Current Biology Vol 17 No 4R124

Dispatches

Insect Behaviour: Controlling Flight Altitude withOptic Flow

Insects can smoothly control their height while flying by adjusting lift tomaintain a set-point in the ventral optic flow. The efficacy of this simpleflight-control mechanism has been demonstrated using a robothelicopter.

Barbara Webb

When trying to understand animalbehaviour it is common to thinkabout how information is extractedfrom the environment and aninternal representation of thatinformation used to calculate anappropriate controlled response.But this approach can bemisleading when we assume thatcertain information is required toperform a task, when in facta simpler solution, exploiting theclosed-loop interaction of theanimal with its environment,is available. Wehner [1] hasdescribed such solutions, wherethe cue required to perform thetask is directly picked up by theperceptual system, as ‘matchedfilters’. An example is discussed inthe article in this issue byFranceschini et al. [2].

The basic problem addressed byFranceschini et al. [2] is how a flyinginsect controls its height above theground, for example in take-off,landing and maintaining a constantheight above varying terrain. Doesthe insect measure its altitude, andif so, how? It could potentiallyextract height information from theventral optic flow: basically, thehigher it is, the slower will be theapparent motion of the grounddirectly below it. But to actuallyextract height information from thissignal, the insect would need toknow its true velocity relative to theground — its groundspeed — asthe ventral optic flow isproportional to its groundspeeddivided by its height.

In their paper, Franceschini et al.[2] describe a flight control solutionin which neither groundspeed noraltitude are explicitly determined.Instead, the directly available

cue — ventral optic flow — isused in a feedback control loop,with the insect altering its lift tomaintain a setpoint ventral opticflow, and thus a constantgroundspeed:height ratio. Thissimple mechanism has a number ofdesirable properties. If the animalincreases its forward speed, it willautomatically increase its height asit takes off. If it gradually decreasesspeed, it will gradually decreaseheight and thus land smoothly. Ifthe terrain rises, the ventral opticflow will increase and the insect willcompensate by increasing itsheight. If the insect is slowed bya headwind, it will descend; this isa strategy likely to reduce theheadwind, or even to lead theinsect to land if it cannot make anyprogress against the headwind.Franceschini et al. [2] describereports of each of these effectshaving been observed in insectflight. Most of these reports arequalitative — such as theobservation that bees flying overmirror-smooth water, whichprovides no ventral optic flow, maydescend so far that they end up inthe water and drown [3]. In the caseof landing, there are quantitativedata [4] which closely fit thepredictions of their model.

While such a scheme soundsintuitively plausible, Franceschiniet al. [2] have also shown that it canwork in practice by implementing iton a robotic helicopter. This usesjust two photoreceptors, coupledto produce an elementary motiondetector based on fly vision.Forward speed is altered bychanging the pitch of the helicopterrotor, and lift by changing therotor speed. This physical modelhas been designed to matchcharacteristics of insects such as

the angle between photoreceptors,the flight-speed range and opticalflow sensor range. It thus showsthat the controller is plausiblewithin the real physical constraintsknown to hold for insects.This approach — a bioroboticevaluation of a biologicalhypothesis — is becomingincreasingly popular. Recentexamples include roboticinvestigations of locomotion inanimals as diverse as waterstriders [5], salamanders [6] andhumans [7]. It allows modellers toclose the loop: that is, tounderstand how actions affect theinteraction with the world thatleads to sensory input; and tounderstand this as a dynamic looprather than as cause-and-effect. Itcan also close the loop frombiology to engineering byproducing new predictions andinsights that can be used inbiology [8].

Indeed, the idea of using opticflow directly to control behaviourhas been explored in a number ofother insect-inspired robots.Lateral optic flow can be used toregulate forward speed [9],possibly in a similar feedback loopto the lift control described above.Because lateral optic flow dependson the distance of the surroundingobjects, such a controller willadaptively slow the animal when itis traversing narrower apertures orslow a robot moving throughcluttered environments [10].Balancing the lateral optic flow oneach side also results in centring[10]. And expansion in the opticflow field is an indication ofimpending collision: by triggeringa response when the rate ofexpansion increase is above somethreshold, collisions can beavoided, again without anyexplicit detection of the actual sizeor distance of the object.Specialised neurons to detectthis signal have been described in

DispatchR125

the locust and modelled on a robot[11]. The same principle has beenused to enable a small free modelaircraft to avoid collisions withwalls [12].

Exploiting some aspect of opticflow directly to control locomotionis often discussed, followingGibson [13], as an example of an‘affordance’. The exact meaning ofthis term is subject to debate, butessentially it is the idea that whatanimals are designed to perceiveare opportunities for action, ratherthan action-neutral properties ofthe environment around them.Instead of seeing shape, size anddistance of an object, for example,we observe its graspability. Thisinfluential, and sometimescontroversial, view of perception isparticularly relevant to robotics,where specialised sensorysystems for cues such as opticflow have often proved more usefulthan conventional computervision. In one sense these may bethought of as tricks or short-cutsthat enable the animal or robot toavoid difficult measurementsand calculations. But the conceptof action-oriented perceptionmay also be important inunderstanding higher levelcognitive skills, as it stronglydetermines how we structure theworld around us.

For insect flight control, manyissues remain to be resolved.Ventral optic flow can be easilydetected if the animal is flyingstraight ahead and the sensor ispointing straight down. But if the

Speciation GenetiConflict and the O

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Daven C. Presgraves

Species are often reproductivelyisolated from one another by theintrinsic sterility or inviability oftheir hybrids. Darwin devoted anentire chapter of his Origin of

insect pitches, rolls or rotates,ventral optic flow will be distorted.Can the animal measure anddiscount these movements, or areother sensorimotor loops, such asthe optomotor reflex, deployedsimultaneously to minimise them[14]? Is there any evidence ofsystematic difference, for examplein the sensitivity range, ofelementary motion detectorspointing at different parts of thevisual field [15] that would fit withthe proposed difference in controlfunction? What exactly are thewing movements that need to becontrolled [16] and might thesealso be ‘matched’ to specificcontrol problems? Willunderstanding the basic controlrules help us to trace out the neuralpathways that support thisbehaviour? The combination ofbehavioural experiments androbot models is likely to be animportant tool in futurediscoveries.

References1. Wehner, R. (1987). ‘Matched filters’ —

neural models of the external world.J. Comp. Physiol. A. 161, 511–531.

2. Franceschini, N., Ruffier, F., and Serres, J.(2007). A bio-inspired flying robot shedslight on insect piloting abilities. Curr. Biol.17, 329–335.

3. Heran, P., and Lindauer, M. (1963).Windkompensation undSeitenwindkorrektur der Bienen beimFlug }Uber Wasser. Z. vergl. Physiol. 47,39–55.

4. Srinivasan, M.V., Zhang, S., Chahl, J.S.,Barth, E., and Venkatesh, S. (2000).How honeybees make grazing landingson flat surfaces. Biol. Cyb. 83, 171–183.

5. Hu, D.L., Chan, B., and Bush, J.W.M.(2003). The hydrodynamics of waterstrider locomotion. Nature 424,663–666.

cs: Epistasis,rigin of Species

ognized that the sterility andolve incompatible epistaticenes. The first pair of suchbeen identified.

Species [1] to hybrid sterilitybecause, being scrupulous, hewished to confront the possibleshortcomings of his theory headon: why would natural selection,which acts to increase individualfitness, cause the evolution of

6. Ijspeert, A.J., Crespi, A., andCabelguen, J.M. (2005). Simulation androbotics studies of salamanderlocomotion. Applying neurobiologicalprinciples to the control of locomotionin robots. Neuroinformatics 3,171–196.

7. Collins, S., Ruina, A., Tedrake, R., andWisse, M. (2005). Efficient bipedal robotsbased on passive dynamic walkers.Science 307, 1082–1085.

8. Webb, B. (2000). What does roboticsoffer animal behaviour? Anim. Behav. 60,545–558.

9. Baird, E., Srinivasan, M., Zhang, S., andCowling, A. (2005). Visual control of flightspeed in honeybees. J. Exp. Biol. 208,3895–3905.

10. Srinivasan, M.V., Chahl, J.S., Weber, K.,Nagle, M.G., and Zhang, S.W. (1999).Robot navigation inspired by principles ofinsect systems. Robotics Auton. Sys. 26,203–216.

11. Blanchard, M., Rind, F.C., andVerschure, P.F.M.J. (2000). Collisionavoidance using a model of the locustLGMD neuron. Robotics Auton. Sys. 30,17–38.

12. Zufferey, J.C., and Floreano, D. (2006).Fly-inspired visual steering of an ultralightindoor aircraft. IEEE Trans. Robotics 22,137–146.

13. Gibson, J.J. (1966). The sensesconsidered as perceptual systems(Boston: Houghton Mifflin).

14. Neumann, T.R. and Bulthoff, H.H., (2001).Insect inspired visual control oftranslatory flight. Proceedings ofAdvances in Artificial Life: 6th EuropeanConference, ECAL 2001, Prague, CzechRepublic, September 10–14.

15. Egelhaaf, M., Kern, R., and Krapp, H.G.(2002). Neural encoding ofbehaviourally relevant visual-motioninformation in the fly. Trends Neurosci.25, 96–102.

16. Dickinson, M.H. (2006). Insect flight. Curr.Biol. 16, 309–314.

Institute of Perception, Action andBehaviour, University of Edinburgh,JCMB, Kings Buildings, Mayfield Road,Edinburgh, Eh9 3JZ, UK.E-mail: Bwebb@inf.ed.ac.uk

DOI: 10.1016/j.cub.2006.12.008

hybrid sterility? As with manyproblems, Darwin struggled withthe genetic details but, in the end,got the basics right: hybrid sterility‘‘is not a specially endowedquality, but is incidental on otheracquired differences,’’ (p. 245)and is caused by a hybrid’s‘‘organization having beendisturbed by two organizationshaving been compounded intoone’’ (p. 266).

Fifty years would pass beforeBateson [2] and later Dobzhansky[3] and Muller [4,5] deviseda genetic model for the evolutionof such hybrid fitness problems