NMPC of Unmanned Aerial Manipulators Considering Obstacle Avoidance and Manipulability

Abstract

Trajectory tracking and obstacle avoidance are essential features to ensure the safe navigation of unmanned aerial manipulators (UAMs) in unstructured environments. However, these techniques may not work effectively in complex scenarios; therefore, this work presents a novel method to control the end effector of an aerial manipulator that simultaneously maximizes the manipulability of the robotic arm and guarantees obstacle avoidance of the entire system using non-linear model predictive control (NMPC) formulations. The NMPC includes trajectory tracking, obstacle avoidance, and the manipulability index in the cost function, resulting in fast algorithm convergence. The controller considers system constraints, such as the actuators’ limitations, including the aerial platform's maneuverability velocity and joint limits in the articulated manipulator. The results of the simulations demonstrate that the NMPC formulation successfully achieves the desired tasks, which validates the effectiveness of our approach ensuring safe and flexible navigation of aerial manipulators in complex scenarios.