A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes

Bertrand O, Lindemann JP, Egelhaaf M (2015)
PLoS Computational Biology 11(11): e1004339.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Abstract / Bemerkung
Avoiding collisions is one of the most basic needs of any mobile agent, both biological and technical, when searching around or aiming toward a goal. We propose a model of collision avoidance inspired by behavioral experiments on insects and by properties of optic flow on a spherical eye experienced during translation, and test the interaction of this model with goal-driven behavior. Insects, such as flies and bees, actively separate the rotational and translational optic flow components via behavior, i.e. by employing a saccadic strategy of flight and gaze control. Optic flow experienced during translation, i.e. during intersaccadic phases, contains information on the depth-structure of the environment, but this information is entangled with that on self-motion. Here, we propose a simple model to extract the depth structure from translational optic flow by using local properties of a spherical eye. On this basis, a motion direction of the agent is computed that ensures collision avoidance. Flying insects are thought to measure optic flow by correlation-type elementary motion detectors. Their responses depend, in addition to velocity, on the texture and contrast of objects and, thus, do not measure the velocity of objects veridically. Therefore, we initially used geometrically determined optic flow as input to a collision avoidance algorithm to show that depth information inferred from optic flow is sufficient to account for collision avoidance under closed-loop conditions. Then, the collision avoidance algorithm was tested with bio-inspired correlation-type elementary motion detectors in its input. Even then, the algorithm led successfully to collision avoidance and, in addition, replicated the characteristics of collision avoidance behavior of insects. Finally, the collision avoidance algorithm was combined with a goal direction and tested in cluttered environments. The simulated agent then showed goal-directed behavior reminiscent of components of the navigation behavior of insects.
Erscheinungsjahr
2015
Zeitschriftentitel
PLoS Computational Biology
Band
11
Ausgabe
11
Art.-Nr.
e1004339
ISSN
1553-734X
eISSN
1553-7358
Finanzierungs-Informationen
Open-Access-Publikationskosten wurden durch die Deutsche Forschungsgemeinschaft und die Universität Bielefeld gefördert.
Page URI
https://pub.uni-bielefeld.de/record/2786491

Zitieren

Bertrand O, Lindemann JP, Egelhaaf M. A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes. PLoS Computational Biology. 2015;11(11): e1004339.
Bertrand, O., Lindemann, J. P., & Egelhaaf, M. (2015). A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes. PLoS Computational Biology, 11(11), e1004339. doi:10.1371/journal.pcbi.1004339
Bertrand, Olivier, Lindemann, Jens Peter, and Egelhaaf, Martin. 2015. “A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes”. PLoS Computational Biology 11 (11): e1004339.
Bertrand, O., Lindemann, J. P., and Egelhaaf, M. (2015). A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes. PLoS Computational Biology 11:e1004339.
Bertrand, O., Lindemann, J.P., & Egelhaaf, M., 2015. A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes. PLoS Computational Biology, 11(11): e1004339.
O. Bertrand, J.P. Lindemann, and M. Egelhaaf, “A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes”, PLoS Computational Biology, vol. 11, 2015, : e1004339.
Bertrand, O., Lindemann, J.P., Egelhaaf, M.: A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes. PLoS Computational Biology. 11, : e1004339 (2015).
Bertrand, Olivier, Lindemann, Jens Peter, and Egelhaaf, Martin. “A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes”. PLoS Computational Biology 11.11 (2015): e1004339.
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5 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

The role of optic flow pooling in insect flight control in cluttered environments.
Lecoeur J, Dacke M, Floreano D, Baird E., Sci Rep 9(1), 2019
PMID: 31118454
The problem of home choice in skyline-based homing.
Müller MM, Bertrand OJN, Differt D, Egelhaaf M., PLoS One 13(3), 2018
PMID: 29522546
Spiking Elementary Motion Detector in Neuromorphic Systems.
Milde MB, Bertrand OJN, Ramachandran H, Egelhaaf M, Chicca E., Neural Comput 30(9), 2018
PMID: 30021082
Local motion adaptation enhances the representation of spatial structure at EMD arrays.
Li J, Lindemann JP, Egelhaaf M., PLoS Comput Biol 13(12), 2017
PMID: 29281631

54 References

Daten bereitgestellt von Europe PubMed Central.

Three-dimensional reconstruction of brain-wide wiring networks in Drosophila at single-cell resolution.
Chiang AS, Lin CY, Chuang CC, Chang HM, Hsieh CH, Yeh CW, Shih CT, Wu JJ, Wang GT, Chen YC, Wu CC, Chen GY, Ching YT, Lee PC, Lin CY, Lin HH, Wu CC, Hsu HW, Huang YA, Chen JY, Chiang HJ, Lu CF, Ni RF, Yeh CY, Hwang JK., Curr. Biol. 21(1), 2010
PMID: 21129968
Absolute anzahl und verteilung der zellen im him der honigbiene
AUTHOR UNKNOWN, 1967
Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action.
Egelhaaf M, Boeddeker N, Kern R, Kurtz R, Lindemann JP., Front Neural Circuits 6(), 2012
PMID: 23269913
A 3D laser range finder for autonomous mobile robots
AUTHOR UNKNOWN, 2001

AUTHOR UNKNOWN, 1981
Stabilizing gaze in flying blowflies.
Schilstra C, van Hateren JH., Nature 395(6703), 1998
PMID: 9790186
Blowfly flight and optic flow. I. Thorax kinematics and flight dynamics
Schilstra C, Hateren JH., J. Exp. Biol. 202 (Pt 11)(), 1999
PMID: 10229694
Blowfly flight and optic flow. II. Head movements during flight
Hateren JH, Schilstra C., J. Exp. Biol. 202 (Pt 11)(), 1999
PMID: 10229695
The fine structure of honeybee head and body yaw movements in a homing task.
Boeddeker N, Dittmar L, Sturzl W, Egelhaaf M., Proc. Biol. Sci. 277(1689), 2010
PMID: 20147329
Identifying prototypical components in behaviour using clustering algorithms.
Braun E, Geurten B, Egelhaaf M., PLoS ONE 5(2), 2010
PMID: 20179763
A syntax of hoverfly flight prototypes.
Geurten BR, Kern R, Braun E, Egelhaaf M., J. Exp. Biol. 213(Pt 14), 2010
PMID: 20581276
Blowfly flight characteristics are shaped by environmental features and controlled by optic flow information.
Kern R, Boeddeker N, Dittmar L, Egelhaaf M., J. Exp. Biol. 215(Pt 14), 2012
PMID: 22723490
Gaze strategy in the free flying zebra finch (Taeniopygia guttata).
Eckmeier D, Geurten BR, Kress D, Mertes M, Kern R, Egelhaaf M, Bischof HJ., PLoS ONE 3(12), 2008
PMID: 19107185
Autokorrelationsauswertung als Funktionsprinzip des Zentralnervensystems
AUTHOR UNKNOWN, 1957
Saccadic flight strategy facilitates collision avoidance: closed-loop performance of a cyberfly.
Lindemann JP, Weiss H, Moller R, Egelhaaf M., Biol Cybern 98(3), 2008
PMID: 18180948
Visual guidance of a mobile robot equipped with a network of selfmotion sensors
AUTHOR UNKNOWN, 1990

AUTHOR UNKNOWN, 0

AUTHOR UNKNOWN, 0
Biomimetic Autopilot Based on Minimalistic Motion Vision for Navigating along Corridors Comprising U-shaped and S-shaped Turns
AUTHOR UNKNOWN, 2015
Facts on optic flow.
Koenderink JJ, van Doorn AJ., Biol Cybern 56(4), 1987
PMID: 3607100
Real-time robot navigation in unstructured environments using a 3D laser rangefinder. In: Intelligent Robots and Systems, 1997. IROS’97
AUTHOR UNKNOWN, 1997

AUTHOR UNKNOWN, 0
The vector field histogram-fast obstacle avoidance for mobile robots
AUTHOR UNKNOWN, 1991
Contrast-independent biologically inspired motion detection.
Babies B, Lindemann JP, Egelhaaf M, Moller R., Sensors (Basel) 11(3), 2011
PMID: 22163800
Robust models for optic flow coding in natural scenes inspired by insect biology.
Brinkworth RS, O'Carroll DC., PLoS Comput. Biol. 5(11), 2009
PMID: 19893631
Velocity constancy and models for wide-field visual motion detection in insects.
Shoemaker PA, O'Carroll DC, Straw AD., Biol Cybern 93(4), 2005
PMID: 16151841
On the computations analyzing natural optic flow: quantitative model analysis of the blowfly motion vision pathway.
Lindemann JP, Kern R, van Hateren JH, Ritter H, Egelhaaf M., J. Neurosci. 25(27), 2005
PMID: 16000634
Binocular integration of visual information: a model study on naturalistic optic flow processing.
Hennig P, Kern R, Egelhaaf M., Front Neural Circuits 5(), 2011
PMID: 21519385
Transient and steady-state response properties of movement detectors.
Egelhaaf M, Borst A., J Opt Soc Am A 6(1), 1989
PMID: 2921651
Adaptation of response transients in fly motion vision. II: Model studies.
Borst A, Reisenman C, Haag J., Vision Res. 43(11), 2003
PMID: 12726836

AUTHOR UNKNOWN, 0
The free-flight response of Drosophila to motion of the visual environment.
Mronz M, Lehmann FO., J. Exp. Biol. 211(Pt 13), 2008
PMID: 18552291
Bio-inspired visual ego-rotation sensor for MAVs.
Plett J, Bahl A, Buss M, Kuhnlenz K, Borst A., Biol Cybern 106(1), 2012
PMID: 22350507

AUTHOR UNKNOWN, 0
Minimum viewing angle for visually guided ground speed control in bumblebees.
Baird E, Kornfeldt T, Dacke M., J. Exp. Biol. 213(Pt 10), 2010
PMID: 20435812
Event-Based Visual Flow
AUTHOR UNKNOWN, 2014
The architecture of the desert ant’s navigational toolkit (Hymenoptera: Formicidae)
AUTHOR UNKNOWN, 2009
Desert ant navigation: how miniature brains solve complex tasks.
Wehner R., J. Comp. Physiol. A Neuroethol. Sens. Neural. Behav. Physiol. 189(8), 2003
PMID: 12879352
Looking and homing: how displaced ants decide where to go.
Zeil J, Narendra A, Sturzl W., Philos. Trans. R. Soc. Lond., B, Biol. Sci. 369(1636), 2014
PMID: 24395961
Spontaneous formation of multiple routes in individual desert ants (Cataglyphis velox)
AUTHOR UNKNOWN, 2012
A model of ant route navigation driven by scene familiarity.
Baddeley B, Graham P, Husbands P, Philippides A., PLoS Comput. Biol. 8(1), 2012
PMID: 22241975

AUTHOR UNKNOWN, 1992
Transfer of graded potentials at the photoreceptor-interneuron synapse.
Juusola M, Uusitalo RO, Weckstrom M., J. Gen. Physiol. 105(1), 1995
PMID: 7537323

AUTHOR UNKNOWN, 2010
Error detecting and error correcting codes
AUTHOR UNKNOWN, 1950
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A bio-inspired collision avoidance model leads to common routes
Bertrand O, Lindemann JP, Egelhaaf M (2015)
Bielefeld University.
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