Visual Control of Altitude in Flying Drosophila

Flies and other insects use vision to regulate their groundspeed in flight, enabling them to fly in varying wind conditions. Compared with mechanosensory modalities, however, vision requires a long processing delay (~100 ms) that might introduce instability if operated at high gain. Flies also sense air motion with their antennae, but how this is used in flight control is unknown. We manipulated the antennal function of fruit flies by ablating their aristae, forcing them to rely on vision alone to regulate groundspeed. Arista-ablated flies in flight exhibited significantly greater groundspeed variability than intact flies. We then subjected them to a series of controlled impulsive wind gusts delivered by an air piston and experimentally manipulated antennae and visual feedback. The results show that an antenna-mediated response alters wing motion to cause flies to accelerate in the same direction as the gust. This response opposes flying into a headwind, but flies regularly fly upwind. To resolve this discrepancy, we obtained a dynamic model of the fly's velocity regulator by fitting parameters of candidate models to our experimental data. The model suggests that the groundspeed variability of arista-ablated flies is the result of unstable feedback oscillations caused by the delay and high gain of visual feedback. The antenna response drives active damping with a shorter delay (~20 ms) to stabilize this regulator, in exchange for increasing the effect of rapid wind disturbances. This provides insight into flies' multimodal sensory feedback architecture and constitutes a previously unknown role for the antennae.stability | sensory fusion | feedback delay | system identification | turbulence A nimals rely on input from multiple sensory modalities to regulate their motor actions. For example, to quickly grasp an object, a human uses both vision and tactile sensing. The visual system can estimate how close the object is, but only touch can accurately determine when contact is made (1). Similarly, flying insects rely on multiple senses, including vision and mechanosensation to control their flight (2). This multimodal feedback enables them to perform aerial feats, such as chasing conspecifics (3) and rapid self-righting after takeoff (4). Our neurobiological and biomechanical understanding of these behaviors is incomplete, but physiological studies and physics-based models have helped reveal salient features (5-9).The flight paths of flies often are structured into bouts of straight segments of forward motion, punctuated by rapid changes in heading termed body saccades (3,(10)(11)(12). During the straight segments, flies tend to maintain constant groundspeed despite changes in wind speed, suggesting the presence of an active feedback regulator (13,14). Recent results provide some insight into the properties of this vision-based forward velocity controller, such as its dependence on the spatial and temporal frequency of the visual stimulus, and the magnitude of the underlying sensory-motor delay (15,16). Experiments w...

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“…All experiments were performed in a 150 cm × 30 cm × 30 cm wind tunnel (27,28) equipped with a visual projection system (Fig. 1A).…”

Section: Methodsmentioning

confidence: 99%

Flying Drosophila stabilize their vision-based velocity controller by sensing wind with their antennae

Fuller

Straw

Peek

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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“…To safely fly in cluttered environments, insects instead rely on image motion, also known as optic flow 38,39 , generated by their own displacement relative to the surroundings 40 . It has been experimentally shown that their neural system reacts to optic flow patterns 41,42 to produce a large variety of flight capabilities, such as obstacle avoidance 40,43 , speed maintenance 44 , odometry estimation 45 , wall following and corridor centring 46 , altitude regulation 47,48 , orientation control 49 and landing 50,51 . Optic flow intensity is proportional to the distance from objects only during translational movements, but not during rotational movements when it is proportional to the rotational velocity of the agent.…”

Section: Review Insightmentioning

confidence: 99%

Science, technology and the future of small autonomous drones

Floreano

Wood

2015

Nature

1,050

552

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Drones, a popular nickname for unmanned aerial vehicles and micro aerial vehicles, often conjure up images of unmanned aeroplanes that fly thousands of miles for espionage and to deploy munitions. However, over the past few years, an increasing number of public and private research laboratories have been working on small, human-friendly drones that one day may autonomously fly in confined spaces and in close proximity to people. The development of these small drones, which is the main focus of this Review, has been supported by the miniaturization and cost reduction of electronic components (microprocessors, sensors, batteries and wireless communication units), largely driven by the portable electronic device industry. These improvements have enabled the prototyping and commercialization of small (typically less than 1 kg) drones at smartphone prices.Small drones will have important socio-economic impacts (Fig. 1). Images from drones that are capable of flying a few metres above the ground will fill a gap between expensive, weather-dependent and lowresolution images provided by satellites and car-based images limited to human-level perspectives and the availability of accessible roads. Specialized flying cameras and cloud-based data analytics will allow farmers to continuously monitor the quality of crop growth. Such platforms will enable construction companies to measure work progress in real time. Drones will let mining companies obtain precise volumetric data of excavations. Energy and infrastructure companies will be able to exhaustively survey pipelines, roads and cables. Humanitarian organizations could immediately assess and adapt aid efforts in continuously changing refugee camps. Transportation drones that are capable of safely taking off and landing in the proximity of buildings and humans will allow developing countries -without a suitable road network -to rapidly deliver goods and to finally unleash the full potential of their e-commerce telecommunication infrastructure. Transportation drones will also help developed countries to improve the quality of service in congested or remote areas, and will enable rescue organizations to quickly deliver medical supplies in the field and on demand. Inspection drones that are capable of flying in confined spaces will help fire-fighting and emergency units to assess dangers faster and more safely, logistic companies to detect cracks in the inner and outer shells of ships, road maintenance companies to measure signs of wear and tear in bridges and tunnels, security companies to improve building safety by monitoring areas outside the range of surveillance cameras, and disaster mitigation agencies to inspect partially collapsed buildings where ground clutter is an obstacle for terrestrial robots. Coordinated teams of autonomous drones will enable missions that last longer than the flight time of a single drone by allowing some drones to temporarily leave the team for battery replacement. Drone teams will permit rescue organizations to quickly deploy dedicated commu...

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“…Real-time tracking is performed as images are acquired, removing the need for storing large amounts of digital information [17][18][19] and allowing researchers to influence the animal's environment in real time through virtual reality, robotics, or other dynamical stimulus regimes [22][23][24]. Even under controlled laboratory conditions with small numbers of individuals, automated image-based tracking is a difficult computer vision problem.…”

Section: Automated Image-based Trackingmentioning

confidence: 99%

Automated image-based tracking and its application in ecology

Dell

Bender²,

Branson

et al. 2014

Trends in Ecology & Evolution

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449

484

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The behavior of individuals determines the strength and outcome of ecological interactions, which drive population, community, and ecosystem organization. Bio-logging, such as telemetry and animal-borne imaging, provides essential individual viewpoints, tracks, and life histories, but requires capture of individuals and is often impractical to scale. Recent developments in automated image-based tracking offers opportunities to remotely quantify and understand individual behavior at scales and resolutions not previously possible, providing an essential supplement to other tracking methodologies in ecology. Automated image-based tracking should continue to advance the field of ecology by enabling better understanding of the linkages between individual and higher-level ecological processes, via high-throughput quantitative analysis of complex ecological patterns and processes across scales, including analysis of environmental drivers. Measuring behaviorIndividual behavior (see Glossary) underlies almost all aspects of ecology [1][2][3][4][5]. Accurate and highly resolved behavioral data are therefore critical for obtaining a mechanistic and predictive understanding of ecological systems [5]. Historically, direct observation by trained biologists was used to quantify behavior [6,7]. However, the extent and resolution to which direct observations can be made is highly constrained [8] and the number of individuals that can be observed simultaneously is small. In addition, an exact record of events is not preserved, only the biologist's subjective account of them.Recent technological advances in tracking now make it possible to collect large amounts of highly precise and accurate behavioral data. For many organisms equipment can be attached that provide information about the Glossary Background subtraction: a method used by software to compare the current video frame with a stored picture of the background; any pixel of the current frame that is significantly different from the corresponding pixel in the background is likely to be associated with the body of an animal. Useful in situations where the background is unchanging, for example, when the surface of the background is rigid and lighting does not change. Behavior: the actions of individuals, often in response to stimuli. Behavior can involve movement of the individual's body through space, such as walking or chasing, or can occur while the animal is stationary, such as grooming or eating. Bio-logging: attachment or implantation of equipment to organisms to provide information about their identity, location, behavior, or physiology (e.g., global positioning systems, accelerometers, video cameras, telemetry tags). Ecological interaction: any interaction between an organism and its environment, or between two organisms (i.e., including interactions between conspecifics). Fingerprinting: a method used to identify unmarked individuals using natural variation in their physical and/or behavioral appearance. The method works by transforming the images of each individual ...

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Visual Control of Altitude in Flying Drosophila

Cited by 85 publications

References 42 publications

Flying Drosophila stabilize their vision-based velocity controller by sensing wind with their antennae

Flying Drosophila stabilize their vision-based velocity controller by sensing wind with their antennae

Science, technology and the future of small autonomous drones

Automated image-based tracking and its application in ecology

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