  Blimps are well suited to perform long-duration aerial tasks as they are
energy efficient, relatively silent and safe. To address the blimp navigation
and control task, in previous work we developed a hardware and
software-in-the-loop framework 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, making PID
controllers difficult to tune. Thus, often resulting in large tracking errors.
Moreover, the buoyancy of a blimp is constantly changing due to variations in
ambient temperature and pressure. To address these issues, in this paper we
present a learning-based framework based on deep residual reinforcement
learning (DRRL), for the blimp control task. Within this framework, we first
employ a PID controller to provide baseline performance. Subsequently, the DRRL
agent learns to modify the PID decisions by interaction with the environment.
We demonstrate in simulation that DRRL agent consistently improves the PID
performance. Through rigorous simulation experiments, we show that the agent is
robust to changes in wind speed and buoyancy. In real-world experiments, we
demonstrate that the agent, trained only in simulation, is sufficiently robust
to control an actual blimp in windy conditions. We openly provide the source
code of our approach at https://github.com/
robot-perception-group/AutonomousBlimpDRL.
