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Unmanned aerial vehicles (UAVs) have been shown to be useful for the installation of wireless sensor networks (WSNs). More notably, the accurate placement of sensor nodes using UAVs, opens opportunities for many industrial and scientific uses, in particular, in hazardous environments or inaccessible locations. This publication proposes and demonstrates a new aerial sensor placement method based on impulsive launching. Since direct physical interaction is not required, sensor deployment can be achieved in cluttered environments where the target location cannot be safely approached by the UAV, such as under the forest canopy. The proposed method is based on mechanical energy storage and an ultralight shape memory alloy (SMA) trigger. The developed aerial system weighs a total of 650 grams and can execute up to 17 deployments on a single battery charge. The system deploys sensors of 30 grams up to 4 meters from a target with an accuracy of ±10 cm. The aerial deployment method is validated through more than 80 successful deployments in indoor and outdoor environments. The proposed approach can be integrated in field operations and complement other robotic or manual sensor placement procedures. This would bring benefits for demanding industrial applications, scientific field work, smart cities and hazardous environments [Video attachment: https://youtu.be/duPRXCyo6cY].

Aerial-aquatic robots possess the unique ability of operating in both air and water. However, this capability comes with tremendous challenges, such as communication incompatibility, increased airborne mass, potentially inefficient operation in each of the environments and manufacturing difficulties. Such robots, therefore, typically have small payloads and a limited operational envelope, often making their field usage impractical. We propose a novel robotic water sampling approach that combines the robust technologies of multirotors and underwater micro-vehicles into a single integrated tool usable for field operations. The proposed solution encompasses a multirotor capable of landing and floating on the water, and a tethered mobile underwater pod that can be deployed to depths of several meters. The pod is controlled remotely in three dimensions and transmits video feed and sensor data via the floating multirotor back to the user. The `dual-robot' approach considerably simplifies robotic underwater monitoring, while also taking advantage of the fact that multirotors can travel long distances, fly over obstacles, carry payloads and manoeuvre through difficult terrain, while submersible robots are ideal for underwater sampling or manipulation. The presented system can perform challenging tasks which would otherwise require boats or submarines. The ability to collect aquatic images, samples and metrics will be invaluable for ecology and aquatic research, supporting our understanding of local climate in difficult-to-access environments [Video attachment: https://youtu.be/v4xWmEHUSM4].

Autonomous aquatic vehicles capable of flight can deploy more rapidly, access remote or constricted areas, overfly obstacles and transition easily between distinct bodies of water. This new class of vehicles can be referred as Unmanned Aerial-Aquatic Vehicles (UAAVs), and is capable of reaching distant locations rapidly, conducting measurements and returning to base. This greatly improves upon current solutions, which often involve integrating different types of vehicles (e.g. vessels releasing underwater vehicles), or rely on manpower (e.g. sensors dropped manually from ships). Thanks to recent research efforts, UAAVs are becoming more sophisticated and robust. Nonetheless numerous challenges remain to be addressed, and particularly dedicated control and sensing solutions are still scarce. This paper discusses challenges and opportunities in UAAV control, sensing and actuation. Following a brief overview of the state of the art, we elaborate on the requirements and challenges for the main types of robots and missions proposed in the literature to date, and highlight existing solutions where available. The concise but wide-ranging overview provided will constitute a useful starting point for researchers undertaking UAAV control work.

Tailless flapping wing micro aerial vehicles (FMWAVs) are known for their light weight and agility. However, given the fact that these FWMAVs have been developed only recently, their flight dynamics have not yet been fully explained. In this paper we develop grey-box models for the time-averaged longitudinal dynamics of a tailless FWMAV (DelFly Nimble) from free-flight data using closed-loop system identification techniques. The consequence of the tailless configuration is inherent instability, therefore tailless FWMAVs are generally more complex than their tailed counterparts and require an active feedback control system. The control system introduces additional challenges to the system identification process as it counteracts the perturbations required to excite the system. Based on this approach, grey-box models were estimated and validated for airspeeds ranging from hover conditions, 0 m/s, to 1.0 m/s forward flight. Despite the complexity of the system, we were able to obtain low-order local models that are both efficient and accurate (R2 values up to 0.92) and can therefore be used for stability analysis, simulation and control design. With these models we can also take the first steps towards fully understanding the flight dynamics of tailless FWMAVs.

Robotic vehicles that are capable of autonomously transitioning between various terrains and fluids have received notable attention in the past decade due to their potential to navigate previously unexplored and/or unpredictable environments. Specifically, aerial-aquatic mobility will enable robots to operate in cluttered aquatic environments and carry out a variety of sensing tasks. One of the principal challenges in the development of such vehicles is that the transition from water to flight is a power-intensive process. At a small scale, this is made more difficult by the limitations of electromechanical actuation and the unfavorable scaling of the physics involved. This paper investigates the use of solid reactants as a combustion gas source for consecutive aquatic jump-gliding sequences. We present an untethered robot that is capable of multiple launches from the water surface and of transitioning from jetting to a glide. The power required for aquatic jump-gliding is obtained by reacting calcium carbide powder with the available environmental water to produce combustible acetylene gas, allowing the robot to rapidly reach flight speed from water. The 160-gram robot could achieve a flight distance of 26 meters using 0.2 gram of calcium carbide. Here, the combustion process, jetting phase, and glide were modeled numerically and compared with experimental results. Combustion pressure and inertial measurements were collected on board during flight, and the vehicle trajectory and speed were analyzed using external tracking data. The proposed propulsion approach offers a promising solution for future high-power density aerial-aquatic propulsion in robotics.

Despite significant research progress on small-scale aerial–aquatic robots, most existing prototypes are still constrained by short operation times and limited performance in different fluids. The main challenge is to design a vehicle that satisfies the partially conflicting design requirements for aerial and aquatic operations. In this letter we present a new class of aerial–aquatic robot, the sailing micro air vehicle, “SailMAV.” Thanks to a three-part folding wing design, the SailMAV is capable of both flying and sailing. The robot design permits long and targeted missions at the water interface by leveraging the wind as movement vector. It simultaneously offers the flexibility of flight for rapidly reaching a designated area, overcoming obstacles, and moving from one body of water to another, which can be very useful for water sampling in areas with many obstacles. With a total wingspan of 0.96 m, the SailMAV employs the same wing and actuation surfaces for sailing as for flying. It is capable of water surface locomotion as well as takeoff and flight at a cruising speed of 10.8 m $\cdot$ s $^{-1}$ . The main contributions of this letter are new solutions to the challenges of combined aerial and aquatic locomotions, the design of a novel hybrid concept, the development of the required control laws, and the demonstration of the vehicle successfully sailing and taking off from the water. This letter can inform the design of hybrid vehicles that adapt their morphology to move effectively.

Despite significant interest in tailless flapping-wing micro aerial vehicle designs, tailed configurations are often favoured, as they offer many benefits, such as static stability and a simpler control strategy, separating wing and tail control. However, the tail aerodynamics are highly complex due to the interaction between the unsteady wing wake and tail, which is generally not modelled explicitly. We propose an approach to model the flapping-wing wake and hence the tail aerodynamics of a tailed flapping-wing robot. First, the wake is modelled as a periodic function depending on wing flap phase and position with respect to the wings. The wake model is constructed out of six low-order sub-models representing the mean, amplitude and phase of the tangential and vertical velocity components. The parameters in each sub-model are estimated from stereo-particle image velocimetry measurements using an identification method based on multivariate simplex splines. The computed model represents the measured wake with high accuracy, is computationally manageable and is applicable to a range of different tail geometries. The wake model is then used within a quasi-steady aerodynamic model, and combined with the effect of free-stream velocity, to estimate the forces produced by the tail. The results provide a basis for further modelling, simulation and design work, and yield insight into the role of the tail and its interaction with the wing wake in flapping-wing vehicles. It was found that due to the effect of the wing wake, the velocity seen by the tail is of a similar magnitude as the free stream and that the tail is most effective at 50–70% of its span.

A time-varying model for the forward flight dynamics of a flapping-wing micro aerial vehicle is identified from free-flight optical tracking data. The model is validated and used to assess the validity of the widely applied time-scale separation assumption. Based on this assumption, each aerodynamic force and moment is formulated as a linear addition of decoupled time-averaged and time-varying submodels. The resulting aerodynamic models are incorporated in a set of linearized equations of motion, yielding a simulation-capable full dynamic model. The time-averaged component includes both the longitudinal and the lateral aerodynamics and is assumed to be linear. The time-varying component is modeled as a third-order Fourier series, which approximates the flapping dynamics effectively. Combining both components yields a more complete and realistic simulation. Results suggest that while in steady flight the time-scale separation assumption applies well during maneuvers the time-varying dynamics are not fully captured. More accurate modeling of flapping-wing flight during maneuvers may require considering coupling between the time scales.

Flight control of Flapping Wing Micro Air Vehicles is challenging, because of their complex dynamics and variability due to manufacturing inconsistencies. Machine Learning algorithms can be used to tackle these challenges. A Policy Gradient algorithm is used to tune the gains of a Proportional-Integral controller using Reinforcement Learning. A novel Classification Algorithm for Machine Learning control (CAML) is presented, which uses model identification and a neural network classifier to select from several predefined gain sets. The algorithms show comparable performance when considering variability only, but the Policy Gradient algorithm is more robust to noise, disturbances, nonlinearities and flapping motion. CAML seems to be promising for problems where no single gain set is available to stabilize the entire set of variable systems.

The effects of the horizontal tail surface on the longitudinal dynamics of an or- nithopter were studied by systematically varying its surface area, aspect ratio and its longitudinal position. The objective is to improve the understanding of the tail effect on the behaviour of the ornithopter and to assess if simple models based on tail geometry can predict steady-state conditions and dynamic behaviour. A data- driven approach was adopted since no suitable theoretical models for ornithopter tail aerodynamics are available. Data was obtained through wind tunnel and free-flight experiments. Fourteen tail geometries were tested, at four positions with respect to the fl apping wings. Linearised models were used to study the effects of the tail on dynamic behaviour. The data shows that, within the tested ranges, increasing surface area or aspect ratio increases the steady-state velocity of the platform and improves pitch damping. Results also suggest that the maximum span width of the tail significantly influences the damping properties, especially when the distance between the tail and the flapping wings is large, which likely relates to the induced velocity profile of the flapping wings. Steady-state conditions can be predicted accurately based on tail geometry even when extrapolated slightly outside the original measurement range. Some trends were identified between model parameters and tail geometry, but more research is required before these trends can be applied as a design tool.

Biologically-inspired flapping-wing micro aerial vehicles are characterised by nonlinear, unsteady aerodynamics and complex dynamics, both highly challenging to model. To take full advantage of the flight capabilities of such vehicles, it is necessary to obtain insight into their dynamics in the different flyable conditions, and to provide adequate control in all of these conditions. Nonetheless, the dynamics are typically only considered in a single flight regime, and controllers are frequently tuned for a particular flight condition. Due to the high complexity of flapping flight and limited availability of accurate free-flight data, global models are not yet readily available, particularly models based on free-flight data and suitable for practical applications. This paper demonstrates an approach to obtain a global dynamic model for a flapping-wing micro aerial vehicle. To allow for standard linear control and systems theory to be applied, the nonlinear dynamics are approximated using a linear parameter-varying (LPV) approach based on a set of local linear models. The scheduling parameters, and the parameters in the underlying local models, are determined using system identification methods applied to free-flight data collected on a real test platform, and covering a significant part of the flight envelope. The proposed approach allows for modelling of the vehicle and prediction of the dominant dynamic properties across the considered part of the flight envelope, using a total of 16 parameters, as opposed to the starting point of 46 local models with 12 parameters each. The use of a single model adapting to the flight condition provides flexibility and continuous coverage, and is therefore highly useful for simulation and control applications. While in the explored part of the flight envelope the nonlinearity was found to be limited, such that a weighted average model may be sufficient for some applications, the LPV model provides a higher accuracy and more consistent performance across the conditions considered. Additionally, the approach is shown to be promising and is expected to be adaptable to cover more significant variation. Improvements could be obtained through more extensive flight envelope coverage, more accurate measurement and more informative identification data.

Taking full advantage of the favorable flight properties of biologically inspired flapping-wing micro aerial vehicles requires having insight into their dynamics and providing adequate control in all flight conditions. Because of the high complexity of flapping flight and limited availability of accurate flight data, however, global models are not readily available, particularly models validated with flight data and suitable for practical applications. This paper proposes an approach for global modeling of nonlinear flapping-wing dynamics, constructing a linear parameter-varying model from a set of local linear models. The model parameters and scheduling functions are determined using system identification, from free-flight data collected on a real test platform over a significant part of the flight envelope. The resulting model allows for the dominant parts of the dynamics to be accurately represented across the considered range of conditions. With 25 parameters overall, it significantly improves on the starting point of 46 local models with 12 parameters each. Moreover, a single model that adapts to the flight condition provides flexibility and continuous coverage, highly useful for simulation and control applications. Although in the explored part of the flight envelope nonlinearity was found to be limited, the approach is scalable and expected to also cover larger variations.

Although flapping-wing micro aerial vehicles have become a hot topic in academia, the knowledge we have of these systems, their force generation mechanisms and dynamics is still limited. Recent technological advances have allowed for the development of free flight test setups using on-board sensors and external tracking systems, for system identification purposes. Nevertheless, there is still little knowledge about the system requirements, as well as on how to perform free flight test experiments, and process the collected data. The present article presents the guidelines for flapping-wing micro aerial vehicle free flight testing. In particular, it gathers information produced by different studies and provides the best practices for the proper system dimensioning, system setup, on-board sensors, maneuver input design, error analyses and data post-processing, for the reconstruction of the forces and moments that act during free flight of a flapping-wing robot, for system identification and modeling purposes. Furthermore, this article compares the results obtained using external optical position tracking systems with on-board and external sensor fusion, and provides suitable solutions and methods for data fusion and force reconstruction.

Despite an intensive research on flapping flight and flapping wing MAVs in recent years, there are still no accurate models of flapping flight dynamics. This is partly due to lack of free flight data, in particular during manoeuvres. In this work, we present, for the first time, a comparison of free flight forces estimated using solely an on-board IMU with wind tunnel measurements. The IMU based estimation brings higher sampling rates and even lower variation among individual wingbeats, compared to what has been achieved with an external motion tracking system in the past. A good match was found in comparison to wind tunnel measurements; the slight differences observed are attributed to clamping effects. Further insight was gained from the on-board rpm sensor, which showed motor speed variation of ± 15% due to load variation over a wingbeat cycle. The IMU based force estimation represents an attractive solution for future studies of flapping wing MAVs as, unlike wind tunnel measurements, it allows force estimation at high temporal resolutions also during manoeuvres.

With the increased use of unmanned aerial systems (UAS) for civil and commercial applications, there is a strong demand for new regulations and technology that will eventually permit for the integration of UAS in unsegregated airspace. This requires new technology to ensure sufficient safety and a smooth integration process. The absence of a pilot on board a vehicle introduces new problems that do not arise in manned flight. One challenging and safety-critical issue is flight in known icing conditions. Whereas in manned flight, dealing with icing is left to the pilot and his appraisal of the situation at hand; in unmanned flight, this is no longer an option and new solutions are required. To address this, an icing-related decision-making system (IRDMS) is proposed. The system quantifies in-flight icing based on changes in aircraft performance and measurements of environmental properties, and evaluates what the effects on the aircraft are. Based on this, it determines whether the aircraft can proceed, and whether and which available icing protection systems should be activated. In this way, advice on an appropriate response is given to the operator on the ground, to ensure safe continuation of the flight and avoid possible accidents.

Flapping-wing aerodynamic models that are accurate, computationally efficient and physically meaningful, are challenging to obtain. Such models are essential to design flapping-wing micro air vehicles and to develop advanced controllers enhancing the autonomy of such vehicles. In this work, a phenomenological model is developed for the time-resolved aerodynamic forces on clap-and-fling ornithopters. The model is based on quasi-steady theory and accounts for inertial, circulatory, added mass and viscous forces. It extends existing quasi-steady approaches by: including a fling circulation factor to account for unsteady wing-wing interaction, considering real platform-specific wing kinematics and different flight regimes. The model parameters are estimated from wind tunnel measurements conducted on a real test platform. Comparison to wind tunnel data shows that the model predicts the lift forces on the test platform accurately, and accounts for wing-wing interaction effectively. Additionally, validation tests with real free-flight data show that lift forces can be predicted with considerable accuracy in different flight regimes. The complete parameter-varying model represents a wide range of flight conditions, is computationally simple, physically meaningful and requires few measurements. It is therefore potentially useful for both control design and preliminary conceptual studies for developing new platforms.

This paper presents the development of black-box linear state-space models for the flight dynamics of a flapping-wing micro aerial vehicle (FWMAV), the DelFly. The models were obtained by means of system identification techniques applied to flight data recorded in a motion tracking chamber and describe the time-averaged dynamics of the vehicle in the proximity of specific stationary points in forward flight. Ordinary least squares and maximum likelihood-based estimation approaches were applied in the time domain, and decoupled models were identified for the longitudinal and the lateral dynamics. The availability of several different datasets additionally allowed for validation and for the estimation and comparison among each other of several separate models. Adequate models were obtained for both the longitudinal and the lateral dynamics. These reproduce the estimation data well and are also capable of predicting the response to validation inputs with a reasonable degree of accuracy, thus allowing for a simulation of the DelFly near the stationary points considered. The identified dynamics are stable and thus in agreement with the observed behaviour of the DelFly in the considered flight regime.