Ant-inspired Walling Strategies for Scalable Swarm Separation: Reinforcement Learning Approaches Based on Finite State Machines
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908612479156224 |
|---|---|
| author | Kannapiran, Shenbagaraj Oikonomou, Elena Chu, Albert Berman, Spring Pavlic, Theodore P. |
| author_facet | Kannapiran, Shenbagaraj Oikonomou, Elena Chu, Albert Berman, Spring Pavlic, Theodore P. |
| contents | In natural systems, emergent structures often arise to balance competing demands. Army ants, for example, form temporary "walls" that prevent interference between foraging trails. Inspired by this behavior, we developed two decentralized controllers for heterogeneous robotic swarms to maintain spatial separation while executing concurrent tasks. The first is a finite-state machine (FSM)-based controller that uses encounter-triggered transitions to create rigid, stable walls. The second integrates FSM states with a Deep Q-Network (DQN), dynamically optimizing separation through emergent "demilitarized zones." In simulation, both controllers reduce mixing between subgroups, with the DQN-enhanced controller improving adaptability and reducing mixing by 40-50% while achieving faster convergence. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_22524 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ant-inspired Walling Strategies for Scalable Swarm Separation: Reinforcement Learning Approaches Based on Finite State Machines Kannapiran, Shenbagaraj Oikonomou, Elena Chu, Albert Berman, Spring Pavlic, Theodore P. Robotics In natural systems, emergent structures often arise to balance competing demands. Army ants, for example, form temporary "walls" that prevent interference between foraging trails. Inspired by this behavior, we developed two decentralized controllers for heterogeneous robotic swarms to maintain spatial separation while executing concurrent tasks. The first is a finite-state machine (FSM)-based controller that uses encounter-triggered transitions to create rigid, stable walls. The second integrates FSM states with a Deep Q-Network (DQN), dynamically optimizing separation through emergent "demilitarized zones." In simulation, both controllers reduce mixing between subgroups, with the DQN-enhanced controller improving adaptability and reducing mixing by 40-50% while achieving faster convergence. |
| title | Ant-inspired Walling Strategies for Scalable Swarm Separation: Reinforcement Learning Approaches Based on Finite State Machines |
| topic | Robotics |
| url | https://arxiv.org/abs/2510.22524 |