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ent fault characteristics were fused by a Kalman filter.
The camera tracking signal was pre-filtered before it
was sent to the Kalman filter in order to filter out harsh
jumps in the tracked position. The estimated position
was fed to a vector flight control based on the poten-
tial field method, which allowed the UAV to reach a
sequence of inspection points without colliding with
obstacles in its environment. We also proposed a solu-
tion for the local minimum problem using the random
walk. In our evaluation we demonstrated that fusing
the two sensor values creates an estimate, that is ro-
bust to faulty inputs of one of the sensors. Addition-
ally we demontrated, that our navigation concept al-
lows the UAV to reach a sequence of inspection points
while avoiding surrounding obstacles. Finally it was
shown that the navigation works in conjunction with
the position estimate calculated by the Kalman filter.
In the future, we are planning on implementing the
proposed algorithms on real hardware. Additionally,
we plan on expanding the navigation to react to dy-
namic obstacles that are not known in advance. This
can be done by dynamically placing obstacles that are
detected by the UAV in the potential field and recal-
culating the force vecotor based on the new data. Fi-
nally, using a sensor that tracks the position of the
UAV relative to the part and not relative to a global
coordinate system, could allow the UAV to navigate
relative to the part even when it is in motion. This
would allow inspections of a structure while it is be-
ing craned from one assembly station to another one.
The feasability of this concept will have to be evalu-
ated in future work.
ACKNOWLEDGEMENTS
This work was created in collaboration with Kevin
Dittel, Teoman Ismail, Christian Adorian and
˚
Asa
Odenram from Premium AEROTEC GmbH.
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