1
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Singh S, Garratt M, Srinivasan M, Ravi S. Analysis of collision avoidance in honeybee flight. J R Soc Interface 2024; 21:20230601. [PMID: 38531412 PMCID: PMC10973882 DOI: 10.1098/rsif.2023.0601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 03/01/2024] [Indexed: 03/28/2024] Open
Abstract
Insects are excellent at flying in dense vegetation and navigating through other complex spatial environments. This study investigates the strategies used by honeybees (Apis mellifera) to avoid collisions with an obstacle encountered frontally during flight. Bees were trained to fly through a tunnel that contained a solitary vertically oriented cylindrical obstacle placed along the midline. Flight trajectories of bees were recorded for six conditions in which the diameter of the obstructing cylinder was systematically varied from 25 mm to 160 mm. Analysis of salient events during the bees' flight, such as the deceleration before the obstacle, and the initiation of the deviation in flight path to avoid collisions, revealed a strategy for obstacle avoidance that is based on the relative retinal expansion velocity generated by the obstacle when the bee is on a collision course. We find that a quantitative model, featuring a controller that extracts specific visual cues from the frontal visual field, provides an accurate characterization of the geometry and the dynamics of the manoeuvres adopted by honeybees to avoid collisions. This study paves the way for the design of unmanned aerial systems, by identifying the visual cues that are used by honeybees for performing robust obstacle avoidance flight.
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Affiliation(s)
- Shreyansh Singh
- School of Engineering and Technology, University of New South Wales, Canberra, Australia
| | - Matthew Garratt
- School of Engineering and Technology, University of New South Wales, Canberra, Australia
| | - Mandyam Srinivasan
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
| | - Sridhar Ravi
- School of Engineering and Technology, University of New South Wales, Canberra, Australia
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2
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Schoepe T, Janotte E, Milde MB, Bertrand OJN, Egelhaaf M, Chicca E. Finding the gap: neuromorphic motion-vision in dense environments. Nat Commun 2024; 15:817. [PMID: 38280859 PMCID: PMC10821932 DOI: 10.1038/s41467-024-45063-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 01/15/2024] [Indexed: 01/29/2024] Open
Abstract
Animals have evolved mechanisms to travel safely and efficiently within different habitats. On a journey in dense terrains animals avoid collisions and cross narrow passages while controlling an overall course. Multiple hypotheses target how animals solve challenges faced during such travel. Here we show that a single mechanism enables safe and efficient travel. We developed a robot inspired by insects. It has remarkable capabilities to travel in dense terrain, avoiding collisions, crossing gaps and selecting safe passages. These capabilities are accomplished by a neuromorphic network steering the robot toward regions of low apparent motion. Our system leverages knowledge about vision processing and obstacle avoidance in insects. Our results demonstrate how insects might safely travel through diverse habitats. We anticipate our system to be a working hypothesis to study insects' travels in dense terrains. Furthermore, it illustrates that we can design novel hardware systems by understanding the underlying mechanisms driving behaviour.
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Affiliation(s)
- Thorben Schoepe
- Peter Grünberg Institut 15, Forschungszentrum Jülich, Aachen, Germany.
- Faculty of Technology and Cognitive Interaction Technology Center of Excellence (CITEC), Bielefeld University, Bielefeld, Germany.
- Bio-Inspired Circuits and Systems (BICS) Lab. Zernike Institute for Advanced Materials (Zernike Inst Adv Mat), University of Groningen, Groningen, Netherlands.
- CogniGron (Groningen Cognitive Systems and Materials Center), University of Groningen, Groningen, Netherlands.
| | - Ella Janotte
- Event Driven Perception for Robotics, Italian Institute of Technology, iCub facility, Genoa, Italy
| | - Moritz B Milde
- International Centre for Neuromorphic Systems, MARCS Institute, Western Sydney University, Penrith, Australia
| | | | - Martin Egelhaaf
- Neurobiology, Faculty of Biology, Bielefeld University, Bielefeld, Germany
| | - Elisabetta Chicca
- Faculty of Technology and Cognitive Interaction Technology Center of Excellence (CITEC), Bielefeld University, Bielefeld, Germany
- Bio-Inspired Circuits and Systems (BICS) Lab. Zernike Institute for Advanced Materials (Zernike Inst Adv Mat), University of Groningen, Groningen, Netherlands
- CogniGron (Groningen Cognitive Systems and Materials Center), University of Groningen, Groningen, Netherlands
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3
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Wang H, Zhao J, Wang H, Hu C, Peng J, Yue S. Attention and Prediction-Guided Motion Detection for Low-Contrast Small Moving Targets. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:6340-6352. [PMID: 35533156 DOI: 10.1109/tcyb.2022.3170699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Small target motion detection within complex natural environments is an extremely challenging task for autonomous robots. Surprisingly, the visual systems of insects have evolved to be highly efficient in detecting mates and tracking prey, even though targets occupy as small as a few degrees of their visual fields. The excellent sensitivity to small target motion relies on a class of specialized neurons, called small target motion detectors (STMDs). However, existing STMD-based models are heavily dependent on visual contrast and perform poorly in complex natural environments, where small targets generally exhibit extremely low contrast against neighboring backgrounds. In this article, we develop an attention-and-prediction-guided visual system to overcome this limitation. The developed visual system comprises three main subsystems, namely: 1) an attention module; 2) an STMD-based neural network; and 3) a prediction module. The attention module searches for potential small targets in the predicted areas of the input image and enhances their contrast against a complex background. The STMD-based neural network receives the contrast-enhanced image and discriminates small moving targets from background false positives. The prediction module foresees future positions of the detected targets and generates a prediction map for the attention module. The three subsystems are connected in a recurrent architecture, allowing information to be processed sequentially to activate specific areas for small target detection. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of the proposed visual system for detecting small, low-contrast moving targets against complex natural environments.
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4
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Wang J, Lin S, Liu A. Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review. Biomimetics (Basel) 2023; 8:350. [PMID: 37622955 PMCID: PMC10452487 DOI: 10.3390/biomimetics8040350] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 08/26/2023] Open
Abstract
Biological principles draw attention to service robotics because of similar concepts when robots operate various tasks. Bioinspired perception is significant for robotic perception, which is inspired by animals' awareness of the environment. This paper reviews the bioinspired perception and navigation of service robots in indoor environments, which are popular applications of civilian robotics. The navigation approaches are classified by perception type, including vision-based, remote sensing, tactile sensor, olfactory, sound-based, inertial, and multimodal navigation. The trend of state-of-art techniques is moving towards multimodal navigation to combine several approaches. The challenges in indoor navigation focus on precise localization and dynamic and complex environments with moving objects and people.
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Affiliation(s)
- Jianguo Wang
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Shiwei Lin
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
| | - Ang Liu
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
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5
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Sun X, Fu Q, Peng J, Yue S. An insect-inspired model facilitating autonomous navigation by incorporating goal approaching and collision avoidance. Neural Netw 2023; 165:106-118. [PMID: 37285728 DOI: 10.1016/j.neunet.2023.05.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 03/17/2023] [Accepted: 05/17/2023] [Indexed: 06/09/2023]
Abstract
Being one of the most fundamental and crucial capacity of robots and animals, autonomous navigation that consists of goal approaching and collision avoidance enables completion of various tasks while traversing different environments. In light of the impressive navigational abilities of insects despite their tiny brains compared to mammals, the idea of seeking solutions from insects for the two key problems of navigation, i.e., goal approaching and collision avoidance, has fascinated researchers and engineers for many years. However, previous bio-inspired studies have focused on merely one of these two problems at one time. Insect-inspired navigation algorithms that synthetically incorporate both goal approaching and collision avoidance, and studies that investigate the interactions of these two mechanisms in the context of sensory-motor closed-loop autonomous navigation are lacking. To fill this gap, we propose an insect-inspired autonomous navigation algorithm to integrate the goal approaching mechanism as the global working memory inspired by the sweat bee's path integration (PI) mechanism, and the collision avoidance model as the local immediate cue built upon the locust's lobula giant movement detector (LGMD) model. The presented algorithm is utilized to drive agents to complete navigation task in a sensory-motor closed-loop manner within a bounded static or dynamic environment. Simulation results demonstrate that the synthetic algorithm is capable of guiding the agent to complete challenging navigation tasks in a robust and efficient way. This study takes the first tentative step to integrate the insect-like navigation mechanisms with different functionalities (i.e., global goal and local interrupt) into a coordinated control system that future research avenues could build upon.
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Affiliation(s)
- Xuelong Sun
- School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China; Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, 510006, China
| | - Qinbing Fu
- School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China; Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, 510006, China
| | - Jigen Peng
- School of Mathematics and Information Science, Guangzhou University, Guangzhou, 510006, China; Machine Life and Intelligence Research Centre, Guangzhou University, Guangzhou, 510006, China.
| | - Shigang Yue
- Computational Intelligence Lab (CIL)/School of Computer Science, University of Lincoln, Lincoln, LN6 7TS, United Kingdom; School of Computing and Mathematical Sciences, University of Leicester, Leicester, LE1 7RH, United Kingdom.
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6
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Bertrand OJN, Sonntag A. The potential underlying mechanisms during learning flights. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2023:10.1007/s00359-023-01637-7. [PMID: 37204434 DOI: 10.1007/s00359-023-01637-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 05/02/2023] [Accepted: 05/04/2023] [Indexed: 05/20/2023]
Abstract
Hymenopterans, such as bees and wasps, have long fascinated researchers with their sinuous movements at novel locations. These movements, such as loops, arcs, or zigzags, serve to help insects learn their surroundings at important locations. They also allow the insects to explore and orient themselves in their environment. After they gained experience with their environment, the insects fly along optimized paths guided by several guidance strategies, such as path integration, local homing, and route-following, forming a navigational toolkit. Whereas the experienced insects combine these strategies efficiently, the naive insects need to learn about their surroundings and tune the navigational toolkit. We will see that the structure of the movements performed during the learning flights leverages the robustness of certain strategies within a given scale to tune other strategies which are more efficient at a larger scale. Thus, an insect can explore its environment incrementally without risking not finding back essential locations.
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Affiliation(s)
- Olivier J N Bertrand
- Neurobiology, Bielefeld University, Universitätstr. 25, 33615, Bielefeld, NRW, Germany.
| | - Annkathrin Sonntag
- Neurobiology, Bielefeld University, Universitätstr. 25, 33615, Bielefeld, NRW, Germany
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7
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Egelhaaf M. Optic flow based spatial vision in insects. J Comp Physiol A Neuroethol Sens Neural Behav Physiol 2023:10.1007/s00359-022-01610-w. [PMID: 36609568 DOI: 10.1007/s00359-022-01610-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 12/06/2022] [Accepted: 12/24/2022] [Indexed: 01/09/2023]
Abstract
The optic flow, i.e., the displacement of retinal images of objects in the environment induced by self-motion, is an important source of spatial information, especially for fast-flying insects. Spatial information over a wide range of distances, from the animal's immediate surroundings over several hundred metres to kilometres, is necessary for mediating behaviours, such as landing manoeuvres, collision avoidance in spatially complex environments, learning environmental object constellations and path integration in spatial navigation. To facilitate the processing of spatial information, the complexity of the optic flow is often reduced by active vision strategies. These result in translations and rotations being largely separated by a saccadic flight and gaze mode. Only the translational components of the optic flow contain spatial information. In the first step of optic flow processing, an array of local motion detectors provides a retinotopic spatial proximity map of the environment. This local motion information is then processed in parallel neural pathways in a task-specific manner and used to control the different components of spatial behaviour. A particular challenge here is that the distance information extracted from the optic flow does not represent the distances unambiguously, but these are scaled by the animal's speed of locomotion. Possible ways of coping with this ambiguity are discussed.
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Affiliation(s)
- Martin Egelhaaf
- Neurobiology and Center for Cognitive Interaction Technology (CITEC), Bielefeld University, Universitätsstraße 25, 33615, Bielefeld, Germany.
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8
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Skelton PSM, Finn A, Brinkworth RSA. Contrast independent biologically inspired translational optic flow estimation. BIOLOGICAL CYBERNETICS 2022; 116:635-660. [PMID: 36303043 PMCID: PMC9691503 DOI: 10.1007/s00422-022-00948-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 10/11/2022] [Indexed: 06/16/2023]
Abstract
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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Affiliation(s)
- Phillip S. M. Skelton
- Centre for Defence Engineering Research and Training, College of Science and Engineering, Flinders University, 1284 South Road, Tonsley, South Australia 5042 Australia
| | - Anthony Finn
- Science, Technology, Engineering, and Mathematics, University of South Australia, 1 Mawson Lakes Boulevard, Mawson Lakes, South Australia 5095 Australia
| | - Russell S. A. Brinkworth
- Centre for Defence Engineering Research and Training, College of Science and Engineering, Flinders University, 1284 South Road, Tonsley, South Australia 5042 Australia
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9
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Parra LA, Díaz DEM, Ramos F. Computational framework of the visual sensory system based on neuroscientific evidence of the ventral pathway. COGN SYST RES 2022. [DOI: 10.1016/j.cogsys.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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10
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Huang X, Qiao H, Li H, Jiang Z. Bioinspired approach-sensitive neural network for collision detection in cluttered and dynamic backgrounds. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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11
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Ravi S, Siesenop T, Bertrand OJ, Li L, Doussot C, Fisher A, Warren WH, Egelhaaf M. Bumblebees display characteristics of active vision during robust obstacle avoidance flight. J Exp Biol 2022; 225:274096. [PMID: 35067721 PMCID: PMC8920035 DOI: 10.1242/jeb.243021] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 01/18/2022] [Indexed: 11/20/2022]
Abstract
Insects are remarkable flyers and capable of navigating through highly cluttered environments. We tracked the head and thorax of bumblebees freely flying in a tunnel containing vertically oriented obstacles to uncover the sensorimotor strategies used for obstacle detection and collision avoidance. Bumblebees presented all the characteristics of active vision during flight by stabilizing their head relative to the external environment and maintained close alignment between their gaze and flightpath. Head stabilization increased motion contrast of nearby features against the background to enable obstacle detection. As bees approached obstacles, they appeared to modulate avoidance responses based on the relative retinal expansion velocity (RREV) of obstacles and their maximum evasion acceleration was linearly related to RREVmax. Finally, bees prevented collisions through rapid roll manoeuvres implemented by their thorax. Overall, the combination of visuo-motor strategies of bumblebees highlights elegant solutions developed by insects for visually guided flight through cluttered environments.
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Affiliation(s)
- Sridhar Ravi
- Department of Neurobiology and Center of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33619 Bielefeld, Germany,School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2600, Australia,Author for correspondence ()
| | - Tim Siesenop
- Department of Neurobiology and Center of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33619 Bielefeld, Germany
| | - Olivier J. Bertrand
- Department of Neurobiology and Center of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33619 Bielefeld, Germany
| | - Liang Li
- Department of Collective Behavior, Max Planck Institute of Animal Behavior, University of Konstanz, 78464 Konstanz, Germany,Centre for the Advanced Study of Collective Behaviour, University of Konstanz, 78464 Konstanz, Germany,Department of Biology, University of Konstanz, 78464 Konstanz, Germany
| | - Charlotte Doussot
- Department of Neurobiology and Center of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33619 Bielefeld, Germany
| | - Alex Fisher
- School of Engineering, RMIT University, Melbourne, VIC 3001, Australia
| | - William H. Warren
- Department of Cognitive, Linguistic & Psychological Sciences, Brown University, Providence, RI 02912, USA
| | - Martin Egelhaaf
- Department of Neurobiology and Center of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33619 Bielefeld, Germany
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12
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Bertrand OJN, Doussot C, Siesenop T, Ravi S, Egelhaaf M. Visual and movement memories steer foraging bumblebees along habitual routes. J Exp Biol 2021; 224:269087. [PMID: 34115117 DOI: 10.1242/jeb.237867] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 04/06/2021] [Indexed: 11/20/2022]
Abstract
One persistent question in animal navigation is how animals follow habitual routes between their home and a food source. Our current understanding of insect navigation suggests an interplay between visual memories, collision avoidance and path integration, the continuous integration of distance and direction travelled. However, these behavioural modules have to be continuously updated with instantaneous visual information. In order to alleviate this need, the insect could learn and replicate habitual movements ('movement memories') around objects (e.g. a bent trajectory around an object) to reach its destination. We investigated whether bumblebees, Bombus terrestris, learn and use movement memories en route to their home. Using a novel experimental paradigm, we habituated bumblebees to establish a habitual route in a flight tunnel containing 'invisible' obstacles. We then confronted them with conflicting cues leading to different choice directions depending on whether they rely on movement or visual memories. The results suggest that they use movement memories to navigate, but also rely on visual memories to solve conflicting situations. We investigated whether the observed behaviour was due to other guidance systems, such as path integration or optic flow-based flight control, and found that neither of these systems was sufficient to explain the behaviour.
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Affiliation(s)
- Olivier J N Bertrand
- Department of Neurobiology and Cognitive Interaction Technology Center of Excellence (CITEC) , Bielefeld University, D-33501 Bielefeld, Germany
| | - Charlotte Doussot
- Department of Neurobiology and Cognitive Interaction Technology Center of Excellence (CITEC) , Bielefeld University, D-33501 Bielefeld, Germany
| | - Tim Siesenop
- Department of Neurobiology and Cognitive Interaction Technology Center of Excellence (CITEC) , Bielefeld University, D-33501 Bielefeld, Germany
| | - Sridhar Ravi
- Department of Neurobiology and Cognitive Interaction Technology Center of Excellence (CITEC) , Bielefeld University, D-33501 Bielefeld, Germany.,School of Engineering, RMIT University, Melbourne, VIC 3083, Australia
| | - Martin Egelhaaf
- Department of Neurobiology and Cognitive Interaction Technology Center of Excellence (CITEC) , Bielefeld University, D-33501 Bielefeld, Germany
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13
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Gonsek A, Jeschke M, Rönnau S, Bertrand OJN. From Paths to Routes: A Method for Path Classification. Front Behav Neurosci 2021; 14:610560. [PMID: 33551764 PMCID: PMC7859641 DOI: 10.3389/fnbeh.2020.610560] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Accepted: 12/21/2020] [Indexed: 11/13/2022] Open
Abstract
Many animals establish, learn and optimize routes between locations to commute efficiently. One step in understanding route following is defining measures of similarities between the paths taken by the animals. Paths have commonly been compared by using several descriptors (e.g., the speed, distance traveled, or the amount of meandering) or were visually classified into categories by the experimenters. However, similar quantities obtained from such descriptors do not guarantee similar paths, and qualitative classification by experimenters is prone to observer biases. Here we propose a novel method to classify paths based on their similarity with different distance functions and clustering algorithms based on the trajectories of bumblebees flying through a cluttered environment. We established a method based on two distance functions (Dynamic Time Warping and Fréchet Distance). For all combinations of trajectories, the distance was calculated with each measure. Based on these distance values, we grouped similar trajectories by applying the Monte Carlo Reference-Based Consensus Clustering algorithm. Our procedure provides new options for trajectory analysis based on path similarities in a variety of experimental paradigms.
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14
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de Croon GCHE, De Wagter C, Seidl T. Enhancing optical-flow-based control by learning visual appearance cues for flying robots. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-020-00279-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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15
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Doussot C, Bertrand OJN, Egelhaaf M. The Critical Role of Head Movements for Spatial Representation During Bumblebees Learning Flight. Front Behav Neurosci 2021; 14:606590. [PMID: 33542681 PMCID: PMC7852487 DOI: 10.3389/fnbeh.2020.606590] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 11/23/2020] [Indexed: 11/20/2022] Open
Abstract
Bumblebees perform complex flight maneuvers around the barely visible entrance of their nest upon their first departures. During these flights bees learn visual information about the surroundings, possibly including its spatial layout. They rely on this information to return home. Depth information can be derived from the apparent motion of the scenery on the bees' retina. This motion is shaped by the animal's flight and orientation: Bees employ a saccadic flight and gaze strategy, where rapid turns of the head (saccades) alternate with flight segments of apparently constant gaze direction (intersaccades). When during intersaccades the gaze direction is kept relatively constant, the apparent motion contains information about the distance of the animal to environmental objects, and thus, in an egocentric reference frame. Alternatively, when the gaze direction rotates around a fixed point in space, the animal perceives the depth structure relative to this pivot point, i.e., in an allocentric reference frame. If the pivot point is at the nest-hole, the information is nest-centric. Here, we investigate in which reference frames bumblebees perceive depth information during their learning flights. By precisely tracking the head orientation, we found that half of the time, the head appears to pivot actively. However, only few of the corresponding pivot points are close to the nest entrance. Our results indicate that bumblebees perceive visual information in several reference frames when they learn about the surroundings of a behaviorally relevant location.
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Affiliation(s)
- Charlotte Doussot
- Department of Neurobiology, University of Bielefeld, Bielefeld, Germany
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16
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Meyer HG, Klimeck D, Paskarbeit J, Rückert U, Egelhaaf M, Porrmann M, Schneider A. Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR. PLoS One 2020; 15:e0230620. [PMID: 32236111 PMCID: PMC7112198 DOI: 10.1371/journal.pone.0230620] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 03/04/2020] [Indexed: 11/26/2022] Open
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.
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Affiliation(s)
- Hanno Gerd Meyer
- Research Group Biomechatronics, CITEC, Bielefeld University, Bielefeld, Germany
- Department of Neurobiology and CITEC, Bielefeld University, Bielefeld, Germany
- Biomechatronics and Embedded Systems Group, Faculty of Engineering and Mathematics, University of Applied Sciences, Bielefeld, Germany
| | - Daniel Klimeck
- Cognitronics and Sensor Systems Group, CITEC, Bielefeld University, Bielefeld, Germany
| | - Jan Paskarbeit
- Research Group Biomechatronics, CITEC, Bielefeld University, Bielefeld, Germany
| | - Ulrich Rückert
- Cognitronics and Sensor Systems Group, CITEC, Bielefeld University, Bielefeld, Germany
| | - Martin Egelhaaf
- Department of Neurobiology and CITEC, Bielefeld University, Bielefeld, Germany
| | - Mario Porrmann
- Computer Engineering Group, Osnabrück University, Osnabrück, Germany
| | - Axel Schneider
- Research Group Biomechatronics, CITEC, Bielefeld University, Bielefeld, Germany
- Biomechatronics and Embedded Systems Group, Faculty of Engineering and Mathematics, University of Applied Sciences, Bielefeld, Germany
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17
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Roberts SF, Koditschek DE, Miracchi LJ. Examples of Gibsonian Affordances in Legged Robotics Research Using an Empirical, Generative Framework. Front Neurorobot 2020; 14:12. [PMID: 32153382 PMCID: PMC7044146 DOI: 10.3389/fnbot.2020.00012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Accepted: 01/31/2020] [Indexed: 01/17/2023] Open
Abstract
Evidence from empirical literature suggests that explainable complex behaviors can be built from structured compositions of explainable component behaviors with known properties. Such component behaviors can be built to directly perceive and exploit affordances. Using six examples of recent research in legged robot locomotion, we suggest that robots can be programmed to effectively exploit affordances without developing explicit internal models of them. We use a generative framework to discuss the examples, because it helps us to separate-and thus clarify the relationship between-description of affordance exploitation from description of the internal representations used by the robot in that exploitation. Under this framework, details of the architecture and environment are related to the emergent behavior of the system via a generative explanation. For example, the specific method of information processing a robot uses might be related to the affordance the robot is designed to exploit via a formal analysis of its control policy. By considering the mutuality of the agent-environment system during robot behavior design, roboticists can thus develop robust architectures which implicitly exploit affordances. The manner of this exploitation is made explicit by a well constructed generative explanation.
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Affiliation(s)
- Sonia F Roberts
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Daniel E Koditschek
- Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Lisa J Miracchi
- Department of Philosophy, University of Pennsylvania, Philadelphia, PA, United States
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18
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Zhu H, Liu H, Ataei A, Munk Y, Daniel T, Paschalidis IC. Learning from animals: How to Navigate Complex Terrains. PLoS Comput Biol 2020; 16:e1007452. [PMID: 31917816 PMCID: PMC6952082 DOI: 10.1371/journal.pcbi.1007452] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Accepted: 10/01/2019] [Indexed: 11/18/2022] Open
Abstract
We develop a method to learn a bio-inspired motion control policy using data collected from hawkmoths navigating in a virtual forest. A Markov Decision Process (MDP) framework is introduced to model the dynamics of moths and sparse logistic regression is used to learn control policy parameters from the data. The results show that moths do not favor detailed obstacle location information in navigation, but rely heavily on optical flow. Using the policy learned from the moth data as a starting point, we propose an actor-critic learning algorithm to refine policy parameters and obtain a policy that can be used by an autonomous aerial vehicle operating in a cluttered environment. Compared with the moths' policy, the policy we obtain integrates both obstacle location and optical flow. We compare the performance of these two policies in terms of their ability to navigate in artificial forest areas. While the optimized policy can adjust its parameters to outperform the moth's policy in each different terrain, the moth's policy exhibits a high level of robustness across terrains.
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Affiliation(s)
- Henghui Zhu
- Center for Information and Systems Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Hao Liu
- College of Control Science and Engineering, Zhejiang University, Hangzhou, China
| | - Armin Ataei
- Center for Information and Systems Engineering, Boston University, Boston, Massachusetts, United States of America
| | - Yonatan Munk
- Department of Biology, University of Washington, Seattle, Washington, United States of America
| | - Thomas Daniel
- Department of Biology, University of Washington, Seattle, Washington, United States of America
| | - Ioannis Ch. Paschalidis
- Department of Electrical and Computer Engineering, Division of Systems Engineering, and Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
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19
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Huang JV, Wei Y, Krapp HG. A biohybrid fly-robot interface system that performs active collision avoidance. BIOINSPIRATION & BIOMIMETICS 2019; 14:065001. [PMID: 31412322 DOI: 10.1088/1748-3190/ab3b23] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We have designed a bio-hybrid fly-robot interface (FRI) to study sensorimotor control in insects. The FRI consists of a miniaturized recording platform mounted on a two-wheeled robot and is controlled by the neuronal spiking activity of an identified visual interneuron, the blowfly H1-cell. For a given turning radius of the robot, we found a proportional relationship between the spike rate of the H1-cell and the relative distance of the FRI from the patterned wall of an experimental arena. Under closed-loop conditions during oscillatory forward movements biased towards the wall, collision avoidance manoeuvres were triggered whenever the H1-cell spike rate exceeded a certain threshold value. We also investigated the FRI behaviour in corners of the arena. The ultimate goal is to enable autonomous and energy-efficient manoeuvrings of the FRI within arbitrary visual environments.
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Affiliation(s)
- Jiaqi V Huang
- Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom
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20
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Lecoeur J, Dacke M, Floreano D, Baird E. The role of optic flow pooling in insect flight control in cluttered environments. Sci Rep 2019; 9:7707. [PMID: 31118454 PMCID: PMC6531491 DOI: 10.1038/s41598-019-44187-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Accepted: 05/07/2019] [Indexed: 11/23/2022] Open
Abstract
Flight through cluttered environments, such as forests, poses great challenges for animals and machines alike because even small changes in flight path may lead to collisions with nearby obstacles. When flying along narrow corridors, insects use the magnitude of visual motion experienced in each eye to control their position, height, and speed but it is unclear how this strategy would work when the environment contains nearby obstacles against a distant background. To minimise the risk of collisions, we would expect animals to rely on the visual motion generated by only the nearby obstacles but is this the case? To answer this, we combine behavioural experiments with numerical simulations and provide the first evidence that bumblebees extract the maximum rate of image motion in the frontal visual field to steer away from obstacles. Our findings also suggest that bumblebees use different optic flow calculations to control lateral position, speed, and height.
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Affiliation(s)
- Julien Lecoeur
- Laboratory of Intelligent Systems, Institute of Microengineering, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland.
| | - Marie Dacke
- Lund Vision Group, Department of Biology, Lund University, Lund, SE-22362, Sweden
| | - Dario Floreano
- Laboratory of Intelligent Systems, Institute of Microengineering, École Polytechnique Fédérale de Lausanne, Lausanne, CH-1015, Switzerland
| | - Emily Baird
- Lund Vision Group, Department of Biology, Lund University, Lund, SE-22362, Sweden.,Division of Functional Morphology, Department of Zoology, Stockholm University, Stockholm, SE-10691, Sweden
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21
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Ravi S, Bertrand O, Siesenop T, Manz LS, Doussot C, Fisher A, Egelhaaf M. Gap perception in bumblebees. ACTA ACUST UNITED AC 2019; 222:222/2/jeb184135. [PMID: 30683732 DOI: 10.1242/jeb.184135] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2018] [Accepted: 10/26/2018] [Indexed: 11/20/2022]
Abstract
A number of insects fly over long distances below the natural canopy, where the physical environment is highly cluttered consisting of obstacles of varying shape, size and texture. While navigating within such environments, animals need to perceive and disambiguate environmental features that might obstruct their flight. The most elemental aspect of aerial navigation through such environments is gap identification and 'passability' evaluation. We used bumblebees to seek insights into the mechanisms used for gap identification when confronted with an obstacle in their flight path and behavioral compensations employed to assess gap properties. Initially, bumblebee foragers were trained to fly though an unobstructed flight tunnel that led to a foraging chamber. After the bees were familiar with this situation, we placed a wall containing a gap that unexpectedly obstructed the flight path on a return trip to the hive. The flight trajectories of the bees as they approached the obstacle wall and traversed the gap were analyzed in order to evaluate their behavior as a function of the distance between the gap and a background wall that was placed behind the gap. Bumblebees initially decelerated when confronted with an unexpected obstacle. Deceleration was first noticed when the obstacle subtended around 35 deg on the retina but also depended on the properties of the gap. Subsequently, the bees gradually traded off their longitudinal velocity to lateral velocity and approached the gap with increasing lateral displacement and lateral velocity. Bumblebees shaped their flight trajectory depending on the salience of the gap, indicated in our case by the optic flow contrast between the region within the gap and on the obstacle, which decreased with decreasing distance between the gap and the background wall. As the optic flow contrast decreased, the bees spent an increasing amount of time moving laterally across the obstacles. During these repeated lateral maneuvers, the bees are probably assessing gap geometry and passability.
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Affiliation(s)
- Sridhar Ravi
- Department of Neurobiology and Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33615 Bielefeld, Germany .,School of Engineering, RMIT University, Melbourne, VIC 3001, Australia
| | - Olivier Bertrand
- Department of Neurobiology and Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33615 Bielefeld, Germany
| | - Tim Siesenop
- Department of Neurobiology and Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33615 Bielefeld, Germany
| | - Lea-Sophie Manz
- Department of Neurobiology and Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33615 Bielefeld, Germany.,Faculty of Biology, Johannes Gutenberg-Universität Mainz, 55122 Mainz, Germany
| | - Charlotte Doussot
- Department of Neurobiology and Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33615 Bielefeld, Germany
| | - Alex Fisher
- School of Engineering, RMIT University, Melbourne, VIC 3001, Australia
| | - Martin Egelhaaf
- Department of Neurobiology and Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, 33615 Bielefeld, Germany
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22
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Fu Q, Wang H, Hu C, Yue S. Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review. ARTIFICIAL LIFE 2019; 25:263-311. [PMID: 31397604 DOI: 10.1162/artl_a_00297] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.
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Affiliation(s)
- Qinbing Fu
- Guangzhou University, School of Mechanical and Electrical Engineering; Machine Life and Intelligence Research Centre
- University of Lincoln, Computational Intelligence Lab, School of Computer Science; Lincoln Centre for Autonomous Systems.
| | - Hongxin Wang
- University of Lincoln, Computational Intelligence Lab, School of Computer Science; Lincoln Centre for Autonomous Systems.
| | - Cheng Hu
- Guangzhou University, School of Mechanical and Electrical Engineering; Machine Life and Intelligence Research Centre
- University of Lincoln, Computational Intelligence Lab, School of Computer Science; Lincoln Centre for Autonomous Systems.
| | - Shigang Yue
- Guangzhou University, School of Mechanical and Electrical Engineering; Machine Life and Intelligence Research Centre
- University of Lincoln, Computational Intelligence Lab, School of Computer Science; Lincoln Centre for Autonomous Systems.
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23
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Shaping the collision selectivity in a looming sensitive neuron model with parallel ON and OFF pathways and spike frequency adaptation. Neural Netw 2018; 106:127-143. [PMID: 30059829 DOI: 10.1016/j.neunet.2018.04.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 03/15/2018] [Accepted: 04/03/2018] [Indexed: 11/20/2022]
Abstract
Shaping the collision selectivity in vision-based artificial collision-detecting systems is still an open challenge. This paper presents a novel neuron model of a locust looming detector, i.e. the lobula giant movement detector (LGMD1), in order to provide effective solutions to enhance the collision selectivity of looming objects over other visual challenges. We propose an approach to model the biologically plausible mechanisms of ON and OFF pathways and a biophysical mechanism of spike frequency adaptation (SFA) in the proposed LGMD1 visual neural network. The ON and OFF pathways can separate both dark and light looming features for parallel spatiotemporal computations. This works effectively on perceiving a potential collision from dark or light objects that approach; such a bio-plausible structure can also separate LGMD1's collision selectivity to its neighbouring looming detector - the LGMD2. The SFA mechanism can enhance the LGMD1's collision selectivity to approaching objects rather than receding and translating stimuli, which is a significant improvement compared with similar LGMD1 neuron models. The proposed framework has been tested using off-line tests of synthetic and real-world stimuli, as well as on-line bio-robotic tests. The enhanced collision selectivity of the proposed model has been validated in systematic experiments. The computational simplicity and robustness of this work have also been verified by the bio-robotic tests, which demonstrates potential in building neuromorphic sensors for collision detection in both a fast and reliable manner.
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24
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Milde MB, Bertrand OJN, Ramachandran H, Egelhaaf M, Chicca E. Spiking Elementary Motion Detector in Neuromorphic Systems. Neural Comput 2018; 30:2384-2417. [PMID: 30021082 DOI: 10.1162/neco_a_01112] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Apparent motion of the surroundings on an agent's retina can be used to navigate through cluttered environments, avoid collisions with obstacles, or track targets of interest. The pattern of apparent motion of objects, (i.e., the optic flow), contains spatial information about the surrounding environment. For a small, fast-moving agent, as used in search and rescue missions, it is crucial to estimate the distance to close-by objects to avoid collisions quickly. This estimation cannot be done by conventional methods, such as frame-based optic flow estimation, given the size, power, and latency constraints of the necessary hardware. A practical alternative makes use of event-based vision sensors. Contrary to the frame-based approach, they produce so-called events only when there are changes in the visual scene. We propose a novel asynchronous circuit, the spiking elementary motion detector (sEMD), composed of a single silicon neuron and synapse, to detect elementary motion from an event-based vision sensor. The sEMD encodes the time an object's image needs to travel across the retina into a burst of spikes. The number of spikes within the burst is proportional to the speed of events across the retina. A fast but imprecise estimate of the time-to-travel can already be obtained from the first two spikes of a burst and refined by subsequent interspike intervals. The latter encoding scheme is possible due to an adaptive nonlinear synaptic efficacy scaling. We show that the sEMD can be used to compute a collision avoidance direction in the context of robotic navigation in a cluttered outdoor environment and compared the collision avoidance direction to a frame-based algorithm. The proposed computational principle constitutes a generic spiking temporal correlation detector that can be applied to other sensory modalities (e.g., sound localization), and it provides a novel perspective to gating information in spiking neural networks.
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Affiliation(s)
- M B Milde
- Institute of Neuroinformatics, University of Zurich, and ETH Zurich, 8057 Zurich, Switzerland
| | - O J N Bertrand
- Neurobiology, Faculty of Biology, Bielefeld University, 33615 Bielefeld, and Cognitive Interaction Technology, Center of Excellence, Bielefeld University, 33501 Bielefeld, Germany
| | - H Ramachandran
- Faculty of Technology, Bielefeld University, 33615 Bielefeld, and Cognitive Interaction Technology, Center of Excellence, Bielefeld University, 33501 Bielefeld, Germany
| | - M Egelhaaf
- Neurobiology, Faculty of Biology, Bielefeld University, 33615 Bielefeld, and Cognitive Interaction Technology, Center of Excellence, Bielefeld University, 33501 Bielefeld, Germany
| | - E Chicca
- Faculty of Technology, Bielefeld University, 33615 Bielefeld, Germany, and Cognitive Interaction Technology, Center of Excellence, Bielefeld University, 33501 Bielefeld, Germany
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25
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Spatial Encoding of Translational Optic Flow in Planar Scenes by Elementary Motion Detector Arrays. Sci Rep 2018; 8:5821. [PMID: 29643402 PMCID: PMC5895815 DOI: 10.1038/s41598-018-24162-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Accepted: 03/28/2018] [Indexed: 02/02/2023] Open
Abstract
Elementary Motion Detectors (EMD) are well-established models of visual motion estimation in insects. The response of EMDs are tuned to specific temporal and spatial frequencies of the input stimuli, which matches the behavioural response of insects to wide-field image rotation, called the optomotor response. However, other behaviours, such as speed and position control, cannot be fully accounted for by EMDs because these behaviours are largely unaffected by image properties and appear to be controlled by the ratio between the flight speed and the distance to an object, defined here as relative nearness. We present a method that resolves this inconsistency by extracting an unambiguous estimate of relative nearness from the output of an EMD array. Our method is suitable for estimation of relative nearness in planar scenes such as when flying above the ground or beside large flat objects. We demonstrate closed loop control of the lateral position and forward velocity of a simulated agent flying in a corridor. This finding may explain how insects can measure relative nearness and control their flight despite the frequency tuning of EMDs. Our method also provides engineers with a relative nearness estimation technique that benefits from the low computational cost of EMDs.
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Abstract
Navigation in cluttered environments is an important challenge for animals and robots alike and has been the subject of many studies trying to explain and mimic animal navigational abilities. However, the question of selecting an appropriate home location has, so far, received only little attention. This is surprising, since the choice of a home location might greatly influence an animal’s navigation performance. To address the question of home choice in cluttered environments, a systematic analysis of homing trajectories was performed by computer simulations using a skyline-based local homing method. Our analysis reveals that homing performance strongly depends on the location of the home in the environment. Furthermore, it appears that by assessing homing success in the immediate vicinity of the home, an animal might be able to predict its overall success in returning to it from within a much larger area.
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Affiliation(s)
- Martin M. Müller
- Department of Neurobiology, Faculty of Biology, and Cluster of Excellence ‘Cognitive Interaction Technology’ (CITEC), Bielefeld University, Bielefeld, Germany
- * E-mail:
| | - Olivier J. N. Bertrand
- Department of Neurobiology, Faculty of Biology, and Cluster of Excellence ‘Cognitive Interaction Technology’ (CITEC), Bielefeld University, Bielefeld, Germany
| | - Dario Differt
- Computer Engineering Group, Faculty of Technology, Bielefeld University, Bielefeld, Germany
| | - Martin Egelhaaf
- Department of Neurobiology, Faculty of Biology, and Cluster of Excellence ‘Cognitive Interaction Technology’ (CITEC), Bielefeld University, Bielefeld, Germany
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27
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Li J, Lindemann JP, Egelhaaf M. Local motion adaptation enhances the representation of spatial structure at EMD arrays. PLoS Comput Biol 2017; 13:e1005919. [PMID: 29281631 PMCID: PMC5760083 DOI: 10.1371/journal.pcbi.1005919] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2017] [Revised: 01/09/2018] [Accepted: 11/13/2017] [Indexed: 11/18/2022] Open
Abstract
Neuronal representation and extraction of spatial information are essential for behavioral control. For flying insects, a plausible way to gain spatial information is to exploit distance-dependent optic flow that is generated during translational self-motion. Optic flow is computed by arrays of local motion detectors retinotopically arranged in the second neuropile layer of the insect visual system. These motion detectors have adaptive response characteristics, i.e. their responses to motion with a constant or only slowly changing velocity decrease, while their sensitivity to rapid velocity changes is maintained or even increases. We analyzed by a modeling approach how motion adaptation affects signal representation at the output of arrays of motion detectors during simulated flight in artificial and natural 3D environments. We focused on translational flight, because spatial information is only contained in the optic flow induced by translational locomotion. Indeed, flies, bees and other insects segregate their flight into relatively long intersaccadic translational flight sections interspersed with brief and rapid saccadic turns, presumably to maximize periods of translation (80% of the flight). With a novel adaptive model of the insect visual motion pathway we could show that the motion detector responses to background structures of cluttered environments are largely attenuated as a consequence of motion adaptation, while responses to foreground objects stay constant or even increase. This conclusion even holds under the dynamic flight conditions of insects.
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Affiliation(s)
- Jinglin Li
- Department of Neurobiology and Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany
- * E-mail:
| | - Jens P. Lindemann
- Department of Neurobiology and Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany
| | - Martin Egelhaaf
- Department of Neurobiology and Cluster of Excellence Cognitive Interaction Technology (CITEC), Bielefeld University, Bielefeld, Germany
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