受蚂蚁筑墙启发，用强化学习让机器人集群自动分隔任务区域。
Ant-inspired Walling Strategies for Scalable Swarm Separation: Reinforcement Learning Approaches Based on Finite State Machines
- 用有限状态机触发相遇动作，形成稳定隔离墙
- 融合深度Q网络后，混合率降低40%-50%，收敛更快
- 适合需要多任务并行的异构机器人集群
在自然系统中，涌现结构常用于平衡多重需求。例如，军蚁会临时构建‘墙’以防止觅食路径相互干扰。受此行为启发，我们为异构机器人集群设计了两种去中心化控制器，实现并发任务时的空间分离。第一种基于有限状态机（FSM），通过遭遇触发状态转移，形成刚性稳定的隔离墙；第二种将FSM状态与深度Q网络（DQN）结合，动态生成‘非军事区’，实现自适应分离。仿真结果表明，两种控制器均有效减少子群混杂，其中DQN增强型控制器在适应性与分离效果上更优，混合率降低40%-50%，且收敛速度更快。
原文摘要 · Abstract (English)
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.
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