate various ways of interaction and an-
ticipate shortcomings and benefits. This can contribute to any research where
human interaction with a group of agents is an elementary part. Because of the
adjustable scenario, the research findings are also important in search and rescue
situations as well as during the development of any UAV Ground Station.
References
1. Ruff, H.: Exploring automation issues in supervisory control of multiple UAVs.
Technical Report February, DTIC Document (2004)
2. Parasuraman, R., Sheridan, T., Wickens, C.: A model for types and levels of human
interaction with automation. Systems, Man and Cybernetics, Part A: Systems and
Humans, IEEE Transactions on 30(3) (May 2000) 286–297
3. Reynolds, C.: Steering behaviors for autonomous characters. In: Game Developers
Conference. http://www. red3d. com/cwr/steer/gdc99, Sony Computer Entertain-
ment America (1999)