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Photos of the observed flyers and the tracking device. (A) A paragliding pilot and a bird of prey thermalling together. (B) Peregrine falcon with the GPS device on its back. (C) Schematic picture of the thermalling and gliding parts of the flights with the notations indicated.
Source publication
Gliding saves much energy, and to make large distances using only this form of flight represents a great challenge for both birds and people. The solution is to make use of the so-called thermals, which are localized, warmer regions in the atmosphere moving upwards with a speed exceeding the descent rate of bird and plane. Whereas birds use this te...
Context in source publication
Context 1
... of the MacCready Theory. In this theory the relation between the horizontal and the corresponding sinking speeds (the so-called gliding polar curve, p(x), characteristic for the given gliding object) is used. It is supposed that the climbing rate of the next thermal is known by the flyer, and no geographical effects are taken into account. We apply the following interpretation of the MacCready theory. The goal of the gliders is to make a given distance L AB (using both thermalling and gliding and not loosing height in average) during a time as short as possible. Thus, they intend to minimize the quantity (time) L AB 1/v xy − v z /(v xy v climb ) , where v xy , v z = p(v xy ) are the gliding horizontal and vertical velocities, and v climb denotes the climbing rate in the thermals (see Fig. 1 and SI Appendix Fig. 2). The optimal strategy is determined from equalling the derivative of this expression to zero, and in this way obtaining a relationship between the optimal v xy and v climb . This leads to the expression p(v xy ) − v climb v xy = dp(v xy ) dv ...
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Citations
... While man-made flying vehicles have made remarkable advancements, surpassing natural flyers in terms of effective payload, flight speed, and distance covered, the exceptional maneuverability, controllability, and stability demonstrated by natural flyers remain elusive for current human-engineered vehicles [6][7][8][9][10][11][12]. For instance, consider the supersonic aircraft SR-71 "Blackbird," capable of traveling near 3 Mach (~900 m/s) and covering approximately 32 body lengths per second. ...
... The exceptional maneuvering and flight characteristics exhibited by birds are primarily attributed to their wing structures, which continue to hold numerous captivating mysteries despite millions of years of evolution. Therefore, delving deeper into the understanding and exploration of the low-Reynoldsnumber aerodynamic mechanisms governing bird wings offers a practical path to surmount the current technical bottlenecks faced by MAVs [1,2,[6][7][8][9][10][11][12]. ...
To investigate the flow mechanism of feather-like rough airfoils based on swift wings, computational simulations were employed to explore their overall aerodynamic characteristics in comparison to equivalent smooth airfoils. The study focused on angles of attack ranging from 0° to 20° at low Reynolds numbers. The results reveal that the rough airfoil exhibits higher lift and lower drag compared to the smooth airfoil at moderate angles of attack ranging from 6° to 10°, resulting in significantly improved aerodynamic efficiency. Notably, at an angle of attack of 8°, the aerodynamic efficiency is increased by 19%. However, at angles of attack smaller than 6°, the increase in drag outweighs the increase in lift, leading to lower aerodynamic efficiency for the rough airfoil. Conversely, when the angle of attack exceeds 16°, both airfoils experience separated flow-dominated flow fields, resulting in comparable effective aerodynamic shapes and similar aerodynamic efficiencies. Furthermore, the study found that increasing the Reynolds number results in greater pressure differences in the flow field, leading to higher aerodynamic efficiency. These preliminary conclusions are valuable for elucidating the flight mechanisms of bird-feather-like wings and can inform the design or morphing design of bio-inspired micro aerial vehicles in the near future.
... Adopting the RB turbulent environment, Reddy et al. [18] numerically trained a glider to rise on thermals using reinforcement learning algorithms. The trained glider can ascend from low altitude to high altitude in a spiral form, which has a similar pattern to soaring birds in nature [19]. They analyzed the changes in the glider's flight strategy when the turbulent intensity varied. ...
We adopt the reinforcement learning algorithm to train the self-propelling agent migrating long-distance in a thermal turbulent environment. We choose the Rayleigh-Bénard turbulent convection cell with an aspect ratio (Γ, which is defined as the ratio between cell length and cell height) of 2 as the training environment. Our results showed that, compared to a naive agent that moves straight from the origin to the destination, the smart agent can learn to utilize the carrier flow currents to save propelling energy. We then apply the optimal policy obtained from the Γ=2 cell and test the smart agent migrating in convection cells with Γ up to 32. In a larger Γ cell, the dominant flow modes of horizontally stacked rolls are less stable, and the energy contained in higher-order flow modes increases. We found that the optimized policy can be successfully extended to convection cells with a larger Γ. In addition, the ratio of propelling energy consumed by the smart agent to that of the naive agent decreases with the increase of Γ, indicating more propelling energy can be saved by the smart agent in a larger Γ cell. We also evaluate the optimized policy when the agents are being released from the randomly chosen origin, which aims to test the robustness of the learning framework, and possible solutions to improve the success rate are suggested. This work has implications for long-distance migration problems, such as unmanned aerial vehicles patrolling in a turbulent convective environment, where planning energy-efficient trajectories can be beneficial to increase their endurance.
... Adopting the RB turbulent environment, Reddy et al. [18] numerically trained a glider to rise on thermals using reinforcement learning algorithms. The trained glider can ascend from low altitude to high altitude in a spiral form, which has a similar pattern to soaring birds in nature [19]. They analyzed the changes in the glider's flight strategy when the turbulent intensity varied. ...
We adopt the reinforcement learning algorithm to train the self-propelling agent migrating long-distance in a thermal turbulent environment. We choose the Rayleigh-B\'enard turbulent convection cell with an aspect ratio ($\Gamma$, which is defined as the ratio between cell length and cell height) of 2 as the training environment. Our results showed that, compared to a naive agent that moves straight from the origin to the destination, the smart agent can learn to utilize the carrier flow currents to save propelling energy. We then apply the optimal policy obtained from the $\Gamma=2$ cell and test the smart agent migrating in convection cells with $\Gamma$ up to 32. In a larger $\Gamma$ cell, the dominant flow modes of horizontally stacked rolls are less stable, and the energy contained in higher-order flow modes increases. We found that the optimized policy can be successfully extended to convection cells with a larger $\Gamma$. In addition, the ratio of propelling energy consumed by the smart agent to that of the naive agent decreases with the increase of $\Gamma$, indicating more propelling energy can be saved by the smart agent in a larger $\Gamma$ cell. We also evaluate the optimized policy when the agents are being released from the randomly chosen origin, which aims to test the robustness of the learning framework, and possible solutions to improve the success rate are suggested. This work has implications for long-distance migration problems, such as unmanned aerial vehicles patrolling in a turbulent convective environment, where planning energy-efficient trajectories can be beneficial to increase their endurance.
... Thermal soaring is a flight style in which birds repeatedly ascend with updrafts (convection currents) and then glide. If the bird knows the distance and updraft speed of successive thermals, the theory predicts an "optimal speed" that maximizes horizontal travel speed (45,46). However, in practice, large bird species employ a slower airspeed than this "optimal speed" (42). ...
The way goal-oriented birds adjust their travel direction and route in response to the wind significantly affects their travel costs. This is expected to be particularly pronounced in albatrosses, which employ a wind-dependent flight style called dynamic soaring. Dynamic soaring birds in situations without a definite goal, e.g. searching for prey, are known to preferentially fly with tail-to-side winds to increase the speed and search area. However, little is known about their reaction to wind when heading to a definite goal, such as returning to their nest. For example, returning tracks of albatrosses vary from beelines to zigzags similar to that of sailboats; however, there is no empirical test of whether the wind causes this variation. Here, based on the similar wind-dependent speed between albatrosses and sailboats, we tested whether the time-minimizing strategies used by yacht racers can explain the locomotion patterns of wandering albatrosses. We predicted that when the goal is located upwind or downwind, albatrosses should (i) deviate their travel directions from the goal on the microscale and (ii) increase the number of turns on the macroscale. Both hypotheses were supported by track data from albatrosses and racing yachts in the Southern Ocean confirming that albatrosses qualitatively employ the same strategy as yacht racers. Nevertheless, albatrosses did not strictly minimize their travel time, likely making their flight robust against wind fluctuations. Our study provides the first empirical evidence of tacking in albatrosses and demonstrates that man-made movement strategies provide a new perspective on the laws underlying wildlife movement.
... Most, if not all, surface-initiated vertical velocities in the atmosphere decay rapidly with height (<1000 m) or are modified by the condensation of water vapor, the formation of cloud and rain, or the release of latent heat [21]. The experiences of glider pilots have also been usefully drawn upon to describe bird flight and, in particular, to explain how birds gain initial height from their earth-bound habitat to reach near and distant locations [10,22,23]. In the course of extended migrations under varying atmospheric conditions and surface topography, the bird may use more than one of these modes of flight subsidy [5,24,25]. ...
One-second-processed three-dimensional position observations transmitted from an instrumented golden eagle were used to determine the detailed long-range flight behavior of the bird. Once elevated from the surface, the eagle systematically used atmospheric gravity waves, first to gain altitude, and then, in multiple sequential glides, to cover over 100 km with a minimum expenditure of its metabolic energy.
... Since then, gliders have tried to adjust their gliding speed to the expected thermal climb rate according to their polar curve. With the aid of measured polar curves, Akos et al. 6 found that there are relevant common features in the way that falcons and the world's leading paraglider pilots use thermals, which are also close to the optimal soring strategy predicted by MacCready's theory. To apply the above soaring strategy for an unmanned aerial vehicle (UAV) to take advantage of thermals, Allen and Lin 7 adopted autonomous soaring algorithms to detect and exploit thermals. ...
We present a numerical study of training a self-propelling agent to migrate in the unsteady flow environment. We control the agent to utilize the background flow structure by adopting the reinforcement learning algorithm to minimize energy consumption. We considered the agent migrating in two types of flows: one is simple periodical double-gyre flow as a proof-of-concept example, while the other is complex turbulent Rayleigh–Bénard convection as a paradigm for migrating in the convective atmosphere or the ocean. The results show that the smart agent in both flows can learn to migrate from one position to another while utilizing background flow currents as much as possible to minimize the energy consumption, which is evident by comparing the smart agent with a naive agent that moves straight from the origin to the destination. In addition, we found that compared to the double-gyre flow, the flow field in the turbulent Rayleigh–Bénard convection exhibits more substantial fluctuations, and the training agent is more likely to explore different migration strategies; thus, the training process is more difficult to converge. Nevertheless, we can still identify an energy-efficient trajectory that corresponds to the strategy with the highest reward received by the agent. These results have important implications for many migration problems such as unmanned aerial vehicles flying in a turbulent convective environment, where planning energy-efficient trajectories are often involved.
... It is important to emphasize that BASS receives no explicit information about the stimulus experienced by the animal; the extracted motifs therefore do not contain information about the precise stimulus-response map of the animal, but can reveal relevant qualitative aspects of the animal's behaviour when the sensory information is not known. As illustrated here with our simulation, BASS may discover the spiraling of a soaring bird [59] with no reference to what stimulus triggers those responses. More generally, BASS is applicable when characteristic patterns of behaviour are repeated on several occasions during a task. ...
Animals display characteristic behavioural patterns when performing a task, such as the spiraling of a soaring bird or the surge-and-cast of a male moth searching for a female. Identifying such recurring sequences occurring rarely in noisy behavioural data is key to understanding the behavioural response to a distributed stimulus in unrestrained animals. Existing models seek to describe the dynamics of behaviour or segment individual locomotor episodes rather than to identify the rare and transient sequences of locomotor episodes that make up the behavioural response. To fill this gap, we develop a lexical, hierarchical model of behaviour. We designed an unsupervised algorithm called “BASS” to efficiently identify and segment recurring behavioural action sequences transiently occurring in long behavioural recordings. When applied to navigating larval zebrafish, BASS extracts a dictionary of remarkably long, non-Markovian sequences consisting of repeats and mixtures of slow forward and turn bouts. Applied to a novel chemotaxis assay, BASS uncovers chemotactic strategies deployed by zebrafish to avoid aversive cues consisting of sequences of fast large-angle turns and burst swims. In a simulated dataset of soaring gliders climbing thermals, BASS finds the spiraling patterns characteristic of soaring behaviour. In both cases, BASS succeeds in identifying rare action sequences in the behaviour deployed by freely moving animals. BASS can be easily incorporated into the pipelines of existing behavioural analyses across diverse species, and even more broadly used as a generic algorithm for pattern recognition in low-dimensional sequential data.
... Since then, gliders have tried to adjust their gliding speed to the expected thermal climb rate according to their polar curve. With the aid of measured polar curves, Akos et al. 6 found that there are relevant common features in the way that falcons and the world's leading paraglider pilots use thermals, which are also close to the optimal soring strategy predicted by MacCready's theory. To apply the above soaring strategy for an unmanned aerial vehicle (UAV) to take advantage of thermals, Allen and Lin 7 adopted autonomous soaring algorithms to detect and exploit thermals. ...
We present a numerical study of training a self-propelling agent to migrate in the unsteady flow environment. We control the agent to utilize the background flow structure by adopting the reinforcement learning algorithm to minimize energy consumption. We considered the agent migrating in two types of flows: one is simple periodical double-gyre flow as a proof-of-concept example, while the other is complex turbulent Rayleigh-Bénard convec-tion as a paradigm for migrating in the convective atmosphere or the ocean. The results show that the smart agent in both flows can learn to migrate from one position to another while utilizing background flow currents as much as possible to minimize the energy consumption , which is evident by comparing the smart agent with a naive agent that moves straight from the origin to the destination. In addition, we found that compared to the double-gyre flow, the flow field in the turbulent Rayleigh-Bénard convection exhibits more substantial fluctuations, and the training agent is more likely to explore different migration strategies; thus, the training process is more difficult to converge. Nevertheless, we can still identify an energy-efficient trajectory that corresponds to the strategy with the highest reward received by the agent. These results have important implications for many migration problems such as unmanned aerial vehicles flying in a turbulent convective environment, where planning energy-efficient trajectories are often involved. a a) This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Xu et al., Phys. Fluids 34, 035117 (2022) and may be found at
... According to Pennycuick, Frigatebird (Fregata magnificens), which has a different wing morphology than other seabirds, perform a 12 m radius of turn with the 23.7°bank angle, and a crow vulture (Coragyps atratus) has a 17.1 m radius of turn with a bank angle of 24.7°, a brown pelican (Pelecanus occidentalis) reaches 18 m radius of turn with a 22.9°bank angle (Pennycuick, 1983). Similarly, according to the global positioning system (GPS) tracking of the white stork (Ciconia ciconia), the radius of turn is 22.3 m while the turning radius of the peregrine falcon (Falco peregrinus) is 20.2 m (Akos et al., 2008). The average bank angle of the Himalayan vulture (Gyps himalayensis) is 27°with a turning radius of 26 m, and for the red vulture (Sarcogyps calvus) the turning radius is 26.5 m with an average bank angle of 32° (Williams et al., 2018). ...
... These developments led to fully autonomous unmanned glider competitions were organized, reaching designs up to 3.5 h endurance and 70 km flight distance in UAVs limited to 5 kg (Edwards and Silberberg, 2010). When comparing the thermal selection of birds and the unmanned glider manufactured by NASA, the results show that unlike the UAV, birds choose more efficient thermals (Akos et al., 2008). ...
Purpose
Increasing endurance was a very appropriate subject for the biomimetic approach. The study aims to design and manufacture a long-lasting mini unmanned aerial vehicle (UAV) using active gliding and soaring.
Design/methodology/approach
The endurance of mini UAVs is limited by battery or fuel capacity, and it is not always possible to increase these energy sources due to the fuselage size. Long endurance aircraft are required in various areas such as silent environment and traffic monitoring or search and rescue. Literature research on bird flight performance conducted to determine design parameters. These parameters are used in the theoretical design of the UAV for optimization. Computational fluid dynamics simulation and flight tests of the UAV performed to figure out the success of the design.
Findings
For a mini UAV to be produced in this class, it has been observed that it is more accurate to examine birds instead of gliders due to the size similarity. The UAV design reaches a 27.5 L/D (Glide ratio) ratio in the theoretical approach. However, flight results approved max L/D ratio is around 25 at the sea level. This flight performance is enough to outperform in glide ratio of Wandering albatrosses.
Practical implications
Sailplanes are known as sport aircraft. However, recent projects focus on glider designs due to fuel efficiency and silent tracking. Stemme S-14 that carries a high-resolution camera is one of the examples of these projects. The unmanned glider design can lead to these implications in the UAVs at least during the stand-by period in the air. Thanks to low weight, UAVs do not require strong thermals, which allows flying almost all over the world.
Originality/value
Researchers generally focus on increasing the battery capacity or the performance of the UAV. However, this study’s concentration is to increase the flight duration of the UAV by using geographical currents. For this purpose, taking advantage of bird morphology is quite a new topic. Also, glider type designs are rarely found in the field.
... Thus, drones can use wind power to glide unpowered as birds do to improve their range. 9 As thermal updraft exists over a wide range on Earth and is a wind field that can be skillfully used by human pilots, 10 this paper focuses on static soaring. ...
Unmanned Aerial Vehicles (UAVs) can extract energy to improve their range and endurance due to thermal updrafts in the environment via static soaring. In this paper, a soaring control strategy is improved based on multi-hole probe technology. A vertical wind speed estimation method based on the multi-hole probe is proposed to design thermal updraft identification, mode switching logic, and the soaring controller. Turn logic is used to determine the correct turn direction for UAVs when a thermal updraft is detected. A simulation environment containing a six-degree-of-freedom glider model and a thermal updraft model is described. The induced roll effect caused by the asymmetric vertical velocity distribution of thermal updraft is included in the simulations. The results show an improved soaring capability from the proposed soaring strategy. Thus, the proposed method based on a multi-hole probe is more accurate and robust, and the turn logic allows a glider to enter a thermal updraft more quickly. The more energy a glider can obtain from thermal updraft enables a lower soaring speed during static soaring.
