A Bio-Inspired Model for Visual Collision Avoidance on a Hexapod Walking Robot

Meyer, Hanno Gerd; Bertrand, O.; Paskarbeit, Jan; Lindemann, Jens Peter; Schneider, Axel; Egelhaaf, Martin

doi:10.1007/978-3-319-42417-0_16

Cited by 12 publications

(25 citation statements)

References 20 publications

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“…Flying insects, such as flies or bees, are able to perform complex visually-guided behaviors with a minimum of neuronal computing power. Hence, in the vision-based direction controller used to control collision avoidance and navigation behavior on HECTOR [16] aspects of the early visual stages of the visual system of flies are adopted. Due to the large panoramic visual field and the low spatial resolution of the visual system of flies [30] the computational requirements of processing optic flow can be reduced, since optic flow induced on the retina by selfmotion or motion in the environment can be processed with a relatively small number of computational units.…”

Section: Preprocessing Of Imagesmentioning

confidence: 99%

“…To mimic the relatively low spatial resolution of the compound eyes of flies [30], each camera image is scaled down to a rectangular grid of photoreceptors with an interommatidial angle of 1.5˚, resulting in an array of 128x30 pixels (i.e. luminance values) covering a field of view of 192˚horizontally and 45˚vertically [16].…”

Section: Preprocessing Of Imagesmentioning

confidence: 99%

“…rather than a flying system is challenging as stride-coupled motion of the camera might obfuscate nearness estimation from optic flow [15]. The controller architecture implemented on the embedded hardware module [16] comprises a simple model for insect-inspired visual collision avoidance which has been proposed recently [17]. In contrast to bio-inspired approaches using binocular vision [18] or models of visual motion processing in cortical areas [19] the model presented here is based on the extraction of nearness information from optic flow using so-called correlation-type elementary motion detectors (EMDs, [20]).…”

Section: Introductionmentioning

confidence: 99%

“…EMDs can be used to model the neuronal mechanisms for estimating optic flow in flying insects [21,22], avian [23] and other vertebrate species including man (for review, see [24]). The responses of EMDs to pure translational optic flow have been concluded to resemble a representation of the relative contrast-weighted nearness to objects in the environment, or, in other words, of the contours of nearby objects [16,[25][26][27][28]. The collision avoidance model (a) extracts nearness information from optic flow by EMDs, (b) determines a collision avoidance direction from the map of nearness estimates and (c) computes a collision avoidance necessity, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…potential obstacles are still rather distant). When coupled with a goal direction, the algorithm has been shown to successfully guide HECTOR to a goal in different simulated cluttered environments while avoiding collisions with obstacles [16]. In this study, the above introduced, bio-inspired algorithms are implemented on a dedicated, resource-efficient hardware and their performance with respect to obstacle avoidance and navigation is optimized in simulation and tested in a minimal real-world scenario with one obstacle in an otherwise visually cluttered environment.…”

Section: Introductionmentioning

confidence: 99%

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Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR

et al. 2020

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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. OPEN ACCESS Citation: Meyer HG, Klimeck D, Paskarbeit J, Rückert U, Egelhaaf M, Porrmann M, et al. (2020) Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR. PLoS ONE 15(4): e0230620. https://doi.org/10.

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Section: Preprocessing Of Imagesmentioning

confidence: 99%

Section: Preprocessing Of Imagesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR

et al. 2020

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A Hybrid Visual-Model Based Robot Control Strategy for Micro Ground Robots

Liu

et al. 2018

From Animals to Animats 15

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This paper proposed a hybrid vision-based robot control strategy for micro ground robots by mediating two vision models from mixed categories: a bio-inspired collision avoidance model and a segmentation based target following model. The implemented model coordination strategy is described as a probabilistic model using finite state machine (FSM) that allows the robot to switch behaviours adapting to the acquired visual information. Experiments demonstrated the stability and convergence of the embedded hybrid system by real robots, including the studying of collective behaviour by a swarm of such robots with environment mediation. This research enables micro robots to run visual models with more complexity. Moreover, it showed the possibility to realize aggregation behaviour on micro robots by utilizing vision as the only sensing modality from non-omnidirectional cameras.

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Contrast independent biologically inspired translational optic flow estimation

2022

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The visual systems of insects are relatively simple compared to humans. However, they enable navigation through complex environments where insects perform exceptional levels of obstacle avoidance. Biology uses two separable modes of optic flow to achieve this: rapid gaze fixation (rotational motion known as saccades); and the inter-saccadic translational motion. While the fundamental process of insect optic flow has been known since the 1950’s, so too has its dependence on contrast. The surrounding visual pathways used to overcome environmental dependencies are less well known. Previous work has shown promise for low-speed rotational motion estimation, but a gap remained in the estimation of translational motion, in particular the estimation of the time to impact. To consistently estimate the time to impact during inter-saccadic translatory motion, the fundamental limitation of contrast dependence must be overcome. By adapting an elaborated rotational velocity estimator from literature to work for translational motion, this paper proposes a novel algorithm for overcoming the contrast dependence of time to impact estimation using nonlinear spatio-temporal feedforward filtering. By applying bioinspired processes, approximately 15 points per decade of statistical discrimination were achieved when estimating the time to impact to a target across 360 background, distance, and velocity combinations: a 17-fold increase over the fundamental process. These results show the contrast dependence of time to impact estimation can be overcome in a biologically plausible manner. This, combined with previous results for low-speed rotational motion estimation, allows for contrast invariant computational models designed on the principles found in the biological visual system, paving the way for future visually guided systems.

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A Bio-Inspired Model for Visual Collision Avoidance on a Hexapod Walking Robot

Cited by 12 publications

References 20 publications

Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR

Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR

A Hybrid Visual-Model Based Robot Control Strategy for Micro Ground Robots

Contrast independent biologically inspired translational optic flow estimation

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