Insect Inspired View Based Navigation Exploiting Temporal Information

Kagioulis, Efstathios; Philippides, Andrew; Graham, Paul; Knight, James C.; Nowotny, Thomas

doi:10.1007/978-3-030-64313-3_20

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…Algorithmically, a temporal window can be introduced, where only a limited amount of the sequentially stored snapshots is used for familiarity detection. This way, general aliasing can be minimised as well as more complex routes followed (Kagioulis et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Familiarity-taxis: A bilateral approach to view-based navigation

Steinbeck,

Kagioulis,

Dewar

et al. 2023

Preprint

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Many insects use view-based navigation, or snapshot matching, to return to familiar locations, or navigate routes. This relies on egocentric memories being matched to current views of the world. Previous snapshot navigation algorithms have used full panoramic vision for the comparison of memorised images with query images to establish a measure of familiarity, which leads to a recovery of the original heading direction from when the snapshot was taken. Many aspects of insect sensory systems are lateralised with steering being derived from the comparison of left and right signals like a classic Braitenberg vehicle. Here we investigate whether view-based route navigation can be implemented using bilateral visual familiarity comparisons. We found that the difference in familiarity between estimates from left and right fields of view can be used as a steering signal to recover the original heading direction. This finding extends across many different sizes of field of view and visual resolutions. In insects, steering computations are implemented in a brain region called the Lateral Accessory Lobe, within the Central Complex. In a simple simulation we show with a SNN model of the LAL an existence proof of how bilateral visual familiarity could drive a search for a visually defined goal.

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Section: Discussionmentioning

confidence: 99%

Familiarity-taxis: A bilateral approach to view-based navigation

Steinbeck,

Kagioulis,

Dewar

et al. 2023

Preprint

Self Cite

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“…Follow-up robotic studies have explored extensions ranging from the compact encoding of visual scenes [113], to scanning-free route following [114], applicability to aerial navigation [115], and even the use of temporal cues to improve visual place recognition [116,117]. Moreover, direct insights were drawn towards the function of the observed learning walks and flights of insects [118] as they begin foraging [119].…”

Section: Mental Faculties: Revealing the Neural Basis Of Invertebrate...mentioning

confidence: 99%

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research

et al. 2023

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Many invertebrates are ideal model systems on which to base robot design principles due to their success in solving seemingly complex tasks across domains while possessing smaller nervous systems than vertebrates. Three areas are particularly relevant for robot designers: Research on flying and crawling invertebrates has inspired new materials and geometries from which robot bodies (their morphologies) can be constructed, enabling a new generation of softer, smaller, and lighter robots. Research on walking insects has informed the design of new systems for controlling robot bodies (their motion control) and adapting their motion to their environment without costly computational methods. And research combining wet and computational neuroscience with robotic validation methods has revealed the structure and function of core circuits in the insect brain responsible for the navigation and swarming capabilities (their mental faculties) displayed by foraging insects. The last decade has seen significant progress in the application of principles extracted from invertebrates, as well as the application of biomimetic robots to model and better understand how animals function. This Perspectives paper on the past 10 years of the Living Machines conference outlines some of the most exciting recent advances in each of these fields before outlining lessons gleaned and the outlook for the next decade of invertebrate robotic research.

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“…Recent results showing that lesioning the MB in ants specifically affects performance on tasks requiring learned (but not innate) visual orientation have supported this assumption (42)(43)(44). However, computational models based on the MB (for both olfactory and visual learning) have also mostly used static input patterns (28,(45)(46)(47)(48)(49) and, for route following, have evaluated performances in somewhat simplified visual environments with little of the variability that occurs in the real world. Our current model, by contrast, addresses the problem of learning and recognizing, on repeated traversal, the pattern of input spikes produced from visual change detection using an event camera (Fig.…”

Section: Introductionmentioning

confidence: 99%

Neuromorphic sequence learning with an event camera on routes through vegetation

Zhu,

Mangan,

Webb

2023

Sci. Robot.

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For many robotics applications, it is desirable to have relatively low-power and efficient onboard solutions. We took inspiration from insects, such as ants, that are capable of learning and following routes in complex natural environments using relatively constrained sensory and neural systems. Such capabilities are particularly relevant to applications such as agricultural robotics, where visual navigation through dense vegetation remains a challenging task. In this scenario, a route is likely to have high self-similarity and be subject to changing lighting conditions and motion over uneven terrain, and the effects of wind on leaves increase the variability of the input. We used a bioinspired event camera on a terrestrial robot to collect visual sequences along routes in natural outdoor environments and applied a neural algorithm for spatiotemporal memory that is closely based on a known neural circuit in the insect brain. We show that this method is plausible to support route recognition for visual navigation and more robust than SeqSLAM when evaluated on repeated runs on the same route or routes with small lateral offsets. By encoding memory in a spiking neural network running on a neuromorphic computer, our model can evaluate visual familiarity in real time from event camera footage.

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Insect Inspired View Based Navigation Exploiting Temporal Information

Cited by 7 publications

References 25 publications

Familiarity-taxis: A bilateral approach to view-based navigation

Familiarity-taxis: A bilateral approach to view-based navigation

A virtuous cycle between invertebrate and robotics research: perspective on a decade of Living Machines research

Neuromorphic sequence learning with an event camera on routes through vegetation

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