Optical flow on a flapping wing robot

Bermudez, Fernando Garcia; Fearing, Ronald S.

doi:10.1109/iros.2009.5354337

Cited by 16 publications

(3 citation statements)

References 30 publications

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“…One small improvement of the DTA may be to not reset the evidence e to 0, reducing the influence of only a few τ RL measurements. As main directions for larger improvements of the obstacle avoidance capabilities, we identify the incorporation of onboard vision processing for reducing noise and allowing the direct feedback of gyro measurements (cf [55]) or the feedback current from the motors (as in [7]) for derotating optic flow. In addition, work on shorter turn radiuses of DelFly would also be of great help in confined spaces such as small office rooms.…”

Section: Discussionmentioning

confidence: 99%

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Design, aerodynamics and autonomy of the DelFly

et al. 2012

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One of the major challenges in robotics is to develop a fly-like robot that can autonomously fly around in unknown environments. In this paper, we discuss the current state of the DelFly project, in which we follow a top-down approach to ever smaller and more autonomous ornithopters. The presented findings concerning the design, aerodynamics and autonomy of the DelFly illustrate some of the properties of the top-down approach, which allows the identification and resolution of issues that also play a role at smaller scales. A parametric variation of the wing stiffener layout produced a 5% more power-efficient wing. An experimental aerodynamic investigation revealed that this could be associated with an improved stiffness of the wing, while further providing evidence of the vortex development during the flap cycle. The presented experiments resulted in an improvement in the generated lift, allowing the inclusion of a yaw rate gyro, pressure sensor and microcontroller onboard the DelFly. The autonomy of the DelFly is expanded by achieving (1) an improved turning logic to obtain better vision-based obstacle avoidance performance in environments with varying texture and (2) successful onboard height control based on the pressure sensor.

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Section: Discussionmentioning

confidence: 99%

“…The second limitation is that time-to-impact estimates rely on accurate optic flow measurements. Unfortunately, images made with flapping wing MAVs are challenging (cf [7]). Currently, a small camera onboard the DelFly is used that transmits its images analogically to a ground control station.…”

Section: Autonomy Of the Delflymentioning

confidence: 99%

Design, aerodynamics and autonomy of the DelFly

et al. 2012

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“…Nevertheless, due to the limitations of micromachining methods and the shortcomings of power systems, payloads and navigation technologies, artificial MAVs cannot fly as freely as real insects. (Bermudez & Fearing, 2009; Breugel et al., 2008; Whitney & Wood, 2010). They could only imitate the flying performance of insects to a certain extent, and were far from comparable with insects in terms of flight speed, perception of external conditions, energy supply, environmental adaptability, effective flight distance, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Behavioral control and changes in brain activity of honeybee during flapping

2021

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Introduction: Insect cyborg is a kind of novel robot based on insect-machine interface and principles of neurobiology. The key idea is to stimulate live insects by specific stimuli; thus, the flight trajectory of insects could be controlled as anticipated. However, the neuroregulatory mechanism of insect flight has not been elucidated completely at present. Methods:To explore the neuro-mechanism of insect flight behaviors, a series of electrical stimulation was applied on the optic lobes of semi-constrained honeybees. Times of flight initiation, flapping frequency, and duration were recorded by a high-speed camera. In addition, flapping and steering initiation experiments of the cyborg honeybee were verified. Moreover, series of local field potential signals of optic lobes during flapping were collected, pre-processed to remove baseline wander and DC components, then analyzed by power spectrum estimation.Results: A quantitative optimization method and optimal stimulation parameters of flight initiation were presented. Stimulation results showed that the flapping duration differed greatly while the flapping frequency varied with little difference among different individuals. Moreover, there was always a fluctuation peak around 20-30 Hz in power spectral density (PSD) curves during flapping, distinguishing from calm state, which indicated some brain activity changes during flapping. Conclusions:Our study presented a range of relatively optimal electrical parameters to initiate honeybee flight behavior. Meanwhile, the regularity of flapping duration and flapping frequency under electrical stimulations with different parameters were given.The feasibility of controlling a honeybee's flight behavior by brain electrical stimulation was verified through the flapping and steering initiation experiment of honeybees under semi-constrained state. PSD fluctuations reflected changes in brain activity during flapping and that those fluctuation characteristics at the specific frequency band could be sensitive determinants to distinguish whether the honeybee was flying or not, which benefits our understanding of honeybee's flapping behavior and furthers the study of honeybee cyborgs.

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