Resource-efficient Bio-inspired Visual Processing on the Hexapod Walking Robot HECTOR

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2020 Apr 3

PMID 32236111

Citations 3

Authors

Hanno Gerd Meyer

Daniel Klimeck

Jan Paskarbeit

Ulrich Ruckert

Martin Egelhaaf

Mario Porrmann

Axel Schneider

Affiliations

Soon will be listed here.

Abstract

Emulating the highly resource-efficient processing of visual motion information in the brain of flying insects, a bio-inspired controller for collision avoidance and navigation was implemented on a novel, integrated System-on-Chip-based hardware module. The hardware module is used to control visually-guided navigation behavior of the stick insect-like hexapod robot HECTOR. By leveraging highly parallelized bio-inspired algorithms to extract nearness information from visual motion in dynamically reconfigurable logic, HECTOR is able to navigate to predefined goal positions without colliding with obstacles. The system drastically outperforms CPU- and graphics card-based implementations in terms of speed and resource efficiency, making it suitable to be also placed on fast moving robots, such as flying drones.

Citing Articles

Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review.

Wang J, Lin S, Liu A Biomimetics (Basel). 2023; 8(4).

PMID: 37622955 PMC: 10452487. DOI: 10.3390/biomimetics8040350.

Insect-Inspired Robots: Bridging Biological and Artificial Systems.

Manoonpong P, Patane L, Xiong X, Brodoline I, Dupeyroux J, Viollet S Sensors (Basel). 2021; 21(22).

PMID: 34833685 PMC: 8623770. DOI: 10.3390/s21227609.

Descending and Ascending Signals That Maintain Rhythmic Walking Pattern in Crickets.

Naniwa K, Aonuma H Front Robot AI. 2021; 8:625094.

PMID: 33855051 PMC: 8039156. DOI: 10.3389/frobt.2021.625094.

References

Altshuler D, Srinivasan M . Comparison of Visually Guided Flight in Insects and Birds. Front Neurosci. 2018; 12:157. PMC: 5864886. DOI: 10.3389/fnins.2018.00157. View

Kress D, Egelhaaf M . Impact of stride-coupled gaze shifts of walking blowflies on the neuronal representation of visual targets. Front Behav Neurosci. 2014; 8:307. PMC: 4164030. DOI: 10.3389/fnbeh.2014.00307. View

Bertrand O, Lindemann J, Egelhaaf M . A Bio-inspired Collision Avoidance Model Based on Spatial Information Derived from Motion Detectors Leads to Common Routes. PLoS Comput Biol. 2015; 11(11):e1004339. PMC: 4652890. DOI: 10.1371/journal.pcbi.1004339. View

Egelhaaf M, Kern R, Lindemann J . Motion as a source of environmental information: a fresh view on biological motion computation by insect brains. Front Neural Circuits. 2014; 8:127. PMC: 4211400. DOI: 10.3389/fncir.2014.00127. View

Cope A, Sabo C, Gurney K, Vasilaki E, Marshall J . A Model for an Angular Velocity-Tuned Motion Detector Accounting for Deviations in the Corridor-Centering Response of the Bee. PLoS Comput Biol. 2016; 12(5):e1004887. PMC: 4858260. DOI: 10.1371/journal.pcbi.1004887. View

Borst A . Fly visual course control: behaviour, algorithms and circuits. Nat Rev Neurosci. 2014; 15(9):590-9. DOI: 10.1038/nrn3799. View

Serres J, Ruffier F . Optic flow-based collision-free strategies: From insects to robots. Arthropod Struct Dev. 2017; 46(5):703-717. DOI: 10.1016/j.asd.2017.06.003. View

Lindemann J, Kern R, van Hateren J, Ritter H, Egelhaaf M . On the computations analyzing natural optic flow: quantitative model analysis of the blowfly motion vision pathway. J Neurosci. 2005; 25(27):6435-48. PMC: 6725274. DOI: 10.1523/JNEUROSCI.1132-05.2005. View

Schwegmann A, Lindemann J, Egelhaaf M . Depth information in natural environments derived from optic flow by insect motion detection system: a model analysis. Front Comput Neurosci. 2014; 8:83. PMC: 4118023. DOI: 10.3389/fncom.2014.00083. View

10.

Ramdya P, Thandiackal R, Cherney R, Asselborn T, Benton R, Ijspeert A . Climbing favours the tripod gait over alternative faster insect gaits. Nat Commun. 2017; 8:14494. PMC: 5321742. DOI: 10.1038/ncomms14494. View

11.

Koenderink J . Optic flow. Vision Res. 1986; 26(1):161-79. DOI: 10.1016/0042-6989(86)90078-7. View

12.

Egelhaaf M, Boeddeker N, Kern R, Kurtz R, Lindemann J . Spatial vision in insects is facilitated by shaping the dynamics of visual input through behavioral action. Front Neural Circuits. 2012; 6:108. PMC: 3526811. DOI: 10.3389/fncir.2012.00108. View

13.

Meyer H, Lindemann J, Egelhaaf M . Pattern-dependent response modulations in motion-sensitive visual interneurons--a model study. PLoS One. 2011; 6(7):e21488. PMC: 3132178. DOI: 10.1371/journal.pone.0021488. View

14.

Srinivasan M . Honeybees as a model for the study of visually guided flight, navigation, and biologically inspired robotics. Physiol Rev. 2011; 91(2):413-60. DOI: 10.1152/physrev.00005.2010. View

15.

Borst A, Egelhaaf M . Principles of visual motion detection. Trends Neurosci. 1989; 12(8):297-306. DOI: 10.1016/0166-2236(89)90010-6. View

16.

Duchon A, Warren Jr W . A visual equalization strategy for locomotor control: of honeybees, robots, and humans. Psychol Sci. 2002; 13(3):272-8. DOI: 10.1111/1467-9280.00450. View

17.

Durr V, Arena P, Cruse H, Dallmann C, Drimus A, Hoinville T . Integrative Biomimetics of Autonomous Hexapedal Locomotion. Front Neurorobot. 2019; 13:88. PMC: 6819508. DOI: 10.3389/fnbot.2019.00088. View

18.

Juusola M, Uusitalo R, Weckstrom M . Transfer of graded potentials at the photoreceptor-interneuron synapse. J Gen Physiol. 1995; 105(1):117-48. PMC: 2216927. DOI: 10.1085/jgp.105.1.117. View

19.

van der Schaaf A, van Hateren J . Modelling the power spectra of natural images: statistics and information. Vision Res. 1996; 36(17):2759-70. DOI: 10.1016/0042-6989(96)00002-8. View

20.

Cruse H, Kindermann T, Schumm M, Dean J, Schmitz J . Walknet-a biologically inspired network to control six-legged walking. Neural Netw. 2003; 11(7-8):1435-1447. DOI: 10.1016/s0893-6080(98)00067-7. View