In agile quadrotor flight, accurately modeling the varying aerodynamic drag forces encountered at different speeds is critical. These drag forces significantly impact the performance and maneuverability of the UAV, especially during high-speed maneuvers. Traditional control models based on first principles struggle to capture these dynamics due to the complexity and variability of aerodynamic effects, which are challenging to model accurately. To address these challenges, this study proposes a meta-learning-based control strategy for accurately modeling quadrotor dynamics under varying speeds, treating each velocity condition as an independent learning task with a specifically trained neural network to ensure precise dynamic predictions. The meta-learning framework rapidly generates task-specific parameters adapted to speed variations by solving an optimization problem and employs an online incremental learning strategy to integrate real-time data for continuous model updates, enhancing system robustness. Regularization is introduced to prevent overfitting and improve generalizability. The integration of the meta-learned model into Model Predictive Contouring Control (MPCC) allows the system to achieve optimal control across different velocity levels, ensuring efficient and accurate flight control even during sharp turns and high-speed maneuvers. Extensive simulations and real-world experiments confirm that the proposed algorithm maintains a high level of control precision despite the nonlinear effects of rapid speed changes, complex flight trajectories and wind disturbances. The results highlight the advantages of combining meta-learning with adaptive control strategies, providing a robust framework for quadrotors operating in diverse and dynamic environments.

The article presents an air-assisted ground robotic autonomous exploration framework, which leverages the high mobility and wide aerial perspective of unmanned aerial vehicles (UAVs) to assist unmanned ground vehicles (UGVs) in detailed exploration, enhancing exploration efficiency and improving the quality of point cloud collection in regions of interest in large-scale, unknown environments. In this framework, the UAV, equipped with an onboard RGB camera, rapidly surveys large unknown areas and generates a bird's eye view (BEV) to identify critical zones for UGV exploration. With prior information about the unexplored area's outline from the real-time shared BEV, the UGV can carry out more efficient and informed exploration from a global perspective. To maximize the utility of this prior information and optimize point cloud collection, a hierarchical exploration strategy and an attention mechanism are incorporated to guide the UGV's focus toward areas requiring detailed mapping, rather than broad, featureless regions. Real-world experiments validate the effectiveness of the framework, demonstrating significant improvements in exploration efficiency and point cloud collection compared to state-of-the-art methods. The results further show that even with a coarse BEV, the UGV's exploration efficiency is greatly enhanced.

Control of an aerial manipulator is challenging due to the decentralized dynamics of the aerial vehicle and the robotic arm. It is generally complex to adjust the controller of the aerial manipulator when replacing a different robotic arm. This paper presents a flexible control scheme for a quadrotor-based aerial manipulator equipped with a replaceable robotic arm. To analyze the dynamic characteristics during grasping, the model of the aerial manipulator is decentralized including the models of a quadrotor and the centroid of an n-DOF robotic arm. The interaction effect of a moving robotic arm on the quadrotor is considered by analyzing the varying centroid of the robotic arm. Based on the modeling of the aerial manipulator, a control scheme integrating a linear model predictive control (LMPC) and a feedforward controller is presented to accurately control the motion of the aerial platform. The LMPC controls the aerial vehicle to follow the desired trajectory, and a feedforward controller keeps the aerial platform hovering stably during grasping. Practical experiments with two different robotic arms are performed. Experimental results show that the proposed modeling and control scheme provides a flexible and effective approach for an aerial manipulator with a replaceable robotic arm.