ArticleBiomimetics (Basel, Switzerland)2024
Intelligent Fish-Inspired Foraging of Swarm Robots with Sub-Group Behaviors Based on Neurodynamic Models.
Article in Biomimetics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Novel Black Widow Optimization Algorithm Based on Lagrange Interpolation Operator for ResNet18.Biomimetics (Basel, Switzerland) · 2025Article
- Bio-Inspired Swarm Confrontation Algorithm for Complex Hilly Terrains.Biomimetics (Basel, Switzerland) · 2025Article
- Distributed Broadcast Control of Multi-Agent Systems Using Hierarchical Coordination.Biomimetics (Basel, Switzerland) · 2024Article
- Bio-Inspired Neural Network for Real-Time Evasion of Multi-Robot Systems in Dynamic Environments.Biomimetics (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors.
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Abstract
This paper proposes a novel intelligent approach to swarm robotics, drawing inspiration from the collective foraging behavior exhibited by fish schools. A bio-inspired neural network (BINN) and a self-organizing map (SOM) algorithm are used to enable the swarm to emulate fish-like behaviors such as collision-free navigation and dynamic sub-group formation. The swarm robots are designed to adaptively reconfigure their movements in response to environmental changes, mimicking the flexibility and robustness of fish foraging patterns. The simulation results show that the proposed approach demonstrates improved cooperation, efficiency, and adaptability in various scenarios. The proposed approach shows significant strides in the field of swarm robotics by successfully implementing fish-inspired foraging strategies. The integration of neurodynamic models with swarm intelligence not only enhances the autonomous capabilities of individual robots, but also improves the collective efficiency of the swarm robots.
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