ArticlePatterns (New York, N.Y.)2022
Nature-inspired self-organizing collision avoidance for drone swarm based on reward-modulated spiking neural network.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- SpikeAEC: a neuromodulation-based spiking controller for explore-exploit balancing in mobile robots.Frontiers in neurorobotics · 2026Article
- Generative real-time planning for multi-robot contingencies using deep prior-guided whale optimization.Frontiers in robotics and AI · 2026Article
- Synaptic Synchronization-Based Learning of Pattern Separation in Self-Organizing Probabilistic Spiking Neural Networks.bioRxiv : the preprint server for biology · 2025Article
- Neuromorphic computing for robotic vision: algorithms to hardware advances.Communications engineering · 2025Review
- Article
- Brain-inspired learning rules for spiking neural network-based control: a tutorial.Biomedical engineering letters · 2025Review
- Exploiting noise as a resource for computation and learning in spiking neural networks.Patterns (New York, N.Y.) · 2023Article
- A brain-inspired theory of mind spiking neural network improves multi-agent cooperation and competition.Patterns (New York, N.Y.) · 2023Article
- BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and brain simulation.Patterns (New York, N.Y.) · 2023Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Biological systems can exhibit intelligent swarm behavior through relatively independent individual, local interaction and decentralized decision-making. A major research challenge of self-organized swarm intelligence is the coupling influences between individual behaviors. Existing methods optimize the behavior of multiple individuals simultaneously from a global perspective. However, these methods lack in-depth inspiration from swarm behaviors in nature, so they are short of flexibly adapting to real multi-robot online decision-making tasks. To overcome such limits, this paper proposes a self-organized collision avoidance model for real drones incorporating a bio-inspired reward-modulated spiking neural network (RSNN). The local interaction and autonomous learning of a single individual leads to the emergence of swarm intelligence. We validated the proposed model on swarm collision avoidance tasks (a swarm of unmanned aerial vehicles without central control) in a bounded space, carrying out simulation and real-world experiments. Compared with artificial neural network-based online learning methods, our proposed method exhibits superior performance and better stability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.