The tailsitter vertical takeoff and landing (VTOL) UAV is widely used due to its lower dead weight, which eliminates the actuators and mechanisms for tilting. However, the tailsitter UAV is susceptible to wind disturbances in multi-rotor mode, as it exposes a large frontal fuselage area. To address this issue, our tailsitter UAV features a reconfigurable wing design, allowing wings to retract in multi-rotor mode and extend in fixed- wing mode. Considering power efficiency, we design a coaxial heterogeneous dual-rotor configuration, which significantly re- duces the total power consumption. To reduce structural weight and simplify structural complexity, we employ a swashplateless mechanism with an improved design to control pitch and roll in multi-rotor mode. We optimize the structure of the swashplateless mechanism by adding flapping hinges, which reduces vibration during cyclic acceleration and deceleration. Finally, we perform comprehensive transition flight tests to validate stable flight performance across the entire flight envelope of the tailsitter UAV.

Unmanned aerial vehicles (UAVs) operating in dynamic wind fields must generate safe and energy-efficient trajectories under physical and environmental constraints. Traditional planners, such as A* and kinodynamic RRT*, often yield suboptimal or non-smooth paths due to discretization and sampling limitations. This paper presents a physics-informed neural network (PINN) framework that embeds UAV dynamics, wind disturbances, and obstacle avoidance directly into the learning process. Without requiring supervised data, the PINN learns dynamically feasible and collision-free trajectories by minimizing physical residuals and risk-aware objectives. Comparative simulations show that the proposed method outperforms A* and Kino-RRT* in control energy, smoothness, and safety margin, while maintaining similar flight efficiency. The results highlight the potential of physics-informed learning to unify model-based and data-driven planning, providing a scalable and physically consistent framework for UAV trajectory optimization.

In this work, we present a novel control approach based on partial feedback linearization (PFL) for the stabilization of a suspended aerial platform with an attached load. Such systems are envisioned for various applications in construction sites involving cranes, such as the holding and transportation of heavy objects. Our proposed control approach considers the underactuation of the whole system while utilizing its coupled dynamics for stabilization. We demonstrate using numerical stability analysis that these coupled terms are crucial for the stabilization of the complete system. We also carried out robustness analysis of the proposed approach in the presence of external wind disturbances, sensor noise, and uncertainties in system dynamics. As our envisioned target application involves cranes in outdoor construction sites, our control approaches rely on only onboard sensors, thus making it suitable for such applications. We carried out extensive simulation studies and experimental tests to validate our proposed control approach.

The endurance and energy efficiency of drones remain critical challenges in their design and operation. To extend mission duration, numerous studies explored perching mechanisms that enable drones to conserve energy by temporarily suspending flight. This paper presents a new perching drone that utilizes an active flexible perching mechanism inspired by the rapid predation mechanism of the Venus flytrap, achieving perching in less than 100 ms. The proposed system is designed for high-speed adaptability to the perching targets. The overall drone design is outlined, followed by the development and validation of the biomimetic perching structure. To enhance the system stability, a cascade extended high-gain observer (EHGO) based control method is developed, which can estimate and compensate for the external disturbance in real time. The experimental results demonstrate the adaptability of the perching structure and the superiority of the cascaded EHGO in resisting wind and perching disturbances.

Reinforcement learning (RL) has demonstrated the ability to maintain the plasticity of the policy throughout short-term training in aerial robot control. However, these policies have been shown to loss of plasticity when extended to long-term learning in non-stationary environments. For example, the standard proximal policy optimization (PPO) policy is observed to collapse in long-term training settings and lead to significant control performance degradation. To address this problem, this work proposes a cost-aware framework that uses a retrospective cost mechanism (RECOM) to balance rewards and losses in RL training with a non-stationary environment. Using a cost gradient relation between rewards and losses, our framework dynamically updates the learning rate to actively train the control policy in a disturbed wind environment. Our experimental results show that our framework learned a policy for the hovering task without policy collapse in variable wind conditions and has a successful result of 11.29% less dormant units than L2 regularization with PPO.

A critical goal of adaptive control is enabling robots to rapidly adapt in dynamic environments. Recent studies have developed a meta-learning-based adaptive control scheme, which uses meta-learning to extract nonlinear features (represented by Deep Neural Networks (DNNs)) from offline data, and uses adaptive control to update linear coefficients online. However, such a scheme is fundamentally limited by the linear parameterization of uncertainties and does not fully unleash the capability of DNNs. This paper introduces a novel learning-based adaptive control framework that pretrains a DNN via self-supervised meta-learning (SSML) from offline trajectories and online adapts the full DNN via composite adaptation. In particular, the offline SSML stage leverages the time consistency in trajectory data to train the DNN to predict future disturbances from history, in a self-supervised manner without environment condition labels. The online stage carefully designs a control law and an adaptation law to update the full DNN with stability guarantees. Empirically, the proposed framework significantly outperforms (19-39%) various classic and learning-based adaptive control baselines, in challenging real-world quadrotor tracking problems under large dynamic wind disturbance.

This paper evaluates and compares the performance of model-free and model-based reinforcement learning for the attitude control of fixed-wing unmanned aerial vehicles using PID as a reference point. The comparison focuses on their ability to handle varying flight dynamics and wind disturbances in a simulated environment. Our results show that the Temporal Difference Model Predictive Control agent outperforms both the PID controller and other model-free reinforcement learning methods in terms of tracking accuracy and robustness over different reference difficulties, particularly in nonlinear flight regimes. Furthermore, we introduce actuation fluctuation as a key metric to assess energy efficiency and actuator wear, and we test two different approaches from the literature: action variation penalty and conditioning for action policy smoothness. We also evaluate all control methods when subject to stochastic turbulence and gusts separately, so as to measure their effects on tracking performance, observe their limitations and outline their implications on the Markov decision process formalism.

Autonomous drone navigation faces a critical challenge in achieving accurate landings on dynamic platforms, especially under unpredictable conditions such as wind turbulence. Our research introduces TornadoDrone, a novel Deep Reinforcement Learning (DRL) model that adopts bio-inspired mechanisms to adapt to wind forces, mirroring the natural adaptability seen in birds. This model, unlike traditional approaches, derives its adaptability from indirect cues such as changes in position and velocity, rather than direct wind force measurements. TornadoDrone was rigorously trained in the gym-pybullet-drone simulator, which closely replicates the complexities of wind dynamics in the real world. Through extensive testing with Crazyflie 2.1 drones in both simulated and real windy conditions, TornadoDrone demonstrated a high performance in maintaining high-precision landing accuracy on moving platforms, surpassing conventional control methods such as PID controllers with Extended Kalman Filters. The study not only highlights the potential of DRL to tackle complex aerodynamic challenges but also paves the way for advanced autonomous systems that can adapt to environmental changes in real-time. The success of TornadoDrone signifies a leap forward in drone technology, particularly for critical applications such as surveillance and emergency response, where reliability and precision are paramount.

Robotic blimps, as lighter-than-air aerial systems, offer prolonged duration and enhanced safety in human-robot interactions due to their buoyant lift. However, robust flight against environmental airflow disturbances remains a significant challenge, limiting the broader application of these robots. Drawing inspiration from the flight mechanics of birds and their ability to perch against natural wind, this article introduces RGBlimp-Q, a robotic gliding blimp equipped with a bird-inspired continuum arm. This arm allows for flexible attitude adjustments through moving mass control to enhance disturbance resilience, while also enabling object capture by using claws to counteract environmental disturbances, similar to a bird. This article presents the design, modeling, and prototyping of RGBlimp-Q, thus extending the advantages of robotic blimps to more complex environments. To the best of the authors' knowledge, this is the first interdisciplinary design integrating continuum mechanisms onto robotic blimps. Experimental results from both indoor and outdoor settings validate the improved flight robustness against environmental disturbances offered by this novel design.

It is challenging to model and control a tail-sitter unmanned aerial vehicle (UAV) because its blended wing body generates complicated nonlinear aerodynamic effects, such as wing lift, fuselage drag, and propeller-wing interactions. We therefore devised a hybrid aerodynamic modeling method and model predictive control (MPC) design for a quadrotor tail-sitter UAV. The hybrid model consists of the Newton-Euler equation, which describes quadrotor dynamics, and a feedforward neural network, which learns residual aerodynamic effects. This hybrid model exhibits high predictive accuracy at a low computational cost and was used to implement hybrid MPC, which optimizes the throttle, pitch angle, and roll angle for position tracking. The controller performance was validated in real-world experiments, which obtained a 57% tracking error reduction compared with conventional nonlinear MPC. External wind disturbance was also introduced and the experimental results confirmed the robustness of the controller to these conditions.