This paper provides a comprehensive guide for educators on how multirotor UAV platforms can be utilized to achieve various learning outcomes in undergraduate mechatronics education. This study is based on a PX4 ecosystem combined with the MATLAB Simulink programming environment, covering both hardware and software aspects to support engineering education. The paper explains (i) which learning outcomes can be obtained, (ii) how mathematical models can be derived and implemented in simulation software, (iii) which hardware components are essential, their approximate costs, and possible upgrades based on available budgets, and (iv) which experiments students can perform using the UAV platform. A proposed educational prototype integrates airframe parts produced using additive manufacturing technologies with standard multirotor components. Additionally, a series of experiments were designed, including extensive testing of the multirotor control module. Three learning outcomes related to UAV hardware were incorporated into the engineering curriculum, while two software-related outcomes were addressed through student workshops. Future plans include the implementation of multiple UAV platforms in the educational process to further enhance learning outcomes.

This letter introduces a novel approach for controlling aerial robots during physical interaction by integrating Admittance Control with Nonlinear Model Predictive Control (NMPC). Unlike existing methods, our technique incorporates the desired impedance dynamics directly into the NMPC prediction model, alongside the robot's dynamics. This allows for the explicit prediction of how the robot's impedance will respond to interaction forces within the prediction horizon. Consequently, our controller effectively tracks the desired impedance behavior during physical interaction while seamlessly transitioning to trajectory tracking in free motion, all while consistently respecting actuator constraints. The efficacy of this method is validated through real-time simulations and experiments involving physical interaction tasks with an aerial robot. Our findings demonstrate that, across most scenarios, our method significantly outperforms the state-of-the-art (which does not predict future impedance state), achieving a reduction in tracking error of up to 90%. Furthermore, the results indicate that our approach enables smoother and safer physical interaction, characterized by reduced oscillations and the absence of the unstable behavior observed with the state-of-the-art method in certain situations.

This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.