1. Visionﾠ&ﾠMotion – The frame throughput is
improved by leveraging hardware video encoding
to calculate optic flow using H.264. Motion data is
segmented into foreground and background
classes (Figure 4) to provide an additional input for
to the sensor fusion system.
Controlﾠ&ﾠTuning – In autonomous flight, Pl
controllers are used to keep the heading, altitude
and speed of the blimp constant relative to the
target. The controller step responses were tuned
during test flights (Table 1).
Conclusions – This blimp provides a hardware
and software platform to demonstrate autonomous
station keeping behaviour without relying on
additional ground-based equipment. Future work
will now refine the vision system and improve
control systems reliability.
Untethered
Autonomous Flight of
an Indoor Blimp
Andrew Mathieson
Supervisor: Toby Breckon
School of Engineering & Computer
Sciences, Durham University
Abstract – A prototype autonomous airship was
developed and tested indoors (Figure 1). Until
now, autonomous blimps have utilised additional
equipment on the ground to calculate their
position. This project instead uses visual and
inertia sensing to perform all processing on board.
Hardware – Low-cost commercial hardware
components were integrated into the blimp
payload. The key objectives for component
selection were weight minimisation and software
availability. The hardware comprises an Inertial
Measurement Unit (IMU), Pulse Width Modulated
(PWM) servo outputs and optically isolated motor
drivers (Figure 2).
Software – Estimates blimp pose relative to a
chessboard target using the camera, and fuses
camera pose estimates with data from the IMU.
Software is divided into four concurrent threads
(Figure 3) and runs on a Raspberry Pi 2.
MEng Research & Development Project April 2015
Figure 1 – Blimp in flight
Figure 2 – Hardware components
Figure 3 – Software architecture
Figure 4 – Captured frame and HSV representation of motion
Zeigler-Nichols
predicted 𝐾 𝑃 𝑦𝑎𝑤
Harriot/Nyquist
predicted 𝐾 𝑃 𝑦𝑎𝑤
𝐾 𝑃 𝑦𝑎𝑤
value found
testing
5.7
(underestimate)
19
(overestimate)
10 (Achieves good
control)
Table 1 – Proportional gain (𝐾 𝑃 𝑦𝑎𝑤) tuning: Comparison of
results from two analytical methods with experimental results