Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics
Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism’s traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Biomimetics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-7673/8/8/580 |
| _version_ | 1827575479139303424 |
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| author | Xuelong Sun Cheng Hu Tian Liu Shigang Yue Jigen Peng Qinbing Fu |
| author_facet | Xuelong Sun Cheng Hu Tian Liu Shigang Yue Jigen Peng Qinbing Fu |
| author_sort | Xuelong Sun |
| collection | DOAJ |
| description | Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism’s traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints. |
| first_indexed | 2024-03-08T20:57:41Z |
| format | Article |
| id | doaj.art-69d8b913f7f3417e8514032536d101e1 |
| institution | Directory Open Access Journal |
| issn | 2313-7673 |
| language | English |
| last_indexed | 2024-03-08T20:57:41Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomimetics |
| spelling | doaj.art-69d8b913f7f3417e8514032536d101e12023-12-22T13:55:32ZengMDPI AGBiomimetics2313-76732023-12-018858010.3390/biomimetics8080580Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary DynamicsXuelong Sun0Cheng Hu1Tian Liu2Shigang Yue3Jigen Peng4Qinbing Fu5Machine Life and Intelligence Research Center, Guangzhou University, Guangzhou 510006, ChinaMachine Life and Intelligence Research Center, Guangzhou University, Guangzhou 510006, ChinaMLTOR Numerical Control Technology Co., Ltd., Zhongshan 528400, ChinaMachine Life and Intelligence Research Center, Guangzhou University, Guangzhou 510006, ChinaMachine Life and Intelligence Research Center, Guangzhou University, Guangzhou 510006, ChinaMachine Life and Intelligence Research Center, Guangzhou University, Guangzhou 510006, ChinaPrey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism’s traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints.https://www.mdpi.com/2313-7673/8/8/580prey-predator interactionagent-based approachswarm roboticsmulti-modal interactionemergent behaviorbio-robotics |
| spellingShingle | Xuelong Sun Cheng Hu Tian Liu Shigang Yue Jigen Peng Qinbing Fu Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics Biomimetics prey-predator interaction agent-based approach swarm robotics multi-modal interaction emergent behavior bio-robotics |
| title | Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics |
| title_full | Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics |
| title_fullStr | Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics |
| title_full_unstemmed | Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics |
| title_short | Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics |
| title_sort | translating virtual prey predator interaction to real world robotic environments enabling multimodal sensing and evolutionary dynamics |
| topic | prey-predator interaction agent-based approach swarm robotics multi-modal interaction emergent behavior bio-robotics |
| url | https://www.mdpi.com/2313-7673/8/8/580 |
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