Obstacle avoidance in aerial pursuit

Brighton, Caroline H.; Kempton, James A.; France, Lydia A; KleinHeerenbrink, Marco; Miñano, Sofía; Taylor, Graham K.

doi:10.1101/2023.01.23.525170

Cited by 4 publications

(9 citation statements)

References 27 publications

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“…This behaviour left them with a clearance of just over one wing length (Fig. 2A), consistent with results from Harris' hawks, which also aim for a point giving a clearance of just over one wing length when steering around obstacles (17).…”

Section: Discussionsupporting

confidence: 86%

Zebra finches use brightness cues for gap selection in flight

Borsier

Taylor

2023

Preprint

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Flying animals that are adapted to life in cluttered environments require effective and robust guidance mechanisms to avoid collisions. Birds and insects use optic flow cues to avoid obstacles when flying, but these are only generated by self-motion and are likely to be used in conjunction with other cues such as visual contrast between foreground and background objects. Orchid bees use a strategy based on relative brightness to find clear paths through dense environments. To investigate the possibility that birds use a similar strategy, we presented zebra finchesTaeniopygia guttatawith symmetric or asymmetric brightness distributions behind the aperture of a tunnel and recorded their flight through it. The background brightness conditions influenced both the birds' decision to enter the tunnel and their flight direction upon exit. Zebra finches were more likely to initiate flight through the tunnel if they could see a bright background through its aperture and were more likely to fly to the bright side on exiting the tunnel. We found no evidence of a centring response during gap negotiation; instead, the zebra finches entered the tunnel by turning tightly around its near edge. These results hint at a possible pre-planning of the trajectories before the onset of flight.

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Section: Discussionsupporting

confidence: 86%

Zebra finches use brightness cues for gap selection in flight

Borsier

Taylor

2023

Preprint

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“…L1-norm) in a complementary analysis of these data in the context of obstructed pursuit. ( Brighton et al, 2023 ). The L2 norm penalizes larger deviations to a greater extent than smaller deviations, so the use of the L1 norm was more robust to perturbations to the pursuit trajectories caused by the presence of obstacles in the latter study.…”

Section: Methodsmentioning

confidence: 99%

“…L1-norm) in a complementary analysis of these data in the context of obstructed pursuit. (Brighton et al, 2023).…”

Section: Simulations Of Guidance Behaviourmentioning

confidence: 99%

“…Here we describe a new data set comprising n=228 unobstructed pursuit trajectories flown by N=4 Harris’ hawks. The same data are used as baseline data elsewhere in an analysis of obstacle avoidance in obstructed pursuit Brighton et al (2023 ) utilising a slightly different fitting method (see Methods and Materials). Here we model these unobstructed pursuit data using the original PP, PN, and PNP guidance laws described above, together with new versions of the PN and PNP guidance laws in which the line-of-sight rate is defined with respect to the visual background behind the target.…”

Section: Introductionmentioning

confidence: 99%

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Visual versus visual-inertial guidance in hawks pursuing terrestrial targets

Kempton

Brighton

France

et al. 2022

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The flight behaviour of predatory birds is well modelled by a guidance law called proportional navigation, which commands steering in proportion to the angular rate of the line-of-sight from predator to prey. The line-of-sight rate is defined with respect to an inertial frame of reference, so proportional navigation is necessarily implemented using visual-inertial sensor fusion. In Harris' hawks, pursuit of terrestrial targets is even better modelled by assuming that visual-inertial information on the line-of-sight rate is combined with visual information on the deviation angle between the attacker's velocity and the line-of-sight. Here we ask whether a new variant of this mixed guidance law can model Harris' hawk pursuit behaviour successfully using visual information alone. We use high-speed motion capture to record n=228 attack flights from N=4 Harris' hawks, and confirm that proportional navigation and mixed guidance using visual-inertial information both model the trajectory data well. Moreover, the mixed guidance law still models the data closely if visual-inertial information on the line-of-sight rate is replaced with purely visual information on the apparent motion of the target relative to the background. Whilst the original form of the mixed guidance law provides the best model of the data, all three models can model the behavioural data phenomenologically, whilst making different predictions on the physiological pathways involved.

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“…Here we describe a new dataset comprising n = 228 unobstructed pursuit trajectories flown by N = 4 Harris’ hawks ( figure 2 ). The same data are used as baseline data elsewhere in an analysis of obstacle avoidance in obstructed pursuit [ 19 ] using a slightly different fitting method (see Methods and material). Here we model these unobstructed pursuit data using the original PP, PN and PNP guidance laws described above, together with new versions of the PN and PNP guidance laws in which the line-of-sight rate is defined with respect to the visual background behind the target.…”

Section: Introductionmentioning

confidence: 99%

Visual versus visual-inertial guidance in hawks pursuing terrestrial targets

Kempton

Brighton

France

et al. 2023

J. R. Soc. Interface.

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The aerial interception behaviour of falcons is well modelled by a guidance law called proportional navigation, which commands steering at a rate proportional to the angular rate of the line-of-sight from predator to prey. Because the line-of-sight rate is defined in an inertial frame of reference, proportional navigation must be implemented using visual-inertial sensor fusion. By contrast, the aerial pursuit behaviour of hawks chasing terrestrial targets is better modelled by a mixed guidance law combining information on the line-of-sight rate with information on the deviation angle between the attacker’s velocity and the line-of-sight. Here we ask whether this behaviour may be controlled using visual information alone. We use high-speed motion capture to record n = 228 flights from N = 4 Harris’ hawks Parabuteo unicinctus , and show that proportional navigation and mixed guidance both model their trajectories well. The mixed guidance law also models the data closely when visual-inertial information on the line-of-sight rate is replaced by visual information on the motion of the target relative to its background. Although the visual-inertial form of the mixed guidance law provides the closest fit, all three guidance laws provide an adequate phenomenological model of the behavioural data, whilst making different predictions on the physiological pathways involved.

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Obstacle avoidance in aerial pursuit

Cited by 4 publications

References 27 publications

Zebra finches use brightness cues for gap selection in flight

Zebra finches use brightness cues for gap selection in flight

Visual versus visual-inertial guidance in hawks pursuing terrestrial targets

Visual versus visual-inertial guidance in hawks pursuing terrestrial targets

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