Photovoltaic (PV) panels are becoming increasingly widespread in the domain of renewable energy, and thus, small efficiency gains can have massive effects. Anti-reflective and self-cleaning coatings enhance panel performance but degrade over time, requiring periodic reapplication. Uncrewed Aerial Vehicles (UAVs) offer a flexible and autonomous way to apply protective coatings more often and at lower cost compared to traditional manual coating methods. In this letter, we propose a quadcopter-based system, equipped with a liquid dispersion mechanism, designed to automate such tasks. The localization stack only uses onboard sensors, relying on visual-inertial odometry and the relative position of the PV panel detected with respect to the quadcopter. The control relies on a model-based controller that accounts for the ground effect and the mass decrease of the quadcopter during liquid dispersion. We validate the autonomy capabilities of our system through extensive indoor and outdoor experiments.

Drone-based rapid and accurate environmental edge detection is highly advantageous for tasks such as disaster relief and autonomous navigation. Current methods, using radars or cameras, raise deployment costs and burden lightweight drones with high computational demands. In this paper, we propose AirTouch, a system that transforms the ground effect from a stability "foe" in traditional flight control views, into a "friend" for accurate and efficient edge detection. Our key insight is that analyzing drone basic attitude sensor readings and flight commands allows us to detect ground effect changes. Such changes typically indicate the drone flying over a boundary of two materials, making this information valuable for edge detection. We approach this insight through theoretical analysis, algorithm design, and implementation, fully leveraging the ground effect as a new sensing modality without compromising drone flight stability, thereby achieving accurate and efficient scene edge detection. We also compare this new sensing modality with vision-based methods to clarify its exclusive advantages in resource efficiency and detection capability. Extensive evaluations demonstrate that our system achieves a high detection accuracy with mean detection distance errors of 0.051m, outperforming the baseline method performance by 86%. With such detection performance, our system requires only 43 mW power consumption, contributing to this new sensing modality for low-cost and highly efficient edge detection.

--The ground effect on multicopters introduces several challenges, such as control errors caused by additional lift, oscillations that may occur during near-ground flight due to external torques, and the influence of ground airflow on models such as the rotor drag and the mixing matrix. This article collects and analyzes the dynamics data of near-ground multicopter flight through various methods, including force measurement platforms and real-world flights. For the first time, we summarize the mathematical model of the external torque of multicopters under ground effect. The influence of ground airflow on rotor drag and the mixing matrix is also verified through adequate experimentation and analysis. Through simplification and derivation, the differential flatness of the multicopter's dynamic model under ground effect is confirmed. T o mitigate the influence of these disturbance models on control, we propose a control method that combines dynamic inverse and disturbance models, ensuring consistent control effectiveness at both high and low altitudes. In this method, the additional thrust and variations in rotor drag under ground effect are both considered and compensated through feedforward models. The leveling torque of ground effect can be equivalently represented as variations in the center of gravity and the moment of inertia. In this way, the leveling torque does not explicitly appear in the dynamic model. The final experimental results show that the method proposed in this paper reduces the control error (RMSE) by 45.3%. Please check the supplementary material at: https://github.com/ZJU-F

Due to dynamic variations such as changing payload, aerodynamic disturbances, and varying platforms, a robust solution for quadrotor trajectory tracking remains challenging. To address these challenges, we present a deep reinforcement learning (DRL) framework that achieves physical dynamics invariance by directly optimizing force/torque inputs, eliminating the need for traditional intermediate control layers. Our architecture integrates a temporal trajectory encoder, which processes finite-horizon reference positions/velocities, with a latent dynamics encoder trained on historical state-action pairs to model platform-specific characteristics. Additionally, we introduce scale-aware dynamics randomization parameterized by the quadrotor's arm length, enabling our approach to maintain stability across drones spanning from 30g to 2.1kg and outperform other DRL baselines by 85% in tracking accuracy. Extensive real-world validation of our approach on the Crazyflie 2.1 quadrotor, encompassing over 200 flights, demonstrates robust adaptation to wind, ground effects, and swinging payloads while achieving less than 0.05m RMSE at speeds up to 2.0 m/s. This work introduces a universal quadrotor control paradigm that compensates for dynamic discrepancies across varied conditions and scales, paving the way for more resilient aerial systems.

Designing ground-aerial robots is challenging due to the increased actuation requirements which can lead to added weight and reduced locomotion efficiency. Morphobots mitigate this by combining actuators into multi-functional groups and leveraging ground transformation to achieve different locomotion modes. However, transforming on the ground requires dealing with the complexity of ground-vehicle interactions during morphing, limiting applicability on rough terrain. Mid-air transformation offers a solution to this issue but demands operating near or beyond actuator limits while managing complex aerodynamic forces. We address this problem by introducing the Aerially Transforming Morphobot (ATMO), a robot which transforms near the ground achieving smooth transition between aerial and ground modes. To achieve this, we leverage the near ground aerodynamics, uncovered by experimental load cell testing, and stabilize the system using a model-predictive controller that adapts to ground proximity and body shape. The system is validated through numerous experimental demonstrations. We find that ATMO can land smoothly at body postures past its actuator saturation limits by virtue of the uncovered ground-effect.

This paper presents an optic flow-guided approach for achieving soft landings by resource-constrained unmanned aerial vehicles (UAVs) on dynamic platforms. An offline data-driven linear model based on Koopman operator theory is developed to describe the underlying (nonlinear) dynamics of optic flow output obtained from a single monocular camera that maps to vehicle acceleration as the control input. Moreover, a novel adaptation scheme within the Koopman framework is introduced online to handle uncertainties such as unknown platform motion and ground effect, which exert a significant influence during the terminal stage of the descent process. Further, to minimize computational overhead, an event-based adaptation trigger is incorporated into an event-driven Model Predictive Control (MPC) strategy to regulate optic flow and track a desired reference. A detailed convergence analysis ensures global convergence of the tracking error to a uniform ultimate bound while ensuring Zeno-free behavior. Simulation results demonstrate the algorithm's robustness and effectiveness in landing on dynamic platforms under ground effect and sensor noise, which compares favorably to non-adaptive event-triggered and time-triggered adaptive schemes.

Air ducts are integral to modern buildings but are challenging to access for inspection. Small quadrotor drones offer a potential solution, as they can navigate both horizontal and vertical sections and smoothly fly over debris. However, hovering inside air ducts is problematic due to the airflow generated by the rotors, which recirculates inside the duct and destabilizes the drone, whereas hovering is a key feature for many inspection missions. In this article, we map the aerodynamic forces that affect a hovering drone in a duct using a robotic setup and a force/torque sensor. Based on the collected aerodynamic data, we identify a recommended position for stable flight, which corresponds to the bottom third for a circular duct. We then develop a neural network-based positioning system that leverages low-cost time-of-flight sensors. By combining these aerodynamic insights and the data-driven positioning system, we show that a small quadrotor drone (here, 180 mm) can hover and fly inside small air ducts, starting with a diameter of 350 mm. These results open a new and promising application domain for drones.

Overactuated tilt-rotor platforms offer many advantages over traditional fixed-arm drones, allowing the decoupling of the applied force from the attitude of the robot. This expands their application areas to aerial interaction and manipulation, and allows them to overcome disturbances such as from ground or wall effects by exploiting the additional degrees of freedom available to their controllers. However, the overactuation also complicates the control problem, especially if the motors that tilt the arms have slower dynamics than those spinning the propellers. Instead of building a complex model-based controller that takes all of these subtleties into account, we attempt to learn an end-to-end pose controller using Reinforcement Learning (RL), and show its superior behavior in the presence of inertial and force disturbances compared to a state-of-the-art traditional controller.

The concept of aerial-aquatic robots has emerged as an innovative solution that can operate both in the air and underwater. Previous research on the design of such robots has been mainly focused on mature technologies such as fixed-wing and multi-rotor aircraft. Flying fish, a unique aerial-aquatic animal that can both swim in water and glide over the sea surface, has not been fully explored as a bionic robot model, especially regarding its motion patterns with the collapsible pectoral fins. To verify the contribution of the collapsible wings to the flying fish motion pattern, we have designed a novel bio-robot with collapsible wings inspired by the flying fish. The bionic prototype has been successfully designed and fabricated, incorporating collapsible wings with soft hydraulic actuators, an innovative application of soft actuators to a micro aquatic-aerial robot. We have analyzed and built a precise model of dynamics for control, and tested both the soft hydraulic actuators and detailed aerodynamic coefficients. To further verify the feasibility of collapsible wings, we conducted simulations in different situations such as discharge angles, the area of collapsible wings, and the advantages of using ground effect. The results confirm the control of the collapsible wings and demonstrate the unique multi-modal motion pattern between water and air. Overall, our research represents the study of the collapsible wings in aquatic-aerial robots and significant contributes to the development of aquatic-aerial robots.

A deformable land-air robot is introduced with excellent driving and flying capabilities, offering a smooth switching mechanism between the two modes. An elaborate coupled dynamics model is established for the robot, including rotors, chassis, suspension, and the deformable structure. In addition, a model-based controller is designed for landing and mode switching in various unstructured conditions, such as slopes and curved surface. And considering locomotion and complex near-ground situations to achieve cooperation between the two fused modalities. This system was simulated in ADAMS/Simulink and a tested with hardware-in-the-loop system was constructed for testing in various slopes. With a designed controller, the results showed the robot is capable of fast and smooth land-air switching, with a 24.6 % faster landing on slopes. The controller can also reduce landing offset and impact force more effectively than the normal control method at 32.7 % and 34.3 %, respectively.