Robust and Energy-Efficient Control for Multi-task Aerial Manipulation with Automatic Arm-switching

Aerial manipulation has received increasing research interest with wide applications of drones. To perform specific tasks, robotic arms with various mechanical structures will be mounted on the drone. It results in sudden disturbances to the aerial manipulator when switching the robotic arm or interacting with the environment. Hence, it is challenging to design a generic and robust control strategy adapted to various robotic arms when achieving multi-task aerial manipulation. In this paper, we present a learning-based control algorithm that allows online trajectory optimization and tracking to accomplish various aerial interaction tasks without manual adjustment. The proposed energy-saved trajectory planning approach integrates coupled dynamics model with a single rigid body to generate the energy-efficient trajectory for the aerial manipulator. Addressing the challenges of precise control when performing aerial manipulation tasks, this paper presents a controller based on deep neural networks that classifies and learns accurate forces and moments caused by different robotic arms and interactions. Moreover, the forces arising from robotic arm motions are delicately used as part of the drone’s power to save energy. Extensive real-world experiments demonstrate that the proposed method can adapt to various robotic arms and interactions when performing multi-task aerial manipulation.