With the rise of new and innovative Urban Air Mobility solutions, there also arises a need to integrate these into the existing airspace. Current airspace users include conventional civil, commercial and general aviation, military air users, police and emergency services as well as a plethora of avian life. Planned additions to the airspace are electric vertical take-off and landing vehicles such as logistics drones and air taxis. The airspace for conventional users is stringently controlled. Urban Air Mobility operations are expected to mainly take place in individual corridors to be added to the currently uncontrolled low-level airspace. This airspace is also intended for various types of drone operations, out of which, small-scale drones can be non-co-operative. In addition, the operational altitudes of Urban Air Mobility aircraft will strongly expose them to birds. Due to abundance of these non-cooperating airspace users (like hobby-drones and birds), conflicts with Urban Air Mobility aircraft are expected to be inevitable. The aim of this paper is to develop a concept of Urban Air Mobility Collision Avoidance System to reduce the likelihood of collision between air taxis and non-cooperating airspace users. As such, this work proposes the introduction of an additional safety layer to prevent collisions during operations of strong exposure. The concept consists of a conflict detection and resolution method tailored for Urban Air Mobility operations. A three-dimensional safety envelope is designed using the geometric and performance values of the aircraft configurations currently available. Procedures to avoid conflicts prior to as well as during the flights are presented. Finally, the concept is visualized for the common use case of a shuttle service between an airport and a railway station. The results demonstrate the importance of incorporating individual aircraft configuration into conflict avoidance approach and report its effect to avoid collision.@en

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.

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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.

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The demand for always smaller, more manoeuvrable and versatile unmanned aerial vehicles cannot be met with conventional manned flight approaches. This has led engineers to seek inspiration in nature, giving rise to the bio-inspired flapping-wing micro aerial vehicle (FWMAV). FWMAVs achieve a remarkable flight performance at small scales, however their flight mechanics are extremely complex. This hinders the development of effective dynamic models, which are essential for simulation, design and advanced controller development, and would enhance the performance and autonomy of such vehicles. This thesis addresses the challenge of modelling flapping-wing dynamics, using free-flight and wind tunnel data, with the aim of devising new models that are both accurate and computationally simple enough for control and simulation applications. The research is based on a test vehicle, i.e. the DelFly, developed at TU Delft. To meet the stated objectives, two modelling approaches are developed. The first approach is based on free-flight system identification and yields time-varying grey-box state-space models of the full vehicle dynamics, covering different flight conditions. The second approach results in physically meaningful phenomenological models of the aerodynamics specifically, accounting for complex effects such as the clap-and-fling mechanism and the interaction between the unsteady wing wake and tail. In addition to the modelling, recommendations for effective FWMAV flight testing are put forth, and a sensor fusion method is developed to advantageously combine on-board sensor data with off-board motion tracking data. All the developed models are accurate and computationally inexpensive, and the approaches can be generalised to comparable FWMAVs. While each model is best suited for different applications, thanks to its specific properties, all the developed models pave the way for new work in design, simulation, and control of FWMAVs.@en

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.@en

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. @en

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. @en

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. @en

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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.@en

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.

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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@en

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. @en