Review of insect-inspired wing micro air vehicle

Song, Fa; Yan, Yongwei; Sun, Jiyu

doi:10.1016/j.asd.2022.101225

Cited by 10 publications

(10 citation statements)

References 115 publications

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“…The evolution of insect-inspired MAVs is discussed in several review papers, e.g. [36][37][38]. Progress on hover-capable, tailless, insect-like FWMAVs has for instance been reviewed by Phan et al [39,40] and by Xiao et al [41].…”

Section: Previous Reviews On Fwmavsmentioning

confidence: 99%

Landing and take-off capabilities of bioinspired aerial vehicles: a review

Hammad,

Armanini

2024

Bioinspir. Biomim.

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Bioinspired Flapping-Wing Micro Aerial Vehicles (FWMAVs) haveemerged over the last two decades as a promising new type of robot. Their high thrustto-weight ratio, versatility, safety, and maneuverability, especially at small scales,could make them more suitable than fixed-wing and multi-rotor vehicles for variousapplications, especially in cluttered, confined environments and in close proximity tohumans, flora, and fauna. Unlike natural flyers, however, most FWMAVs currentlyhave limited take-off and landing capabilities. Natural flyers are able to take offand land effortlessly from a wide variety of surfaces and in complex environments.Mimicking such capabilities on flapping-wing robots would considerably enhance theirpractical usage. This review presents an overview of take-off and landing techniques forFWMAVs, covering different approaches and mechanism designs, as well as dynamicsand control aspects. The special case of perching is also included. As well as discussingsolutions investigated for FWMAVs specifically, we also present solutions that havebeen developed for different types of robots but may be applicable to flapping-wingones. Different approaches are compared and their suitability for different applicationsand types of robots is assessed. Moreover, research and technology gaps are identified,and promising future work directions are identified.

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Section: Previous Reviews On Fwmavsmentioning

confidence: 99%

Landing and take-off capabilities of bioinspired aerial vehicles: a review

Hammad,

Armanini

2024

Bioinspir. Biomim.

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“…The distribution of beetle hindwing venation is a key feature for analyzing beetle flight performance, which is important for designing beetle-inspired micro aerial vehicles [44,45]. Hindwing landmarks can provide important information about the structural and functional features of beetle hindwings related to flight performance [12]; for example, the number and position of veins can affect the stiffness and elasticity of the hindwing, which consequently affects the lift and maneuverability of beetles [46,47].…”

Section: Entomological Significance Of Wing Landmark Detectionmentioning

confidence: 99%

“…By automatically detecting the landmarks of hindwing veins with the proposed approach, designing linkage mechanisms by simulating insect veins becomes a feasible solution. A rigid and flexible coupled bionic deployable wing may use linkage mechanisms as artificial veins according to the detected locations of joints to achieve flapping wing folding/unfolding and vein joints rotating [44]. Coordinates of landmarks can be obtained quickly with the proposed approach, and discriminant space of landmark-based wing venation geometric morphometrics can be used to distinguish morphologically similar species to explore intraspecific variation among populations and determine sexual dimorphism in various insects [3].…”

Section: Entomological Significance Of Wing Landmark Detectionmentioning

confidence: 99%

Detection of Hindwing Landmarks Using Transfer Learning and High-Resolution Networks

Yang

Liu

et al. 2023

Biology

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Hindwing venation is one of the most important morphological features for the functional and evolutionary analysis of beetles, as it is one of the key features used for the analysis of beetle flight performance and the design of beetle-like flapping wing micro aerial vehicles. However, manual landmark annotation for hindwing morphological analysis is a time-consuming process hindering the development of wing morphology research. In this paper, we present a novel approach for the detection of landmarks on the hindwings of leaf beetles (Coleoptera, Chrysomelidae) using a limited number of samples. The proposed method entails the transfer of a pre-existing model, trained on a large natural image dataset, to the specific domain of leaf beetle hindwings. This is achieved by using a deep high-resolution network as the backbone. The low-stage network parameters are frozen, while the high-stage parameters are re-trained to construct a leaf beetle hindwing landmark detection model. A leaf beetle hindwing landmark dataset was constructed, and the network was trained on varying numbers of randomly selected hindwing samples. The results demonstrate that the average detection normalized mean error for specific landmarks of leaf beetle hindwings (100 samples) remains below 0.02 and only reached 0.045 when using a mere three samples for training. Comparative analyses reveal that the proposed approach out-performs a prevalently used method (i.e., a deep residual network). This study showcases the practicability of employing natural images—specifically, those in ImageNet—for the purpose of pre-training leaf beetle hindwing landmark detection models in particular, providing a promising approach for insect wing venation digitization.

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“…Historically, insects have provided a significant source for bioinspiration in engineered technologies [3][4][5][6] . The precise tuning of their sensory systems results from their physical anatomy and efficient encoding mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Mechanosensory representation of wing deformations

Yarger,

Maeda,

Siwanowicz

et al. 2024

Preprint

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Efficient representation of structural deformations is crucial for monitoring the instantaneous state of biological structures. Insects’ ability to encode wing deformations during flight demonstrates a general morphological computing principle applicable across sensory systems in nature as well as engineered systems. To characterize how relevant features are encoded, we measured and modelled displacement and strain across dragonfly wing surfaces in tethered and free flight. Functional interpretations were supported by neuroanatomical maps, and ablation and perturbation experiments. We find that signal redundancy is reduced by non-random sensor distributions and that morphology limits the stimulus space such that sensory systems can monitor natural states with few sensors. Deviations from the natural states are detected by a flexible population of additional sensors with many distinguishable activation patterns.

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Review of insect-inspired wing micro air vehicle

Cited by 10 publications

References 115 publications

Landing and take-off capabilities of bioinspired aerial vehicles: a review

Landing and take-off capabilities of bioinspired aerial vehicles: a review

Detection of Hindwing Landmarks Using Transfer Learning and High-Resolution Networks

Mechanosensory representation of wing deformations

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