  Aerial robot solutions are becoming ubiquitous for an increasing number of
tasks. Among the various types of aerial robots, blimps are very well suited to
perform long-duration tasks while being energy efficient, relatively silent and
safe. To address the blimp navigation and control task, in our recent work, we
have developed a software-in-the-loop simulation and a PID-based controller for
large blimps in the presence of wind disturbance. However, blimps have a
deformable structure and their dynamics are inherently non-linear and
time-delayed, often resulting in large trajectory tracking errors. Moreover,
the buoyancy of a blimp is constantly changing due to changes in the ambient
temperature and pressure. In the present paper, we explore a deep reinforcement
learning (DRL) approach to address these issues. We train only in simulation,
while keeping conditions as close as possible to the real-world scenario. We
derive a compact state representation to reduce the training time and a
discrete action space to enforce control smoothness. Our initial results in
simulation show a significant potential of DRL in solving the blimp control
task and robustness against moderate wind and parameter uncertainty. Extensive
experiments are presented to study the robustness of our approach. We also
openly provide the source code of our approach.
