This work focuses on enhancing cooperation within a heterogeneous group of robots. It explores a scenario where an aerial manipulator hands over an object from a moving mobile robot on the ground, with both robots operating independently. The mobile robot is controlled separately, while the aerial manipulator observes its previous movements and must collaborate efficiently. The first contribution is a method for predicting the mobile robot's future trajectory based on its past behavior. Once this trajectory is established, the next challenge is to plan the aerial manipulator's approach for a time-optimal handover without needing to validate its dynamics. The proposed DMCC framework uses discrete variational Lagrangian mechanics to constrain system dynamics, ensuring reliable estimations. Additionally, handover opportunities are automatically identified and arranged according to desired complementarity constraints. The final contribution involves controlling the aerial manipulator through a nonlinear model predictive control (NMPC) framework, which incorporates an augmented dynamic model utilizing Gaussian processes for nonparametric regression. This approach enables the NMPC framework to determine optimal control inputs for the aerial manipulator, ensuring stable flight performance during the handover process.
