Finding the gap: neuromorphic motion-vision in dense environments
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-01-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-45063-y |
| _version_ | 1797276446017716224 |
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| author | Thorben Schoepe Ella Janotte Moritz B. Milde Olivier J. N. Bertrand Martin Egelhaaf Elisabetta Chicca |
| author_facet | Thorben Schoepe Ella Janotte Moritz B. Milde Olivier J. N. Bertrand Martin Egelhaaf Elisabetta Chicca |
| author_sort | Thorben Schoepe |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T15:29:18Z |
| format | Article |
| id | doaj.art-ff2f1bc30be04a0e877429a91e1e6ffa |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-07T15:29:18Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-ff2f1bc30be04a0e877429a91e1e6ffa2024-03-05T16:35:13ZengNature PortfolioNature Communications2041-17232024-01-0115111410.1038/s41467-024-45063-yFinding the gap: neuromorphic motion-vision in dense environmentsThorben Schoepe0Ella Janotte1Moritz B. Milde2Olivier J. N. Bertrand3Martin Egelhaaf4Elisabetta Chicca5Peter Grünberg Institut 15, Forschungszentrum JülichEvent Driven Perception for Robotics, Italian Institute of Technology, iCub facilityInternational Centre for Neuromorphic Systems, MARCS Institute, Western Sydney UniversityNeurobiology, Faculty of Biology, Bielefeld UniversityNeurobiology, Faculty of Biology, Bielefeld UniversityFaculty of Technology and Cognitive Interaction Technology Center of Excellence (CITEC), Bielefeld UniversityAbstract 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.https://doi.org/10.1038/s41467-024-45063-y |
| spellingShingle | Thorben Schoepe Ella Janotte Moritz B. Milde Olivier J. N. Bertrand Martin Egelhaaf Elisabetta Chicca Finding the gap: neuromorphic motion-vision in dense environments Nature Communications |
| title | Finding the gap: neuromorphic motion-vision in dense environments |
| title_full | Finding the gap: neuromorphic motion-vision in dense environments |
| title_fullStr | Finding the gap: neuromorphic motion-vision in dense environments |
| title_full_unstemmed | Finding the gap: neuromorphic motion-vision in dense environments |
| title_short | Finding the gap: neuromorphic motion-vision in dense environments |
| title_sort | finding the gap neuromorphic motion vision in dense environments |
| url | https://doi.org/10.1038/s41467-024-45063-y |
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