Research on grasping control algorithm and optimization of rotorcraft aerial manipulator based on TD3 reinforcement learning

As drone control and communication technologies continue to mature, the use of drones equipped with cameras, robotic arms, pods, and other devices for multi-tasking and multi-scenario applications is becoming increasingly prevalent. Rotorcraft aerial manipulators have the potential to perform complex aerial operations such as grasping and transporting, offering wide-ranging applications. However, the complex coupling between the drone and the manipulator affects the stability and precision of the rotorcraft aerial manipulator. This paper aims to tackle the complex system dynamics and coupling issues that traditional control methods struggle to handle by leveraging reinforcement learning algorithms. By comparatively analyzing the performance of different reinforcement learning algorithms in rotorcraft aerial manipulator grasping tasks, a multi-objective TD3 reinforcement learning algorithm is proposed. Simulation experiments demonstrate the effectiveness and stability of this algorithm in the rotorcraft aerial manipulator system.