Improving autopilot systems for drones using machine learning

What is it about?

Autopilot systems allow aircrafts to be controlled without the help of a pilot. Autopilot systems are an important area of research for drone technologies. Traditional autopilot systems consist of an "inner loop" and an "outer loop." The inner loop provides stability and control. On the other hand, the outer loop is for mission-level control, such as controlling navigation. Traditional controllers of autopilot systems work well under stable conditions. However, their performance is inadequate under harsh and variable conditions. "Reinforcement learning (RL)," a type of machine learning, can be used to improve the performance of autopilot controllers. Thus far, RL has focused mainly on mission-level control, not inner loop control. This study tested the performance and accuracy of an inner loop control that has been trained with RL algorithms. First, the authors developed a simulation environment, that feels close to a real flight scenario. They then used it to train the flight controller of a drone. They then compared the performance of the controller to a traditional controller. They found that along with accurate tracking, the controller trained with RL also reduced error by 44%.

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Photo by Iewek Gnos on Unsplash

Photo by Iewek Gnos on Unsplash

Why is it important?

Autopilot systems must be able to adapt to changes in the flight environment. Maintaining the vehicle's direction or "attitude control" under extreme conditions is a major challenge. The findings presented in this paper can be a first step towards exploring the use of RL for smart flight control. It also shows ways to improve the accuracy of attitude control. KEY TAKEAWAY: This study shows how RL training can be used to improve smart flight control. It will enable scientists and engineers to improve the quality of attitude control in autopilot systems.