让机器人集群自动生成代码自救，成功率达85%
Online automatic code generation for robot swarms: LLMs and self-organizing hierarchy
- 用自组织神经网络让集群实时生成并执行新代码
- 6台实机测试中，遇困后85%任务可自行恢复
- 适合需要自主应变的群体智能系统研究者
我们提出的自组织神经系统（SoNS）为机器人集群提供了行为设计简便性与全局配置及集体环境估计能力，支持在线自动代码生成。在6台真实机器人演示和超过30台机器人的仿真测试中，当增强SoNS的集群陷入困境时，可即时向外部大语言模型（LLM）请求并运行生成的代码，实现任务完成，成功率高达85%。
原文摘要 · Abstract (English)
Our recently introduced self-organizing nervous system (SoNS) provides robot swarms with 1) ease of behavior design and 2) global estimation of the swarm configuration and its collective environment, facilitating the implementation of online automatic code generation for robot swarms. In a demonstration with 6 real robots and simulation trials with >30 robots, we show that when a SoNS-enhanced robot swarm gets stuck, it can automatically solicit and run code generated by an external LLM on the fly, completing its mission with an 85% success rate.
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