Fixed-Wing Unmanned Aircraft In-Flight Pitch and Yaw Control Moment Sensing

Yeo, Derrick; Atkins, Ella M.; Bernal, Luis P.; Shyy, Wei

doi:10.2514/1.c032682

Cited by 10 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Learning to minimize stall distance on the fly requires aeromechanical information (for example, from feather 1 , 2 or muscle 39 proprioceptors) and distance information (for example, from static visual 5 or optic flow 7 , 11 , 40 cues) to be combined. Fly-by-feel concepts 41 – 45 may therefore prove critical to the learning and control of perching in autonomous vehicles. Moreover, it seems likely that our birds would have learned the optimized position of the transition point as a virtual target for trajectory control, analogous to the ‘entry gate’ approach adopted in one recent implementation of autonomous perching 18 .…”

Section: Discussionmentioning

confidence: 99%

Optimization of avian perching manoeuvres

KleinHeerenbrink

France

Brighton

et al. 2022

Nature

View full text Add to dashboard Cite

Perching at speed is among the most demanding flight behaviours that birds perform1,2 and is beyond the capability of most autonomous vehicles. Smaller birds may touch down by hovering3–8, but larger birds typically swoop up to perch1,2—presumably because the adverse scaling of their power margin prohibits hovering9 and because swooping upwards transfers kinetic to potential energy before collision1,2,10. Perching demands precise control of velocity and pose11–14, particularly in larger birds for which scale effects make collisions especially hazardous6,15. However, whereas cruising behaviours such as migration and commuting typically minimize the cost of transport or time of flight16, the optimization of such unsteady flight manoeuvres remains largely unexplored7,17. Here we show that the swooping trajectories of perching Harris’ hawks (Parabuteo unicinctus) minimize neither time nor energy alone, but rather minimize the distance flown after stalling. By combining motion capture data from 1,576 flights with flight dynamics modelling, we find that the birds’ choice of where to transition from powered dive to unpowered climb minimizes the distance over which high lift coefficients are required. Time and energy are therefore invested to provide the control authority needed to glide safely to the perch, rather than being minimized directly as in technical implementations of autonomous perching under nonlinear feedback control12 and deep reinforcement learning18,19. Naive birds learn this behaviour on the fly, so our findings suggest a heuristic principle that could guide reinforcement learning of autonomous perching.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimization of avian perching manoeuvres

KleinHeerenbrink

France

Brighton

et al. 2022

Nature

View full text Add to dashboard Cite

show abstract

“…Because the aerodynamic forces that perturb flight can be sensed before any measurable disturbance to the kinematic state of the system has had time to evolve, force feedback has a lower latency than state feedback 40,41 . This is a key attraction of fly-by-feel concepts in experimental autonomous vehicles 42 , which have demonstrated some notable successes in gust rejection 43,44 . Our modelling hints at an unexpected role for force feedback in perching, analogous to the role of accelerometer feedback in coordinating turns in high-performance aircraft 45 .…”

Section: Minimizing Either Time or Energy Requires Very High Lift Coefficientsmentioning

confidence: 98%

“…Controlling these optimized parameter settings in open loop would be susceptible to external disturbances and internal error, but muscle power output is expected to be regulated locally under strain rate feedback from the muscle spindle cells 39 , and the lift force communicated to the body 41 may be regulated locally under force feedback from the muscle Golgi tendon organs 39 . Analogous “fly-by-feel” concepts 42 have demonstrated some notable successes in gust rejection in small air vehicles 43,44 , and may therefore prove important to controlling perching given the low-latency feedback that they provide 45,46 . Furthermore, given the physical significance of the transition point, which was always located at approximately 60% of perch spacing distance, it seems plausible that this learned optimum could have served as a virtual target for closed-loop trajectory control under visual feedback.…”

mentioning

confidence: 99%

Optimization of avian perching manoeuvres

KleinHeerenbrink

Brighton

Taylor

2021

Preprint

View full text Add to dashboard Cite

Flight is the most energetically costly activity that animals perform, making its optimisation crucial to evolutionary fitness. Steady flight behaviours like migration and commuting are adapted to minimise cost-of-transport or time-of-flight, but the optimisation of unsteady flight behaviours is largely unexplored. Unsteady manoeuvres are important in attack, evasion, and display, and ubiquitous during take-off and landing. Whereas smaller birds may touchdown slowly by flapping, larger birds swoop upward to perch - presumably because adverse scaling of their power margin prohibits slow flapping flight, and because swooping transfers excess kinetic to potential energy. Landing is especially risky in larger birds and entails reaching the perch with appropriate velocity and pose, but it is unknown how this challenging behaviour is optimised. Here we show that Harris' hawks Parabuteo unicinctus minimise neither time nor energy when swooping between perches for food, but instead minimise the gap they must close under hazardous post-stall conditions. By combining high-speed motion capture of 1,592 flights with dynamical modelling and numerical optimization, we found that the birds' choice of where to transition from powered dive to unpowered climb minimised the distance from the perch at which they stalled. Time and energy are therefore invested to maintain the control authority necessary to execute a safe landing, rather than being minimized continuously as they have been in applications of autonomous perching under nonlinear feedback control and deep reinforcement learning. Naïve birds acquire this behaviour through end-to-end learning, so penalizing stall distance using machine learning may provide robustness in autonomous systems.

show abstract

“…Some modern flying vehicles already use arrays of lowbandwidth anemometers for flow sensing [16,82,83]. These sensor arrays can be, for example, shear stress sensors embedded in a flexible skin [84], pressure ports distributed over the body or wings [56,[84][85][86], or hot-film sensors distributed over the wing [87]. Some sensors have narrow frequency responses, like the microelectromechanical system (MEMS) sensor array modelled after cricket hairs (peak response at 75 Hz [88]).…”

Section: Convergence Is Accelerated By Multiple Lowbandwidth Anemometersmentioning

confidence: 99%

Adaptive control of turbulence intensity is accelerated by frugal flow sampling

Quinn¹,

Halder²,

Lentink³

2017

J. R. Soc. Interface.

View full text Add to dashboard Cite

The aerodynamic performance of vehicles and animals, as well as the productivity of turbines and energy harvesters, depends on the turbulence intensity of the incoming flow. Previous studies have pointed at the potential benefits of active closed-loop turbulence control. However, it is unclear what the minimal sensory and algorithmic requirements are for realizing this control. Here we show that very low-bandwidth anemometers record sufficient information for an adaptive control algorithm to converge quickly. Our online Newton-Raphson algorithm tunes the turbulence in a recirculating wind tunnel by taking readings from an anemometer in the test section. After starting at 9% turbulence intensity, the algorithm converges on values ranging from 10% to 45% in less than 12 iterations within 1% accuracy. By down-sampling our measurements, we show that very-low-bandwidth anemometers record sufficient information for convergence. Furthermore, down-sampling accelerates convergence by smoothing gradients in turbulence intensity. Our results explain why low-bandwidth anemometers in engineering and mechanoreceptors in biology may be sufficient for adaptive control of turbulence intensity. Finally, our analysis suggests that, if certain turbulent eddy sizes are more important to control than others, frugal adaptive control schemes can be particularly computationally effective for improving performance.

show abstract

Fixed-Wing Unmanned Aircraft In-Flight Pitch and Yaw Control Moment Sensing

Cited by 10 publications

References 34 publications

Optimization of avian perching manoeuvres

Optimization of avian perching manoeuvres

Optimization of avian perching manoeuvres

Adaptive control of turbulence intensity is accelerated by frugal flow sampling

Contact Info

Product

Resources

About