Coaxial multirotors, characterized by overlapping rotors, represent a common solution to increasing payload capacity while maintaining a compact platform size. However, the overlap between motors generates airflow disturbances that, if not taken into account properly, may decrease the system’s overall performance. In this paper, aerodynamic interactions for coaxial multirotors are analyzed and characterized. Two rotor models are introduced, which account for the aerodynamic interaction between the upper and the lower rotor. Each model is accompanied by its corresponding mixer design and analyzed with respect to the state-of-the-art mixer solution for classical multirotor systems. The proposed approaches are tested through rotor stand experiments, simulations, and implementation on an actual coaxial platform. The results demonstrate the effectiveness of these models in mitigating the adverse aerodynamic effects, thereby improving the performance and efficiency of coaxial multirotor systems.

Fully-actuated multirotors exhibit good performance in mitigating external forces due to their capability to generate forces in any direction, resulting in precise and robust disturbance rejection in unpredictable environments. However, in practice, the increased degrees of freedom of these platforms introduce new challenges for control strategies to ensure effective performance in the presence of model uncertainties and external disturbances. Existing solutions primarily focus on either parameter estimation or disturbance rejection to address these challenges, with limited applicability to fully-actuated systems. This study introduces a novel approach that simultaneously addresses both parameter uncertainties and disturbance rejection, specifically tailored for fully-actuated multirotors. The proposed methodology further incorporates the mixing strategy within the disturbance estimator's prediction model to facilitate rapid disturbance rejection and mitigate associated uncertainties.

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.